research on metaphor analysis

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• Published: 09 December 2023

Research on metaphor processing during the past five decades: a bibliometric analysis

• Zhibin Peng 1 &
• Omid Khatin-Zadeh 2 , 3  

Humanities and Social Sciences Communications volume  10 , Article number:  928 ( 2023 ) Cite this article

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Metaphor processing has been the subject of extensive research over the past five decades. A systematic review of metaphor processing publications through bibliometric tools can provide a clear overview of research on metaphor processing. In this study, we used the CiteSpace bibliometric tool to conduct a systematic review of publications related to metaphor processing. A total of 3271 works published and indexed in the Web of Science (WoS) were gathered. These works had been published between 1970 and 2022. We analyzed the co-citations of these works by CiteSpace to identify the most influential publications in metaphor processing research. A co-occurrence term analysis was done to identify dominant topics in this area of research. The results of this analysis showed that Language, comprehension, metaphor, figurative language , and context were the most frequent keywords. The most prominent clusters were students, figurative language, right hemisphere, embodied cognition, comprehension, N400 , and anger . Based on the results of this analysis, we suggest that task properties such as response format and linguistic features should be carefully taken into account in future studies on metaphor processing.

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Introduction.

How people understand and produce metaphors has long aroused the interest of scholars from various disciplines such as philosophy, linguistics, and psychology. From the 1970s, scholars began to study the processing of metaphors through experiments. Throughout the past five decades, a large body of experimental and theoretical works on metaphor have been produced, and many journals have started to publish papers related to metaphor. During this period, many researchers in neurolinguistics and psycholinguistics published their works on metaphor. These works have fundamentally changed the ways that researchers have been studying metaphors. This is particularly the case with research on metaphor processing. According to a study conducted by Han et al. ( 2022 ), research on metaphor processing has been the most active area of research on metaphor.

Metaphor processing research is an interdisciplinary area of study on metaphor that involves linguistics, psychology, and neuroscience. A large number of works on metaphor processing have been published in recent years, including reviews directed at selected subtopics. For instance, Rai and Chakraverty ( 2020 ) provided a systematic review of computational models and approaches to metaphor comprehension. This systemic review presented a concise yet representative picture of computational metaphor processing. In a related work, Kertész, Rákosi, and Csatár ( 2012 ) presented a review that was focused on the data, problems, heuristics, and results in cognitive research on metaphor. Some works have presented comprehensive reviews of studies conducted on metaphor comprehension in non-typical populations. For example, Morsanyi et al. ( 2020 ) conducted a systematic review and meta-analysis of metaphor processing in autism. Kalandadze et al. ( 2019 ) also presented a systematic review and meta-analysis of studies on metaphor comprehension in individuals suffering from autism. This review specifically focused on task properties. However, among these works, except for a review paper published by Holyoak and Stamenković ( 2018 ), no other publication has specifically focused on theories and evidence related to metaphor processing. Furthermore, the past review papers have been primarily based on subjective judgment rather than bibliometric tools. Therefore, a systematic review conducted by bibliometric tools can shed new light on our understanding of metaphor processing research. In the literature of the field, we found just two works on bibliometrics of conceptual metaphor research (Han et al., 2022 ; Zhao et al., 2023 ). These two works have presented bibliometric assessments of published works on conceptual metaphor theory. However, they are only about conceptual metaphors in general. To fill this gap in the literature of the field, we used CiteSpace to present a systematic review of studies on metaphor processing.

CiteSpace is a bibliometric analysis tool that can provide an exhaustive account of research in any area over a certain period of time. In this way, it can suggest some directions for future research. Compared to those reviews relying on subjective judgment, a review conducted by CiteSpace can help us navigate through the key documents, research fields, and dominant topics in metaphor processing. Importantly, the results of such analysis can be presented in the form of easily understandable diagrams. We intended to identify the most productive and influential journals, authors, and institutions in the field of metaphor processing. Also, we intended to identify the most influential documents, active research areas, and dominant topics in metaphor processing research. Specifically, by analyzing the co-occurrence of keywords associated with metaphor processing, we aimed to depict a cluster picture of related keywords and dominant topics in this area of research. In this way, we intended to answer the following research questions:

Q1: What are the active research areas and dominant topics in metaphor processing research?

Q2: Is it possible to use a cluster picture of related keywords and research topics to identify research features that play a critical role in studies on metaphor processing?

We hypothesized that a cluster picture of related keywords and research topics in metaphor processing can be used to identify critical research properties that can be taken into account in future studies on metaphor processing.

Methodology

Data collection.

As the study was focused on metaphor processing, we collected and analyzed the published documents by conducting an advanced search in the Web of Science (WoS), Thomson Reuters Core Collection. This search incorporated Social Sciences Citation Index (SSCI), Arts and Humanities Citation Index (A and HCI), Science Citation Index Expanded (SCI-EXPANDED), and Conference Proceedings Citation Index-Social Science & Humanities (CPCI-SSH). We chose WoS as the data source for two reasons. Firstly, WoS has established an independent and comprehensive editing process to ensure the excellent quality of the journals and has formed an unparalleled data structure based on more than 50 years of consistent, accurate, and complete indexing. The indexed journals in the Web of Science Core Collection have been carefully selected. Therefore, the articles indexed in WoS are of high quality. Secondly, WoS is CiteSpace’s primary data source. CiteSpace has been designed to work with WoS data. Datasets from other sources have to be transformed before they can be visualized in CiteSpace.

The following fields were used to retrieve the data:

TS = (metaphor*) AND (process* OR comprehen*)), which means that only articles with both “metaphor” and “process” or comprehen(sion) in the title or abstract, or keywords are retrieved.

Time span=1970–2022

Document Type=article OR review

(“*”is a wildcard in WoS that represents any group of characters, including no character. For example, metaphor*=metaphor, metaphors, and metaphorical, etc. In addition, the review articles in this research do not contain book reviews.)

Totally, 8358 papers were collected from 123 WoS categories, including experimental psychology, neurosciences, business, linguistics, management, music, nursing, and law. In our study, we specifically focused on metaphor-processing research in the fields of linguistics, psychology, and neurosciences. Therefore, we chose the WoS categories related to linguistics, psychology, neurosciences, literature, communication, sociology, philosophy, anthropology, religion, history, and law (i.e. “Linguistics” or “Language Linguistics” or “Psychology Experimental” or “Education Educational Research” or “Neurosciences” or “Psychology Multidisciplinary” or “Psychology Clinical” or “Psychology” or “Psychology Psychoanalysis” or “Psychology Educational” or “Psychology Applied” or “Psychology Social” or “Psychology Developmental” or “literature” or “communication” or “sociology” or “philosophy” or “anthropology” or “religion” or “history” and “law”). After excluding those works that were unrelated to metaphor processing, 3271 publications remained for further analysis.

Descriptive analysis

Before visualization by CiteSpace, we conducted a descriptive analysis of yearly publication trends. Our aim was to identify the most productive journals, authors, and institutions. These descriptive analyses were directly done on the data obtained from the WoS website. The number of works published each year has been given on the WoS website. We used SPSS software to obtain the annual trend of publications (see Fig. 1 ). The numbers of publications for each journal, author, and institution have also been given on the WoS website. We selected the top ten for analysis.

The diagram reveals the publication number for each year and the general trend.

CiteSpace analysis

The descriptive analysis of WoS provides only a basic overview of the research field. It cannot provide an exhaustive account of the research projects over previous decades and directions for future research. Previous reviews without bibliometric tools mainly relied on prior knowledge and subjective judgment. To address this problem, we used CiteSpace to examine the structures of the knowledge of metaphor processing that have been developed over the past years.

In this study, we used CiteSpace, a bibliometric analysis program developed by Chen ( 2004 , 2006 , 2017 ; also see Chen et al., 2010 ; Chen and Song, 2019 ). Bibliometric analysis offers an objective and quantitative method for examining published works in a certain area of research (Mou et al., 2019 , p. 221; Chen, 2020 ). CiteSpace is a Java application for analyzing co-citations and presenting them in the form of visual co-citation networks (Chen, 2004 ). CiteSpace is one of the most well-known bibliometric tools. It offers a variety of analyses, such as keyword analysis and reference analysis, to help academics identify current and upcoming research trends in a field (Mou et al., 2019 ). The bibliographic data files we collected from WoS were in the field-tagged Institute for Scientific Information Export Format. The “full record and cited references” was selected as the content. In this way, CiteSpace could easily identify the files. Once the files were loaded into the CiteSpace, the following procedural operations were performed on them: time slicing, thresholding, modeling, pruning, merging, and mapping (Chen, 2004 ).

In this study, we conducted two separate visualizing analyses of the data. One was a document co-citation analysis, which helped us to identify the important documents in metaphor processing research. A co-cited reference was called a node, and when several nodes were strongly related to one another, they formed a cluster. The other was a keyword co-occurrence analysis. The purpose of this analysis was to identify the most-discussed areas in research on metaphor processing.

Publication years, journals, productive authors, and institutions on metaphor processing

In the Web of Science core collection, the first article about metaphor processing we obtained was published in 1971 by Laurette ( 1971 ). There was no publication on metaphor processing in the years 1972, 1973, and 1974. From 1995 to 2022, more than 50 works were done each year. The maximum number of annual publications belongs to 2021 with 198 published works. Figure 1 presents the annual publications on metaphor processing. Overall, the results show a steady increase in publications on metaphor processing. Therefore, it can clearly be seen that metaphor processing has caught the attention of more and more researchers worldwide.

The 3271 articles or reviews that were examined in this study were published in a number of journals. Table 1 lists the 10 journals that published the highest number of papers in this area of research. With 116 publications on metaphor processing, Metaphor and Symbol , the only SSCI-indexed journal publishing works on metaphor research, was in first place among journals in terms of the number of publications. Frontiers in Psychology and Journal of Pragmatics were in second and third places, with 98 and 71 publications, respectively. The majority of the top 10 journals, as seen in Table 1 , are in the fields of psychology or neuroscience. When considering a submission, metaphor-processing researchers might use Table 1 to select appropriate journals for their papers.

The 10 authors having the highest number of publications in metaphor processing are listed in Table 2 . The author with the most papers published on metaphor processing was Mashal (36), followed by Faust (28) and Gibbs (26).

Table 3 lists the 10 institutions having the highest number of published works in metaphor processing. The University of California is at the top of this list with 131 publications in total, followed by the University of London with 76 articles and Bar Ilan University with 64 articles (Table 3 ).

Document co-citation analysis

A fundamental measure used by academic communities to assess the impact of a publication is the frequency of citations. The value of a published work and its impact on the field is at least partly dependent on the number of works that have been cited. We can identify the important documents in a knowledge domain by analyzing document co-citations. CiteSpace is an efficient tool that can conduct such analysis.

We analyzed document co-citations of 3271 publications collected from the WoS. We used CiteSpace to visualize the 3271 bibliographic recordings from 1970 to 2022. The top 50 papers having the highest number of citations in each 3-year were chosen using a time slice of three years. In order to include all the references cited in those documents regardless of when they were published, we set the Look Back Years (LBY) parameter to −1. Cutting off long-range citation linkages had a positive impact on the clarity of the results; it could increase the clarity of the network structure because long-distance links frequently go hand in hand with a spaghetti-like network. The results are shown in Fig. 2 . The cited publications and co-citation relationships across the entire data set were represented by 1055 distinct nodes and 5928 linkages, respectively. The top 10 articles in the area of metaphor processing research are shown in Table 4 .

The diagram of document co-citations reveals the top 10 most cited articles among the 3271 publications collected from the WoS.

Totally, between 1970 and 2022, 39 documents were cited more than 50 times. The top three most-cited publications in the world’s publications related to metaphor processing are classic books about Conceptual Metaphor Theory (CMT) in general, not about metaphor processing. The work that has received the most citations is “ Metaphors we Live by ” authored by Lakoff and Johnson ( 1980 ). This frequently cited book was a landmark that revolutionized research on metaphor processing. It contends that metaphor is a way of thinking, not just a rhetorical instrument. To put it simply, our conceptual system is fundamentally metaphorical. In contrast to earlier works that looked at metaphor as a purely linguistic figure of speech, this book emphasizes the conceptual nature of metaphor. It defines metaphor as a conceptual process in which a source domain is mapped into a target domain. For example, the conceptual metaphor ARGUMENT IS WAR, in which “argument” is the target and “war” is the source, can be used to explain a statement like “I defended my argument.” Since its introduction in 1980, Conceptual Metaphor Theory (CMT) has gained popularity across various disciplines. The second-most quoted work is also written by Lakoff and Johnson ( 1999 ). This book challenged the Western traditional philosophy by proposing Embodied Philosophy based on the premise that our actions and our languages are based on our bodily experiences. Embodied Philosophy contends that abstract concepts are largely metaphorical. Embodied Philosophy is thus considered as the philosophical basis of Cognitive Linguistics. The third most cited document is a monograph by Gibbs ( 1994 ). Gibbs illustrates that human cognition is inherently poetic and that figurative imagination is central to how we comprehend ourselves and our surroundings. It challenges the traditional understanding of the mind by demonstrating how figurative characteristics of language reflect the poetic structure of the mind. Psychology, linguistics, philosophy, anthropology, and literary theory ideas and research are utilized to demonstrate fundamental ties between the poetic structure of the mind and daily language use. This monograph discusses methods and findings of psycholinguistic and cognitive psychology research to assess current philosophical, linguistic, and literary theories of figurative language. CMT aroused the interest of scholars from different disciplines such as linguistics, cognitive science, neuroscience, and psychology. Scholars in neurolinguistics and psycholinguistics are particularly interested in the cognitive processing of metaphors.

The other publications in Table 4 are not about metaphor in general but about metaphor processing in particular. In an article entitled “An fMRI investigation of the neural correlates underlying the processing of novel metaphoric expressions”, Mashal et al. ( 2007 ) used functional magnetic resonance imaging (fMRI) to investigate the neural networks involved in the processing of related pairs of words that formed literal, novel, and conventional metaphorical expressions. Four different kinds of linguistic expressions were read by the participants, who then determined the relation between the two words (metaphoric, literal, or unrelated). The results showed that the degree of meaning salience of a linguistic expression, rather than literality or nonliterality, modulated the degree of left hemisphere (LH) and right hemisphere (RH) processing of metaphors. This supported the Graded Salience Hypothesis (GSH, Giora, 1997 , 2003 ), which predicts a selective RH involvement in the processing of novel and nonsalient meanings. In this study, the salient interpretations were represented by conventional metaphors and literal expressions, whereas the nonsalient interpretations were represented by novel metaphorical expressions. Right posterior superior temporal sulcus, right inferior frontal gyrus, and left middle frontal gyrus showed considerably stronger activity when the novel metaphors were directly compared to the conventional metaphors. These findings back up the GSH and point to a unique function of the RH in the processing of novel metaphors. Additionally, verbal creativity may be selectively influenced by the right PSTS.

In order to look into the neural substrates underlying the processing of three different sentence types, Stringarisa et al. ( 2007 ) combined a novel cognitive paradigm with event-related functional magnetic resonance imaging (ER-fMRI). Participants were required to read sentences that were either metaphorical, literal, or meaningless before deciding whether or not they made sense. The results of this experiment showed that various types of sentences were processed by various neural mechanisms. Both meaningless and metaphorical sentences activated the left inferior frontal gyrus (LIFG), but not literal sentences. Furthermore, despite the lack of difference between reaction times of literal and metaphoric sentences, the left thalamus is activated only in deriving meaning from metaphoric utterances. The authors attribute this to metaphoric interpretation’s flexibility and ad hoc concept formation. Their findings do not support the idea that the right hemisphere is primarily involved in metaphor comprehension, in contrast to earlier studies.

The two publications mentioned above used new research methods, such as fMRI and ER-fMRI. Additional research methods, such as repetitive transcranial magnetic stimulation (rTMS) by Pobric et al. ( 2008 ) and positron emission tomography (PET) by Bohrn et al. ( 2012 ), were also used in other highly cited papers.

Co-occurring terms analysis

Keywords of any paper present its theme and some kind of summary of the subject that is going to be discussed in it. The occurrence of two keywords in a piece of writing indicates that these words are closely related to one another in the content of the work. The prevailing view is that if two or more terms appear together more frequently, they are more closely related. Betweenness Centrality is one of the functions of CiteSpace that specifies the strength of the relation between two or more terms. This gives us the ability to predict the occurrence of a given term with other terms even in other related topics. If a keyword displays a high Betweenness Centrality value, the keyword may be very significant. In this study, the research areas and dominant topics can be determined utilizing keyword co-occurrence analysis.

We analyzed the keywords to identify the terms and phrases that had co-occurred in at least two separate publications. Highly-frequent terms can show hotspots in a specific field of research (Chen, 2004 ). In this study, we chose the slice length of 3 years, and we set the LBY to 5 years. The results showed language, comprehension, metaphor, figurative language and context were the top 5 key terms having the highest frequencies. The network of related keywords is shown in Fig. 3 , and the terms with a frequency of more than 40 are listed in Table 5 .

The keyword co-occurrence network diagram reveals the most popular keywords of metaphor processing research.

Cluster interpretations

We used CiteSpace to perform a cluster analysis on the basis of keyword co-occurrences. Totally, 528 nodes in the co-citation network with a 3-year time slice were obtained from the analysis. The seven greatest clusters in the research area of metaphor processing are displayed in Fig. 4 . Warmer colors represent more current research subjects, whereas cooler colors represent older research topics in the clusters.

The network diagram of the keyword co-occurrence cluster reveals the most significant clusters of metaphor processing research.

Table 6 shows the top 7 clusters of keywords in metaphor processing research. It is obtained by using index terms as labels for clusters. Also, the clusters were shown by log-likelihood ratio (LLR). The top 7 clusters are named students, figurative language, right hemisphere, embodied cognition, comprehension, N400 , and anger .

Cluster #0, as the largest cluster, is labeled as ‘students’. For native speakers, using and understanding metaphors is simple. However, understanding figurative statements might be challenging for non-native speakers. Littlemore et al. ( 2011 ) found that at a British university, second-language learners had trouble understanding 40% of metaphorical terms that were easily understood by native speakers. Results of another study showed that second-language learners tended to use metaphors incorrectly and in the wrong contexts (Kathpalia and Carmel, 2011 ). It may be challenging for second-language speakers to comprehend and generate metaphors since the metaphorical meaning of a term is developed in the social and cultural context of native speakers. Metaphorical expressions that are easily and automatically understandable for native speakers of a certain language may not be easily interpretable for second-language speakers of that language due to not having enough exposure to that language and culture (Kecskes, 2006 ). Therefore, one of the main concerns for second-language teachers is to enhance second-language learners’ ability in understanding metaphoric language and to use it efficiently in the cultural context of the second language. As a result, there is a lot of discussion in metaphor research about how to improve students’ ability in using metaphors. For instance, Hu et al. ( 2022 ) performed a randomized controlled trial to assess how metaphors affected the symptoms of anxiety in Chinese graduate students.

Cluster #1 is labeled as ‘figurative language’. This cluster shows that two key components of executive functions (working memory and inhibition) could play significant roles in figurative language processing. Since working memory holds information for a short period of time, it plays an active role in discourse comprehension. Therefore, this component of executive functions helps the individual use discourse clues and contextual information in the process of metaphor interpretation. Contextually relevant information and metaphorically relevant information are put together (Wilson and Sperber, 2012 ), enabling the individual to extract the intended metaphorical meaning from an expression. This is done by the active involvement of working memory. Also, the role of inhibition, as another component of executive functions, has been documented in many studies (e.g., Glucksberg et al., 2001 ). These two components can be in close interaction with one another in the process of metaphor comprehension.

Cluster #2 is labeled as ‘right hemisphere’ by LSI test (Chen et al., 2010 ). This cluster shows that functional magnetic resonance imaging has been a common technique in research on the role of the right hemisphere in metaphor processing. Over the past 20 years, researchers in the fields of neurolinguistics and psycholinguistics have intensively studied the role of hemispheres in metaphor processing. Some scholars have hypothesized that the right hemisphere (RH) may have a special role in the processing of metaphorical language. However, many behavioral studies (e.g., Bohrn et al., 2012 ; Faust and Mashal, 2007 ; Mashal et al., 2007 ; Mashal and Faust, 2008 ) have evidence suggesting that the processing of familiar or conventional metaphors requires more left-lateralized processing, compared to the processing of unfamiliar metaphors. Additionally, bilateral processing of traditional metaphors was also supported by the findings of several studies (e.g., Bambini et al., 2011 ; Diaz et al., 2011 ). These results lend credence to the Graded Salience Hypothesis (GSH), according to which semantic salience plays a key role in metaphor processing (Giora, 1997 , 2003 ). According to this hypothesis, conventional, frequent, recognizable, and prototypical meanings are simpler to process than less-prominent meanings. Therefore, the meaning of a conventional metaphor is more salient and more accessible than its literal counterpart. On the other hand, in a novel metaphor, the literal meaning is more evident and the figurative meaning is disclosed later with the support of contextual clues. The GSH claims that unlike novel metaphors, whose meanings are acquired through integration and inferential processes, conventional metaphors’ prominent meanings are stored in the mental lexicon. The GSH also predicts that the left hemisphere (LH) is more active in comprehending conventional and salient metaphorical meanings, while the right hemisphere (RH) is more active in comprehending innovative and non-salient metaphorical meanings (Giora, 2003 ).

Cluster #3 is labeled as ‘embodied cognition’. Theories of embodied cognition challenge the traditional theories of cognition that are based on amodal symbols. These theories offer new perspectives on human cognitive processes. These theories hold that simulation, situated action, and bodily states play a crucial role in cognitive processes. Cognitive linguistics gave rise to some of the first set of theories that supported grounded cognition. Theories of embodied language processing emphasized the role of body, situation, and simulation in language as opposed to the amodal theories of grammar that emerged in the Cognitive Revolution (e.g., Chomsky, 1957 ). The study of embodiment has caught the interest of researchers working in traditional cognitive science, who have started to incorporate the ideas of embodiment in their works. The role of embodiment in language processing was developed and promoted by George Lakoff, Mark Johnson, Mark Turner, and Rafael Núñez based on advancements in the field of cognitive science (Lakoff, 1987 ; Lakoff and Johnson, 1980 ; Lakoff and Johnson, 1999 ; Lakoff and Turner, 1989 ; Lakoff and Núñez, 2000 ). In their studies, they have found evidence suggesting that people draw on their knowledge of everyday physical phenomena to comprehend concepts. According to theories of embodiment and embodied language processing, cognition and cognitive processes are based on the knowledge that comes from the body. There has been an increase in interest in studying the relationship between embodied cognition and language over the last four decades. According to theories of embodied cognition, when people understand words, their sensorimotor systems are engaged in simulating the concepts the words refer to (Jirak et al., 2010 ). Lakoff and Johnson’s ( 1980 , 1999 ) conceptual metaphor theory (CMT) is one of the most prominent theories of embodied cognition. This theory holds that situated and embodied knowledge serves as the metaphorical foundation for abstract concepts. Specifically, Lakoff and Johnson ( 1980 ) argued that abstract concepts are metaphorically understood in terms of concrete concepts with the support of sensorimotor systems. Many studies in various languages have demonstrated how individuals frequently use physical metaphors to discuss abstract concepts. Literature also uses a lot of these metaphors (e.g., Turner, 1996 ). A crucial question is whether these metaphors only reflect linguistic convention or whether they genuinely represent how we think (e.g., Murphy, 1997 ). There is mounting evidence that these metaphors are essential to our thought (e.g., Boroditsky and Ramscar, 2002 ; Gibbs, 2006 ).

Cluster #4 is labeled as ‘comprehension’. One of the keywords in this cluster is the term context . This supports the key role of context in the process of metaphor comprehension. Context of a conversation can provide some information that contributes to metaphor processing (e.g., Steen et al., 2010 ). It helps the individual disregard non-relevant literal meanings and keeps the metaphorically relevant information to derive the intended metaphorical meaning.

Cluster #5 is labeled as ‘N400’. The N400 is a part of time-locked EEG signals called event-related potentials (ERP). It is a negative-going deflection that normally peaks over centro-parietal electrode sites and occurs 400 ms after the stimulus begins, though it can also last between 250 and 500 ms. The N400 is a typical brain response to words and other meaningful (or potentially meaningful) stimuli, such as visual and auditory words, sign language signs, images, faces, environmental sounds, and odors (Kutas and Federmeier, 2000 , 2011 ). During the past 4 decades, ERP has been one of the techniques most frequently employed in cognitive neuroscience research to examine the physiological correlates of sensory, perceptual, and cognitive activities associated with information processing (Handy, 2005 ). ERP is also widely employed in metaphor processing studies, along with other imaging techniques such as fMRI, PET, and MEG.

Cluster #6 is labeled as ‘anger’. This cluster includes the key terms figurative language and eye tracking . This suggests that the metaphoric conceptualization of some emotional states and emotional terms such as anger can be reflected in eye movements. Interestingly, some works have suggested that this can happen not only for emotion-related concepts but also for other categories of abstract concepts that are metaphorically described in terms of movement (e.g., Singh and Mishra, 2010 ).

This clustering of keywords offers an organized and clear picture of key concepts that have been involved in various lines of research on metaphor processing. This clustering shows which lines of investigation have had a strong relationship with one another in research on metaphor processing. Therefore, the suggested clustering of keywords in metaphor comprehension offers a map for research on metaphor processing. This can be a guiding tool for researchers to have a clearer idea and organized map of how various lines of research on metaphor processing intersect with one another.

Discussion and implication for future studies

Over the past 50 years, metaphor processing has been a widely discussed topic among scholars in various disciplines, particularly researchers in neurolinguistics and psycholinguistics. Through the aforementioned document co-citation analysis, co-occurring word analysis, and cluster visualization which were done by CiteSpace, this study showed that research on metaphor processing has mainly focused on hemispheric processing of metaphors, metaphor comprehension, the embodied cognition basis of metaphor processing, behavioral-experiments study, ERP method and other techniques (fMRI, PET, and MEG, etc.), and the comparision of metaphor processing of adults with children.

Results of this study showed that research projects on metaphor processing are mainly conducted by experiments, including behavioral experiments, ERPs, and other imaging techniques such as fMRI, PET, and MEG. However, there is some conflicting evidence in the research findings. For instance, many studies have shown no statistically significant difference between ASD and TD groups in the understanding of metaphors and figurative language (Hermann et al., 2013 ; Kasirer and Mashal, 2014 ; Mashal and Kasirer, 2011 ; Norbury, 2005 ). These results suggest that factors other than disease-specific traits may account for the differences in results between studies. In the past, it has been discovered that group matching strategy and general language proficiency can account for part of the between-study variability in figurative language comprehension (Kalandadze, et al., 2018 ). However, further pertinent variables need to be examined in order to fully explain the observed variabilities. One reason for these different results may be due to the different theories the researchers adhere to. Another reason for mixed results may be due to the task properties of those experiments.

As for the theories on metaphor processing, there are two models that are widely used to study the processing of metaphors, namely, the Direct Access View (Gibbs, 1984 , 1994 ; Gibbs and Gerrig, 1989 ) and the Graded Salience Hypothesis (Giora, 1997 , 2003 ). According to the Direct Access View, in metaphor processing, the non-literal meaning of the metaphor can be directly processed, without inferring and discarding the literal meaning in an initial stage. Based on the Direct Access View, the Parallel Hypothesis was proposed, which holds that understanding figurative language is not different from that of literal language. Therefore, it is not necessary to assume any special cognitive mechanisms to process figurative language such as metaphors (Glucksberg et al., 1982 ). However, the Parallel Hypothesis can only hold if the literal and figurative meanings are fully understood. When the literal and figurative meanings are inconsistent, the coexistence of literal and figurative meanings cannot be explained by the Parallel Hypothesis. This does not mean that the literal meaning is abandoned before it is processed. Rather, the context facilitates the understanding of the inconsistent literal meanings. Therefore, the Direct Access View also supports the Context-dependent Hypothesis, which holds that we have a direct understanding of the figurative meanings with the help of sufficient contextual information.

Another theory on metaphor processing that is widely used to support metaphor research findings is the GSH (Giora, 1997 , 2003 ). As mentioned, the GSH holds that metaphor processing is influenced by the degree of semantic prominence. That is, conventional, frequent, recognizable, and prototypical meanings are easier to assimilate than less-salient meanings. One prediction of GSH is that the right hemisphere (RH) is more active in perceiving creative and non-salient metaphorical meanings, while the left hemisphere (LH) is more active in comprehending conventional and salient metaphorical meanings (Giora, 2003 ).

While the Direct Access View holds that metaphorical meaning is directly accessible, the Graded Salience Hypothesis assumes that metaphorical meaning is activated after the activation of the salient literal meaning. Depending on which theoretical framework is taken for certain research, different and conflicting results may be obtained. However, it should be noted that metaphor processing is a complex phenomenon and a large number of factors may be involved in it. Therefore, a single theory may not be able to describe all aspects of metaphor processing for all types of metaphors. The Direct Access View can describe the processing of highly conventional metaphors and idiomatic expressions. In daily conversations, people can easily produce and understand conventional metaphors and idiomatic expressions automatically. But, in some cases, this theory fails to describe the processing of novel metaphors. On the other hand, the Graded Salience Hypothesis may provide a better picture of how novel metaphors are processed. Therefore, in order to explain the discrepancies in research findings, we may need to take broader frameworks. When a single theoretical framework cannot explain discrepancies, two complementary frameworks can be taken and combined to explain and reconcile the conflicting results. Furthermore, a given theoretical framework may be more applicable to certain groups of people. For example, the GSH may be more applicable to ASD than the AD group, while the Direct Access View may be more applicable to TD than the ASD group. In other words, types of metaphors (e.g., conventional vs. novel metaphor), features of comprehenders (ASD vs. TD group), and possibly many other factors determine which theory of metaphor processing is most applicable. Putting various theoretical frameworks together and trying to make broader theoretical frameworks is a potential solution for responding to some questions that have not been answered yet.

Another reason for the differences in results between studies on metaphor processing may be the task properties of those experiments. There is a consensus in the literature of behavioral and neuroimaging studies that factors such as clinical populations, task characteristics, response format (i.e., multiple-choice vs. verbal explanation task), and lack of linguistic context can affect participants’ capacity to interpret metaphors (Pouscoulous, 2011 , 2014 , Rossetti et al., 2018 ). For instance, when assessed with an act-out rather than a verbal explanation task, children with TD demonstrate earlier proficiency in metaphor understanding. This may be because verbal and other types of tasks place different demands on a child’s linguistic and cognitive abilities (Pouscoulous, 2011 ). A similar explanation for how people with ASD perform metaphor tasks is based on response format. For instance, people with ASD may grasp metaphors similarly to people with TD, but they may have more trouble conveying them orally because of problems with expressive language (Kwok et al., 2015 ). Other aspects of the metaphors may also play a role, such as the amount and type of contextual information that is available to interpret the expression, or the degree of familiarity with the expression (Pouscoulous, 2011 , 2014 ). By combining the preceding studies utilizing the techniques of systematic review and a meta-analysis, Kalandadze et al. ( 2019 ) collated the knowledge that is currently available concerning task properties. Their aim was to find out how task properties affect metaphor comprehension ability in people with ASD compared to people with TD. They discovered that previous studies had used various kinds of materials and tasks that were either created by the researchers who designed the studies or were adapted from earlier research. The possible impact of the task properties was rarely taken into account in the previous studies, despite the fact that the task properties varied widely. Degree of individual’s familiarity with the metaphor (conventionality/novelty), degree of complexity of syntactic structure, linguistic and non-linguistic context (physical context) of the metaphoric expression, modality of stimulus (e.g., audio, visual), response format (verbal or non-verbal), and timing of the task are important task properties that can affect results of studies and their interpretations. Therefore, in order to obtain more accurate results, these factors need to be taken into account.

Implication for future studies

Based on the discussion in the section “Discussion”, we suggest that two issues deserve more consideration in future studies on metaphor processing. The first one is the theories that are employed to support the findings of metaphor processing studies. As different theories on metaphor processing may generate different conclusions, it is suggested that researchers discuss the results from different theoretical perspectives, rather than a single theory.

The second issue that merits more consideration is task properties. Task properties are important but have been neglected. The existing research on metaphor processing has paid little attention to the relevance of task properties in performance on metaphor comprehension tasks. Therefore, we contend that task properties including response format and linguistic features (i.e., metaphor familiarity, the syntactic structure of the metaphor, linguistic context, and stimulus modality) should be carefully considered in future investigations on metaphor processing. The systematic review and meta-analysis by Kalandadze et al. ( 2019 ) revealed that some task properties, including metaphor familiarity, are more frequently taken into account than others when determining the impact of a task. The least studied property in previous research is syntactic structure. Also, research on metaphor processing has not done a good job to examine the influence of contextual information on different groups of people. In future metaphor processing studies, these task properties merit additional consideration. When creating and reporting task properties in metaphor studies, researchers need to be extremely careful.

It should be noted that metaphor processing is a complex and multidimensional process. Therefore, in order to obtain a clear picture of various aspects of metaphor processing, researchers of various fields need to collaborate in interdisciplinary research projects. Neuroimaging data collected by neurolinguistics experts, behavioral data collected by researchers in psycholinguistics and cognitive science, and even corpus-based data can be combined to offer a broader picture of metaphor processing. Various types of evidence can complement each other and fill the gaps. This is a crucial point that should be considered in future research on metaphor processing.

As noted by Han et al. ( 2022 ), metaphor processing has been the most studied research area in metaphor research. Since the 1970s, how metaphors are processed in the brain has been extensively investigated by scholars in linguistics, neurolinguistics, and psycholinguistics. However, up to now, bibliometric tools like CiteSpace have not been used to systematically review literature on metaphor processing. In our study, a total of 3271 bibliometric recordings were collected from the Web of Science Core Collection. These documents had been published between 1970 and 2022. The descriptive analysis revealed a yearly increase in the number of publications, indicating that metaphor processing has caught the interest of academics from a variety of disciplines. Metaphor and Symbol , the sole SSCI-indexed journal devoted to metaphor research, took the first position among journals in terms of publishing yield with 116 publications on metaphor processing. Mashal, Faust, and Gibbs are the three most prolific authors in terms of publications on metaphor processing.

These bibliometric analyses through the CiteSpace software showed that language, comprehension, metaphor, figurative language , and context were the five most frequent keywords. Also, the most prominent clusters were students, figurative language, right hemisphere, embodied cognition, comprehension, N400 , and anger . These findings showed that research on metaphor processing has largely focused on the hemispheric processing of metaphors, metaphor comprehension, and embodiment in metaphor processing. Behavioral experiments, ERP and other techniques, such as fMRI, PET, and MEG were the common techniques in metaphor processing research. The current review through CiteSpace indicates that putting various theoretical frameworks together and trying to make broader theoretical frameworks is a potential solution for responding to some questions that have not been answered yet. This review also suggests that in future studies on metaphor processing, task properties such as response format and linguistic features should be carefully taken into account.

Although the current study aimed to be comprehensive within its defined scope, it was subject to some inevitable limitations. Firstly, being limited to WoS documents was one of the limitations of this study. Other databases such as Scopus, Google Scholar, Index Medicus, and Microsoft Academic Search were not included in this study. Secondly, publishers’ labeling of document types was not always correct. Some articles presented as reviews by WoS, for example, were not review papers at all (Yeung, 2021 ). Thirdly, we used only one scientometric instrument. Fourthly, while several prospective papers have recently been published, these studies were not acknowledged. Furthermore, because of obliteration, the citation count for some earlier published works was low.

Nonetheless, this study comprises a ground-breaking bibliometric assessment of global research on metaphor processing and provides a clear overview of global publications related to metaphor processing. Hence, it can be a helpful source for researchers interested in metaphor and metaphor processing. The results of this review have both theoretical and practical implications for the study of metaphor processing and metaphor in general.

Data availability

All data analyzed during this study can be accessed at https://doi.org/10.7910/DVN/JFRP5W .

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The authors extend their appreciation to the Humanities and Social Science Research Projects of the Chinese Ministry of Education [Grant Number: 19YJA740044].

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Peng, Z., Khatin-Zadeh, O. Research on metaphor processing during the past five decades: a bibliometric analysis. Humanit Soc Sci Commun 10 , 928 (2023). https://doi.org/10.1057/s41599-023-02465-5

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Metaphor research as a research strategy in social sciences and humanities

• Published: 11 March 2023
• Volume 58 , pages 227–248, ( 2024 )

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• Sepehr Ghazinoory   ORCID: orcid.org/0000-0002-6761-4694 1 &
• Parvaneh Aghaei   ORCID: orcid.org/0000-0001-5185-3589 1  

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Metaphors have so far inspired many researchers to explain complex concepts or new theorizing. But there is no clear instruction for metaphor-based research and its validation principles. Here, we first locate metaphor research in social sciences and humanities (SSH) and classify different types of its use. Then we describe the basics of metaphor including its concept, types, components and characteristics. Since the methodology is of special importance in SSH, we introduce the metaphor research strategy in the research onion. Then, inspired by the stages of creative thinking, we present a specific process to carry out this strategy. The stages of proposed process include "expression of rationale and research question", "identification of possible metaphors and selection of preferable one", "evidence collection and cross-domain mapping", and "fitting test". We also propose a fitting test consisting of seven essential principles, which ensure that the final design of the metaphor is appropriate and works properly. The results of this research can formalize and validate the metaphor research as an efficient strategy for theorizing, futurology and describing complex concepts.

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Current Approaches to Metaphor Analysis in Discourse

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• Section 1 Introducing Metaphor Analysis 1 What is Metaphor and Why Does it Matter? Lynne Cameron 2 A Selective Survey of Research Practice in Published Studies Using Metaphor Analysis Zazie Todd, University of Leeds, and Graham Low, University of York Section 2 Contemporary Theories of Metaphor 3 The Cognitive View of Metaphor: Conceptual Metaphor Theory Alice Deignan, University of Leeds 4 Between Mind and Language: A journey Worth Taking David Ritchie, Portland State University 5 The Discourse Dynamics Framework for Metaphor Lynne Cameron Section 3 Metaphor Analysis 6 Identifying Metaphors in Discourse Data Lynne Cameron and Robert Maslen 7 Finding Systematicity in Metaphor Use Lynne Cameron, Robert Maslen and Graham Low 8 Metaphors and Discourse Activity Lynne Cameron 9 Corpus Techniques for Metaphor Analysis Alice Deignan and Elena Semino, Lancaster University 10 Working with Large Amounts of Metaphor Data Robert Maslen 11 Multimodal Metaphor Analysis Alan Cienki, Vrije Universiteit Section 4 Bringing it all Together 12 Guidelines for Good Practice in Meaphor Analysis Graham Low and Zazie Todd 13 Metaphor Analysis in a PhD Study: Analysing Online Discussions
• Learning by Word of Mouse? Sue Rivers, Coventry University 14 Metaphor Analysis in a Social Sciences Project: Using Metaphor Analysis to Compare Expert and Public Perceptions of the Risk of Terrorism Lynne Cameron and Robert Maslen Appendix.
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ORIGINAL RESEARCH article

Research trends in multimodal metaphor: a bibliometric analysis.

• School of Foreign Languages and Cultures, Guangdong University of Finance, Guangzhou, China

The concept of multimodal metaphor has generated a growing body of literature over the past decades. However, a systemic review of the domain seems to be lacking in relevant literature. This study, therefore, is an attempt to conduct a bibliometric analysis of the field of multimodal metaphor during 1977–2022, with a focus on 397 relevant publications retrieved from the Web of Science Core Collection (WoSCC) with the visualization tool VOSviewer. Some major quantitative findings are: (i) the number of publications in multimodal research began to surge in 2010 upon the seminal work of Forceville’s (2009); (ii) USA, China and Spain are the most productive countries; (iii) journals in the field of advertising, communication and linguistics are important sources of publications; and (iv) eleven clusters of keywords are identified, such as “visual metaphor”, “persuasion”, “pictures”, “impact”, “multimodal metaphor”, “model”, etc., representing crucial areas of interests. We also identified, by qualitative observations, three research trends in multimodal metaphor, driven by cognitive linguistic theory, the theory of pragmatics and visual/multimodal rhetoric theory, respectively. Various theoretical perspectives may shed light on possible further research on multimodal metaphor.

1. Introduction

The concept of multimodal/visual/pictorial metaphor has generated a growing body of literature over the past decades since the seminal works of Forceville (1996) . Multimodal metaphor, according to Forceville and Urios-Aparisi (2009 : 4), is a phenomenon that the target and source are each represented “exclusively or predominately” in different modes, while the visual/pictorial metaphor is considered a phenomenon where the target and source are represented predominately in one mode. However, Eggertsson and Forceville (2009 : 430) argued the definition of multimodal metaphor was a “pure” or “strict” metaphor and was “distinguished for analytical purposes only.” They further explained that since “the majority of multimodal metaphors in moving images cue target and/or source in more than one mode simultaneously,” they could be labeled as multimodal metaphors in the broad sense. The definition of multimodal metaphor thus in a broad sense presents its potential to be an umbrella term embracing visual or pictorial metaphors. For the convenience of discussion, we use multimodal metaphor as a general term to name such type of phenomenon in this review.

Multimodal metaphor could appear in various multimodal discourses, such as picture books, posts, magazines, TV shows, films, etc., which comprises different modes (written language, images, sound, gestures, etc.). The interpretation of multimodal metaphors is highly related to traditional metaphor studies. It could be classified into three major dimensions. One is studied within the framework of rhetoric. In rhetoric, the metaphor was used for persuasion or decoration. The representative scholar is Barthes (1977) , who first applies the theory to image studies. Another perspective is offered by pragmatics, which considers metaphor as creativity ( Sperber and Wilson, 1995 ) and should be understood with respect to context. Two relevant theories are developed under the perspective: the interaction theory and the blending theory. The interaction theory is developed by Indurkhya (1992) . It focuses on “interaction” and tries to invent a relation between the source and one of its activated features. Metaphor interpreted in this approach is often coined as a creative metaphor. The blending theory proposed by Fauconnier and Turner (2002) presupposes that different input spaces merge to create a new “blended space”. It combines selected elements from the input spaces, and as a result yields new, emergent meaning that is not present in either of the input spaces. The third dimension proceeds from a cognitive linguistic perspective. It is inspired by Lakoff and Johnson’s (1980) monograph Metaphor we live by . A metaphor is not only a rhetorical device but also a way of thinking and acting, whereas language is just an external manifestation of metaphor ( Lakoff and Johnson, 1980 ). Multimodal metaphor within this theory, according to Forceville (1996) , is a strictly directional phenomenon, positing a relationship between pairs of mental representations. It is concerned with entrenched conceptual relationships and how they may be elaborated.

In Forceville’s (2006) review, a number of issues concerning multimodal metaphor research are pointed out. Those issues include, such as the nature of multimodal metaphor, the difference between structural and creative metaphor, how important genre is for the construal and interpretation of metaphor, etc., ( Forceville, 2006 : 379). To address those issues, more and more scholars have come to extend the multimodal metaphor research to discourses of various genres such as advertising, political cartoons, comics, animation, TV news, films, etc. Those works have not only enriched multimodal metaphor studies but also improved theoretical models due to observations on data of greater varieties.

Despite the significant academic advancement made in multimodal metaphor research by far, the existing pile of literature has received scanty attention of systemic review. An investigation is thus necessary to take stock of the current state of the studies over the past decades. Therefore, this paper conducts a bibliometric analysis, using information visualization methods to make quantitative analysis and observe the indicators of authors, journals, countries, institutions, references and keywords of worldwide literature in a certain field. In this way, we can consolidate the understanding of the nature of multimodal metaphor and propose implications and research directions for future work to promote multimodal metaphor research. Our analysis is guided by the following research questions:

Q1: Who are the most influential authors on the subject of multimodal metaphor?

Q2: What countries/regions and journals are the most influential in the research field of multimodal metaphor?

Q3: What are the most important sub-fields of multimodal metaphor studies?

Q4: What are the research trends and possible future directions in the field of multimodal metaphor?

2. Data and methodology

We retrieved the data in our study on Oct 28, 2022 from the Web of Science (WoS) Core Collection Database in all editions excluding Conference Proceedings Citation Index - Science (CPCI-S), Current Chemical Reactions (CCR-EXPANDED), and Index Chemicus (IC). We searched “Topic” with the keywords “multimodal metaphor,” “pictorial metaphor” and “visual metaphor.” The procedure above generated a search result of 397 articles. Then we exported full record and cited references of the 397 studies and imported it to VOSviewer (version 1.6.18) for further analysis. The basic information of all documents including publication year, author, and country is also exported to an EXCEL file for analysis of possible patterns. We have adopted a minimal intervention approach in the data retrieval process to ensure that the quantitative results generated in our research presents least bias.

Following Brika et al. (2022) , we have gone through seven steps in the whole study process: study design, research questions, selected types of analysis (co-authorship, co-occurence, citation, bibliographic coupling, and co-citation), data compilation, exportation of basic document information including publication years, author, country/region to EXCEL, visualization (to both network maps in VOSviewer and curve/bar chart in EXCEL) and discussion. We have opted for a relatively low threshold and corresponding weight in visualization in VOSviewer for each type of analysis to present a thorough view of link strength in every network, as shown in Table 1 . In the final step, the three authors discussed and illuminated on the quantitative findings for interpretation.

Table 1. Threshold and visualization for each type of analysis in VOSviewer networks.

3. Quantitative results

3.1. publication features, 3.1.1. publication years.

As is shown in Figure 1 , the multimodal metaphor research exhibits an overall increasing trend over the decades. Several features are noteworthy here. First, the period from 1977 to 2010 witnessed relatively small amounts of literature in this field with a peak at 7 studies in 1998. Second, the number of papers in the domain of multimodal metaphor began to surge in 2010, and reached a record high at 48 in 2020, since Forceville (2009) brought the term “multimodal metaphor” into the spotlight and began to draw increasing scholarly interests in the field.

Figure 1. Trends of annual publication of research in multimodal metaphor.

3.1.2. Country/region participation

A total of 53 countries or regions were involved in research related to multimodal metaphor across the whole world. As shown in Figure 2 , there are only 11 countries with more than 10 publications. The most productive countries or regions are USA (80), People’s Republic of China (41), Spain (41), England (34), and Netherlands (23). It is interesting to note that Canada (19), Germany (18), Australia (16), Italy (16), and France (15) share very similar total counts.

Figure 2. Countries or regions that participated in research related to multimodal metaphor.

Figure 3 reveals the result from citation analysis in terms of participating countries or regions. The top four countries are USA with a total number of documents of 75, People’s Republic of China with 73, Spain with 41 and England with 34. However, the number of links in England is 20, exceeding that in People’s Republic of China (18) and that in Spain (15). In other words, England is the second most influential country in the number of citation links, while People’s Republic of China and Spain are the third and the fourth. All the countries or regions involved present a certain degree of collaboration with others, albeit with a few of them including Slovenia, Norway, Chile, Lithuania, Saudi Arab and South Africa the least collaborative with only 1 link separately.

Figure 3. Countries or regions that show collaboration by citation in multimodal metaphor research (min number of citations of a country/region = 1; visualization by weight of documents).

3.1.3. Journal participation: citation analysis; co-citation analysis

Figure 4 reveals the most important sources of publication in terms of co-citation analysis. According to the assigned total link strength, the most influential journals for multimodal metaphor research are (as shown in Table 2 ): Année Psychologique (12), Journal of Advertising (44), Metaphor and Symbolic Activity/Metaphor and Symbol (59) (in separate clusters), Journal of Pragmatics (66), Sage Open (7), Accounting Education (9), Monographs of the Society for Research in Child Development/Social Semitoics (6), Multimodal Communication in the 21st century: Professional and Academic (9), Frontiers in Psychology (13), Food Research International (7), Review of Cognitive Linguistics (28), Metaphor and the Social World (13), Visual Communication (44), Semiotica (20), Tydskrif vir Geesteswetenskappe (2) and Discourse and Communication (4). Among all the sources, Journal of Advertising and International Journal of Advertising are clearly the most influential, with a link strength of nine. This is followed by the linkage between Journal of Pragmatics and Visual Communication with a strength of eight. It is worth noticing that the connection between Journal of Pragmatics and Metaphor and Symbol is also strong with a link strength of four. However, when it comes to the number of total citations, Journal of Advertising is the most influential with 358 publications, while Journal of Pragmatics, the second, has 301. The fact that the two journals, Journal of Advertising and Journal of Pragmatics, have such considerable impact shows how the research of multimodal metaphor becomes recognized in the academia of related disciplines.

Figure 4. Citation analysis by sources with all sources in account (min number of citations of a source = 1; visualization by weight of documents).

Table 2. Journal(s), citation and link strength in the 17 clusters from citation analysis by sources.

In addition, it is clear from the network map in Figure 5 that a significant number of these sources are co-cited. As shown in Table 3 , five sources seem to hold a dominate position over the others: Applied Cognitive Linguistics (263 citations and 8,665 total link strength), Journal of Advertising (217 citations and 8,600 total link strength), Journal of Consumer Research (215 citations and 7,972 total link strength), Journal of Pragmatics (192 citations and 7,369 total link strength) and Metaphors We Live By (1980) (115 citations and 3,370 total link strength). It can be seen that although Lakoff and Johnson (1980) set out by defining conceptual metaphor and implicated its potential in multimodal meaning making, it is other sources that serve to extend the application of CMT theory in the multimodal fields.

Figure 5. Co-citation Analysis of Cited Sources (min number of citations of a source = 3; visualization by weight of citations).

Table 3. Top five sources according to link strength from co-citation analysis by sources.

3.1.4. Participating authors: co-authorship of authors, co-citation of authors

Figure 6 illustrates the partnership network between all the authors as the co-authorship analysis is considered with a minimum of two citations of an author, showing the most influential authors. As is shown, the co-authorship network presents seven prominent authors divided into two clusters. The leading authors in Cluster #1 are Gerhard Schmalz and Dirk Ziebolz, each with three documents and total link strength of 12. In Cluster #2, Tom Sensky is slightly more influential than the other two authors, as he features three documents and total link strength of 10.

Figure 6. Co-authorship of authors (min number of citations of an author = 2; visualization by weight of documents).

The citation pattern of the other authors is shown in Figure 7 with a minimum of two citations per author. These authors are grouped into four clusters. Charles Forceville is clearly the most influential author in Figure 7 , with the total link strength reaching 76 and the total number of citations standing at 361. It is found that Charles Forceville is the most important author in two clusters generated by VOSviewer, whereas in the remaining two, Amitash Ojha and Peter Kravanja contribute the most with the total link strength of 35 and 22, and the total number of citations, 26 and 11.

Figure 7. Network of cited authors (min number of citations of an author = 2; visualization by weight of documents).

3.1.5. Citation: most cited reference, bibliographic coupling

The citation analysis of documents as revealed in Figure 8 shows there are 15 clusters with a minimum of one citation accounted. As is presented in Table 4 , the leading references in each of these clusters are: Kogan et al. (1980) , Johns (1984) , Forceville (2002) , Teng and Sun (2002) , Tsakona (2009) , van Mulken et al. (2010) , Bounegru and Forceville (2011) , Delbaere et al. (2011) , Hidalgo Downing and Kraljevic Mujic (2011) , Ortiz (2011) , Yu (2011) , Feng and O’Halloran (2013) , Indurkhya and Ojha (2013) , Danado and Paternò (2014) , Hart (2017) .

Figure 8. VOSviewer network map showing the most cited reference (min number of citations of a document = 1; visualization by weight of citations).

Table 4. The representative document in the 15 clusters according to citation analysis.

Bibliographic coupling shows the extent to which the documents share the same citations. The network map in Figure 9 shows a total of nine clusters with a minimum of nine citations in one document, as the lowest number of citations among the leading documents listed above is nine. In each of the nine clusters, Forceville (2002) in Cluster #1, van Mulken et al. (2014) in Cluster #2, Hlawatsch et al. (2011) in Cluster #3, van Mulken et al. (2010) in Cluster #4, Wise (1999) in Cluster #5, Tsakona (2009) in Cluster #6, Stark (2011) in Cluster #7, Ng and Koller (2013) in Cluster #8 and Lee (2007) in Cluster #9.

Figure 9. VOSviewer network map showing the bibliographic coupling of documents (min number of citations of a document = 9; visualization by weight of total link strength).

3.2. Research domain of co-occurrence: key word plus

The purpose of co-occurrence keyword analysis is to look into the relationship between keywords in a set of publications to uncover the topical issues and help scholars better grasp current research concerns. A total of 549 keywords were investigated, 137 of which appeared more than two times. Figure 10 shows the visual network map of keyword co-occurrence. There are in total 137 items, 11 clusters. The total link strength is 803. The different colored nodes represent different domains of interests in multimodal metaphor. The size of a node implies the occurrence of keywords. The closeness of the relationship between any two items is shown by the thickness of the connection lines.

Figure 10. VOSviewer network map showing the co-occurrence of key word plus (mini occurrences of a key word = 2; visualization by weight of occurrences).

According to the results of cluster analysis, 11 key areas of research are found: Cluster #1 is related to the visual metaphor comprehension ability of the individuals with intellectual disability. For example, Shnitzer-Meirovich et al. (2018) conduct a program to enhance their analogical thinking and find they can recruit the ability required for visual metaphor comprehension.

Cluster #2 concerns the visualization of the attitudes and the framework for multimodal metaphor comprehension. One of the representative works is Forceville’s (2009) model of multimodal metaphor within a cognitive framework which becomes the mainstream in this field.

Cluster #3 is about the information design and the exploration of the images. For instance, Ojha and Indurkhya (2020) apply perception theory to analyze the design of visual metaphors.

Cluster #4 reveals a perspective from discourse approach to the metaphor representing various emotions. Feng and O’Halloran (2013) , integrating social semiotic theory and cognitive linguistic theory, proposes a multimodal discourse approach to explore the structural features of the visual metaphor mapping various emotions.

Cluster #5 concerns the appreciation of visual metaphor in people with old age-related diseases. One of the representative works explores those people’s humor preferences, which finds that they enjoy simple and familiar ones ( Kmita et al., 2022 ).

Cluster #6 is related to the contribution of metonymy to multimodal metaphor or visual metaphor. Those studies demonstrate the unignorable function of metonymy in understanding multimodal metaphor and propose the types of the interaction of multimodal metaphor and metonymy (e.g., Forceville and Urios-Aparisi, 2009 ).

Cluster #7 focuses on the impact of the persuasion of visual metaphors. Such studies prove that the persuasive effect could be more positive if the message designed in the form of visual metaphor (e.g., Meijers et al., 2019 ).

Cluster #8 is about space-time metaphor system. In this cluster, the issue is related to use visual metaphor method to understand space-time accessibility. For example, Jiang et al. (2022) use visual metaphor to understand the space-time accessibility of the Hong Kong-Zhuhai-Macao Belt.

Cluster #9 reveals a relation to the contribution of visual metaphor. For example, one of the studies show that the advertisement metaphorically representing the product with personification, appears to lead to more positive brand liking ( Marjorie et al., 2011 ).

Cluster #10 is about the function of visual metaphor in representing the evolution of the eubacteria. Those studies demonstrate the appropriateness of visual metaphor to depict the formation of phenotypic variants of bacterial cells ( Sánchez-Romero and Casadesús, 2021 ).

Cluster #11 concerns the understanding of the art of the images. In this cluster, a representative work is by Poppi et al. (2020) , who point out the current interpretations of the metaphorical structures do not always work within the domain of art cognition and thus propose a participant-based framework.

According to the clustering topics, we find that the main issues of multimodal metaphor studies concern what framework to use, what model to interpret, and how to interpret metaphors in different genres, such as advertisements, films, etc. The co-occurrence of the keyword analysis also shows that the effect of multimodal metaphor is one of the hot topics. Those hot topics can be concluded as six top keywords. They are “visual metaphor” with 24 occurrences. “pictures” with 15 occurrences, “persuasion” with 14 occurrences, “model” with 12 occurrences, as well as “impact” and “multimodal metaphor” with 11 occurrences, respectively.

The result implies the current research trend of multimodal metaphor is still in trying to develop an applicable model and in understanding their impacts.

4. Discussion

The co-occurrence of the keyword analysis displays that the hot topics related to multimodal metaphor studies could be classified into two research aims. One is related to the framework or the models for the exploration of visual or multimodal metaphor, the other is related to the impact or the power of the multimodal metaphor. The three major approaches driven by the interaction, the conceptual and the blending theories have been used widely and further developed by scholars to study different types of multimodal discourse. The major concern is on the impact of visual metaphor ( van Mulken et al., 2010 , 33), such as the persuasion of advertisements, cartoons or political discourse ( Teng and Sun, 2002 ; Tsakona, 2009 ; Hidalgo Downing and Kraljevic Mujic, 2011 ; Indurkhya and Ojha, 2013 ). The following sections discuss the research trend and possible future directions in multimodal metaphor studies.

4.1. Research direction driven by cognitive linguistic theory

The conceptual multimodal metaphor initiated by Forceville (1996 , 2006) has been integrated with social semiotic theory and further developed by Feng and O’Halloran (2013) . Feng and O’Halloran (2013) explored the structural features of visual images and models the visual representation of metaphor with respect to the representational, interactive and compositional metafunctions. Their social semiotic model provides a comprehensive account of the visual realization of both creative and conventional metaphors. The model has been widely applied in studies with a social semiotic background and has been used to address the impact of multimodal metaphor in different types of multimodal texts, such as advertisements (e.g., Liu and Zhang, 2020 ). Nonetheless, more empirical studies concerning more different genres are needed to demonstrate its usefulness. Further, how multimodal metaphor works and how to interpret its power is a research direction in the current research landscape.

4.2. Research direction driven by the theory of pragmatics

Within the interaction theory, Ojha and Indurkhya (2016) proposed an improved model for metaphor processing based on the perception theories ( O’Regan and Noe, 2001 ; Zimbardo and Gerrig, 2002 ) and integrated model of text and image processing ( Schnotz, 2002 ). The model comprises top-down and bottom-up mechanisms, which allow the conceptual and the perceptual features to stimulate each other. Such metaphor features are seen as emergent features. In their model, context is a key role in identifying the source and the target of a visual metaphor. The model has provided implications for the analysis on the design of visual metaphors (e.g., Ojha and Indurkhya, 2020 ), which would continue to be a direction worth further study.

In the blending theory, Fauconnier and Turner (2002) developed the model into a more systematic, mature and adaptive theory. The three dimensions for the interpretation: composition, completion and elaboration, have been further expanded. The generated emergent structure in the three dimensions is called “running the blend” and is seen as a dynamic and complex cognitive process highly related to the social world. This model thus concerns the pragmatic and sociological interpretation. This model has been applied by Li and Dai (2020) to explore the hidden ideology of print advertisement. It has also been used in the science education field, such as Fredriksson and Pelger (2020) . They use it to help students verbalize and visualize abstract phenomena and concepts. Their study demonstrates that the model could support students to understand science matters and their learning process and suggests research directions both in exploiting the use of multimodal metaphors in the education field and in evaluating the use of multimodal metaphors.

4.3. Research direction driven by visual/multimodal rhetoric theory

While the above three approaches have illuminated the major directions of multimodal metaphor studies, recent research shows there is another emerging new direction in multimodal metaphor studies. Such studies tend to use the term visual metaphor and draw on rhetoric, pragmatics and argumentation theories, aiming to analyze the impact of multimodal metaphors in, particularly, multimodal arguments. The researchers apply the visual rhetorical theory driven by Barthes’s rhetoric of images, to understand what rhetorical effect of the use of visual metaphor or other rhetorical devices for the reconstruction of argumentation ( Kjeldsen, 2018 ; Tseronis, 2021 ). The study implies a research tendency of combining multimodal rhetoric theory and argumentation theory to investigate the power of multimodal metaphor in multimodal arguments. The exploration in this field could make clear how multimodal argumentation works as well as how to evaluate them.

In all, the studies within cognitive linguistic theory provide an access to understanding the operation of multimodal metaphor mechanism. The studies within pragmatics accentuate the role of social cultural context and the pragmatic functions of multimodal metaphor. Further, the studies from a visual rhetoric theory are enlightening in integrating conceptual metaphor theory, pragmatics and argumentation theory to explore the rhetorical effect. Those studies also show an interdisciplinary approach to multimodal metaphor research.

To conclude, based on the literature review, we have found that cognitive topics are still the common trend in multimodal metaphor studies. Second, most of the multimodal metaphor studies taking advertisements as data. Further research may include more types of multimodal discourse to understand multimodal metaphor more systematically and therefore to further improve current modes for multimodal metaphor interpretations. Third, the impact of multimodal metaphors in multimodal arguments is a new field worth further exploration. Last, more works should be done using multimodal corpus analysis and empirical approaches to prove the applicability of various models.

5. Conclusion

Multimodal metaphor including metaphor constructed in non-verbal expressions, i.e., pictorial/visual metaphor is an important concept as it is based on theoretical mechanisms of interaction, blended space and conceptual blending and is applicable in many practical scenarios such as advertisement and animation. While multimodal metaphor has been a prospering concept that has been increasingly investigated, there remain few studies that address its research trend through a bibliometric analysis.

Based on the 397 articles obtained from the Web of Science (WoS) Core Collection Database in all relevant editions, the current study reveals significant patterns in publication features including publication years, country/region participation, journal publication, participating authors, citation and research domain of concurrence. It is found that there has been a general rising trend in the research on multimodal metaphor with 2020 the most fruitful year. USA is the country with the highest number of documents and collaboration links. Journal of Advertising and Journal of Pragmatics are two most influential sources in terms of the number of citations and co-citations. With minimal co-authorship with others, Charles Forceville is the most impactful author as far as the total link strength and the total number of citations are concerned. This has been confirmed by citation analysis of most cited reference and bibliographic coupling. Co-occurrence in key word plus suggests 11 research domains related to what framework to use, what model to interpret, and how to interpret metaphors in different genres.

In addition to the two research aims concluded from the most frequent hot topics, we have discussed three research directions driven by three different strains of theories, while they are all cognitive in nature. Our findings suggest that the research trends in existing research on multimodal metaphor lie in the types of multimodal discourses, the investigation in multimodal arguments and the use of multimodal corpus analysis.

This study can be potentially useful for those attempting to contribute to the existing line of research, as it provides a detailed account of the entire landscape of literature on multimodal metaphor and sheds light on the possible research directions for further exploration. However, there are limitations that should be noted. First, only one source of data is considered. Future research can involve other sources of data such as Google Scholar and Scopus to generate a more extending view. Second, we have only used VOS viewer as the only bibliometric tool, which means the aspects discussed on the scholarship collected are restricted. It is possible to employ other similar tools such as CiteSpace and Network Workbench to look into more features of the publications.

Data availability statement

The original contributions presented in this study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

Author contributions

ZZ compiled the data and did the statistics and was devoted to chapters 2, 3.1 and conclusion. SW was devoted to chapters 1, 3.2, and 4. SC participated in the discussion and proofread the whole manuscript. All authors annotated the data, contributed to the article, and approved the submitted version.

This work was supported by Project of Humanities and Social Sciences of Ministry of Education in China (22YJA740004), Guangzhou Philosophy and Social Science Planning 2021 Project (2021GZGJ201), and Guangdong Social Science Planning 2021 Foreign Language Subject Special Project (GD21WZX01-02).

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Keywords : multimodal metaphor, visual metaphor, pictorial metaphor, bibliometric analysis, web of science (WoS) database, trends

Citation: Zhong Z, Wen S and Chen S (2023) Research trends in multimodal metaphor: a bibliometric analysis. Front. Psychol. 14:1144725. doi: 10.3389/fpsyg.2023.1144725

Received: 15 January 2023; Accepted: 31 March 2023; Published: 17 April 2023.

Reviewed by:

Copyright © 2023 Zhong, Wen and Chen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Suijun Wen, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

• DOI: 10.54097/zw1t3y94
• Corpus ID: 272032426

Analysis of Multimodal Metaphor and Values Representation in Children’s Picture Books

• Published in International Journal of… 20 August 2024
• Education, Linguistics

7 References

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How ethical behavior is considered in different contexts: a bibliometric analysis of global research trends.

1. Introduction

2. literature review, 2.1. ethical behavior, 2.2. bibliometric, 3. methodology, 4.1. countries and their concerns about ethical behavior, 4.2. key themes in research terms, 4.3. bibliographic coupling analysis, 4.3.1. journals, 4.3.2. authors, 4.4. co-citation analysis, 4.4.1. publications, 4.4.2. journals, 4.4.3. authors, 5. discussion, 5.1. ethical behavior in consumption, 5.2. ethical behavior in leadership, 5.2.1. social learning theory (slt), 5.2.2. social exchange theory (set), transformational leadership, authentic leadership, spiritual leadership, 5.3. ethical behavior in business.

• Focus on social responsibility;
• Emphasis on honesty and fairness;
• Focus on “Golden Rules”;
• Values that are consistent with a person’s behavior or religious beliefs;
• Obligations, responsibilities, and rights towards dedicated or enlightened work;
• Philosophy of good or bad;
• Ability to clarify issues in decision making;
• Focus on personal conscience;
• Systems or theories of justice that question the quality of one’s relationships;
• The relationship of the means to ends;
• Concern with integrity, what should be, habits, logic, and principles of Aristotle;
• Emphasis on virtue, leadership, confidentiality, judgment of others, putting God first, topicality, and publicity.

Values, Business Ethics, and Corporate Social Responsibility (CSR)

5.4. ethical behavior in the medical context, 5.4.1. autonomy, 5.4.2. beneficence, 5.4.3. non-maleficence, 5.4.4. fairness, 5.5. ethical behaviour in education, 5.5.1. violation of school/university regulation, 5.5.2. selfishness, 5.5.3. cheating, 5.5.4. computer ethics, 5.6. ethical context in organization, 5.6.1. context of organizational ethical climate, 5.6.2. context of organizational ethical culture, 6. conclusions, 7. limitations and future research, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

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Vu Lan Oanh, L.; Tettamanzi, P.; Tien Minh, D.; Comoli, M.; Mouloudj, K.; Murgolo, M.; Dang Thu Hien, M. How Ethical Behavior Is Considered in Different Contexts: A Bibliometric Analysis of Global Research Trends. Adm. Sci. 2024 , 14 , 200. https://doi.org/10.3390/admsci14090200

Vu Lan Oanh L, Tettamanzi P, Tien Minh D, Comoli M, Mouloudj K, Murgolo M, Dang Thu Hien M. How Ethical Behavior Is Considered in Different Contexts: A Bibliometric Analysis of Global Research Trends. Administrative Sciences . 2024; 14(9):200. https://doi.org/10.3390/admsci14090200

Vu Lan Oanh, Le, Patrizia Tettamanzi, Dinh Tien Minh, Maurizio Comoli, Kamel Mouloudj, Michael Murgolo, and Mai Dang Thu Hien. 2024. "How Ethical Behavior Is Considered in Different Contexts: A Bibliometric Analysis of Global Research Trends" Administrative Sciences 14, no. 9: 200. https://doi.org/10.3390/admsci14090200

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11 Ways To Do SEO Content Research Beyond Competitor Analysis

Take your SEO strategy to the next level with content research beyond competitor analysis. Discover how to develop unique niche strategies for long-term success.

Early SEO milestones might be easy, but scaling the results needs an upgraded approach.

What could that look like?

Like startups that come up with a solid niche idea and compete significantly with larger companies, we SEO pros and content strategists need to work harder to develop unique, fresh, niche strategies.

However, whenever we think of creating strategies, we start looking at what competitors are doing . We start feeling that we can win this game by outperforming our competitors.

Remember: we win when our focus is on winning the game and not on how to make our competitors lose.

So, here comes an upgraded approach to our SEO strategy – going beyond competitor analysis.

However, since our SEO strategies heavily rely on content, we’ll discuss content research beyond competitor analysis in this blog.

Now, What Is Content Research Beyond Competitor Analysis?

Most of us analyze our competitors to develop content ideas. It’s easy and quick.

But…

What if your competitors are ranking in the top positions but are not serving users’ intent?

What if your competitors might not be yielding enough traffic despite better rankings?

What if your competitors are driving massive organic traffic but not enough conversions?

Also, there may be some competitors that are doing extremely well regarding content KPIs serving SEO growth.

You may feel that if the competitors can achieve such results in one year, you can achieve them in six months by copying their strategies.

But that’s where you limit yourself in growth. Your competitors’ SEO and content teams might also be struggling; who knows?

This is why your content research must go beyond competitor analysis.

In this approach, we don’t look at what content competitors have written.

We don’t want to copy them or repeat their mistakes. We want to work in ways that truly resonate with our target audiences, geographies, business models, and industries.

So, the “ content research beyond competitor analysis” approach helps us bring unique and fresh perspectives to our content research, creating incredible value for our audience and clients and scaling our SEO results extensively.

11 Ways Of Content Research Beyond Competitor Analysis To Scale SEO ROI

We have 11 ways to use this approach. Let’s uncover them one by one with step-by-step processes and examples.

1. Use Semrush

This is our basic step of content research since most of our initial goal is driving organic traffic.

And because Semrush is handy for most of our team members at Missive Digital, we log in immediately to start our content research instead of looking at competitors.

We put seed, actual, long-tail, and more keywords to do our content research, depending on the search volume, keyword difficulty, and search intent.

For example, we have put “diamond jewelry” into Semrush and will add the filters according to our SEO strategy.

Another content research feature of Semrush that we use extensively is Topic Research. We choose the content topics based on which ones relate directly or indirectly to our website.

2. Use Ahrefs

To do the content research on Ahrefs, we follow the same steps as Semrush, but here, we also use Content Explorer.

We filter based on the Page Traffic and reference domains to identify queries that can bring us traffic and conversions.

Then, we also examine the frequency of republishing, which gives our team an idea of when to schedule it next for content optimization, considering the performance.

3. Use Google News

While auditing the content, if we realize that the client is already writing a lot of content, we try researching content ideas through Google News.

Also, for some D2C industries like jewelry, the trend also comes from celebrities wearing them – so we keep a close eye on Google News.

For example, the screenshot below shows a ‘B’ necklace worn by Selena Gomez in reference to her boyfriend.

Bingo! Now, we have to discuss with the client’s team for our next content piece.

4. Use People Also Ask, AlsoAsked

Since most B2B IT and SaaS clients are highly technical, we sometimes struggle to understand the topic and create a content strategy.

People Also Ask on Google Search and AlsoAsked.com by Mark Williams-Cook works like a savior during our content research.

5. Check Google Trends

No matter what industry you are in , you’ve got something or the other trending.

In our SEO industry, SearchGPT is trending.

So it’s worth writing about it to take the early advantage and grab the traffic share.

See, a lot of people are writing about it:

So, it’s worth constantly watching what’s trending via Google Trends.

6. Hop On ChatGPT Or Gemini

Remember, we are here to do content research on ChatGPT or Gemini, not to choose the titles they suggest.

Here is a sample content research prompt that we have put for a contact center software company on ChatGPT:

And here are the responses below:

Since the topics are not up to the mark considering the audience (“BPO” in this case), based on the above content ideas, we’ll pick up the seed keywords or topics such as:

• Lessons from a Legacy Contact Center Software Company.
• The Contact Center Software Market In The BPO Segment.
• Optimizing Your Contact Center Operations.
• How to Drive Innovation in Your Customer Support Department?

7. Monitor Social Media

Yes, we are all active on social media, so we can use it for our content research. Still, we are not considering competitors on social media at the moment.

This viral X thread inspired us to write a blog:

Similarly, this article on content research was inspired by my recent post on LinkedIn :

These are examples of self-created social media content that can be turned into blogs.

However, you can keep monitoring the types of content that get the most visibility and engagement on social media – be it LinkedIn, Instagram, X, or any other platform.

Turn them into your blogs or webinars, but don’t forget to mention them since it’s their original content idea.

8. Dive Into Industry-specific Research Studies

The most unique way to research content ideas is to read your industry-specific research studies extensively. And there’s no one way to do it.

For example, for one of the ecommerce consulting companies, we can get various content ideas from HBR’s eCommerce pricing test :

• Why Should Ecommerce Brands Stop Offering Free Shipping?
• X Benefits of No Free Shipping or Conditional Shipping.
• Free Shipping vs. Conditional Shipping.

In the below study by Broadridge on Digital Transformation , the below can become the topic clusters, and each can have its own spoke-like content topics.

For example, if we take Unleashing Artificial Intelligence, we can pick up so many topics out of just one graphic:

9. Check Industry-Specific Forums/Communities

Most of our clientele includes IT companies, and we have used IT forums and communities like StackOverflow for content research.

For example, we can come up with the below topic clusters when covering Flutter for the non-technical and technical target audiences:

• Flutter animation widgets.
• Flutter dependency management.
• Why add Firebase to your Flutter app?

Similarly, there will be many such forums or communities of your client or employers available to peek into for such content ideas, except for competitive analysis.

10. Google site:reddit.com “my topic”

One such unique idea by Kunjal Chawhan is to Google site:reddit.com “my topic ,”  and let’s see what content ideas look like for a couple of topics:

Looking at the above screenshot, below are the topics that we can definitely create:

• X Most Popular Social Media Platforms for Ecommerce.
• How to Use Video Podcasts to Drive Ecommerce Sales?
• How to Boost Ecommerce Sales When Digital Marketing Seems Expensive?

So yes, Kunjal’s way of content research is amazing, and from that, you can similarly Google:

site:“your industry’s leading site” “topic”

For example:

• site:searchenginejournal.com “ai content”
• site:quora.com “ai content”
• site:practicalecommerce.com “sales”

Let’s move on to the last but not the least method of content research, except for looking at competitors.

11. See What Competing Sites Have NOT Covered

Now you might wonder, “Weren’t the above content research ways except for competitors analysis?”

Yes, they are the ways to research content ideas except for what competitors have written.

But here, I’m trying to make a point where you have to see exactly what indirect competing sites are NOT writing about despite targeting the same industry, keyword clusters, and audience.

What is an indirect competing site?

An indirect competing site is a website that ranks for the industry and search queries of your target audience but is not exactly your product/service competitor. This can be a marketplace, publishing site, or product review site.

Let’s take a website, “leadsquared.com,” for indirect competitive analysis and pick the queries that rank after 50th positions and have a keyword difficulty of less than 29.

Pick those queries and search on Google: site:leadsquared.com “sales funnel vs sales pipeline” .

In short, you can cover the below topics:

• Sales funnel vs. sales pipeline.
• Sales funnel vs. marketing funnel.
• Sales funnel vs. flywheel.

Just ensure these content topics align with your offerings to bring maximum ROI.

How Will Content Research Beyond Competitor Analysis Contribute To SEO Efforts?

When you go beyond competitor analysis for content research, you discover a few benefits:

• You innovate – With innovative content ideas, you can experiment and build better strategies that can bring unbelievable results. Also, with AI taking the space predominantly, businesses are looking for innovation in their business and marketing. So when you innovate, you may get better attention and even resources.
• You get niche opportunities – Instead of just focusing on what competitors are doing, you go deeper into understanding your target audience and explore new content ideas that your competitors might have missed. In such scenarios, you get better results since competition is reduced.
• You create unique, audience-specific content – My LinkedIn post saw great engagement because it resonated with its audience. This opened us to something unique and specific to the pain point of SEOs and content strategists: content ideation to scale SEO results with a not-so-usual approach. Such content helps us build authority in the market, which is essential today to becoming market leaders.
• You capitalize on emerging trends – Being an early adopter of something has huge potential for success. When you create your content strategy focused on what’s new or trending in your industry before it becomes mainstream, you get the most eyes right from the beginning and even repeat eyes going forward.
• You build better engagement and loyalty – You can extend beyond blogs, a traditional way of driving SEO results. Videos, whitepapers, case studies, user-generated content, and many more content formats can take the lead in building user engagement and brand loyalty through SEO.
• You earn backlinks – Yes, such unique content may require less effort to build backlinks since it can earn them.

Stop looking at competitors for content research; try using these fresh and unique ways to drive better content ROI.

Just remember two things: Competitors are not always right, and you are not necessarily required to look upon them when developing your SEO content strategies.

You can copy and paste your competitors’ strategies to achieve certain SEO milestones, but creating history requires an upgraded approach. What say?

More resources: 

• SEO Competitive Analysis: The Definitive Guide
• Leveraging Generative AI Tools For SEO
• Perfectly Optimized Content From Start To Finish

Featured Image: Natalya Kosarevich /Shutterstock

Himani is the Founder of Missive Digital, an organic marketing agency that focuses on enhancing the brand positioning of the ...

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Key things to know about U.S. election polling in 2024

Confidence in U.S. public opinion polling was shaken by errors in 2016 and 2020. In both years’ general elections, many polls underestimated the strength of Republican candidates, including Donald Trump. These errors laid bare some real limitations of polling.

In the midterms that followed those elections, polling performed better . But many Americans remain skeptical that it can paint an accurate portrait of the public’s political preferences.

Restoring people’s confidence in polling is an important goal, because robust and independent public polling has a critical role to play in a democratic society. It gathers and publishes information about the well-being of the public and about citizens’ views on major issues. And it provides an important counterweight to people in power, or those seeking power, when they make claims about “what the people want.”

The challenges facing polling are undeniable. In addition to the longstanding issues of rising nonresponse and cost, summer 2024 brought extraordinary events that transformed the presidential race . The good news is that people with deep knowledge of polling are working hard to fix the problems exposed in 2016 and 2020, experimenting with more data sources and interview approaches than ever before. Still, polls are more useful to the public if people have realistic expectations about what surveys can do well – and what they cannot.

With that in mind, here are some key points to know about polling heading into this year’s presidential election.

Probability sampling (or “random sampling”). This refers to a polling method in which survey participants are recruited using random sampling from a database or list that includes nearly everyone in the population. The pollster selects the sample. The survey is not open for anyone who wants to sign up.

Online opt-in polling (or “nonprobability sampling”). These polls are recruited using a variety of methods that are sometimes referred to as “convenience sampling.” Respondents come from a variety of online sources such as ads on social media or search engines, websites offering rewards in exchange for survey participation, or self-enrollment. Unlike surveys with probability samples, people can volunteer to participate in opt-in surveys.

Nonresponse and nonresponse bias. Nonresponse is when someone sampled for a survey does not participate. Nonresponse bias occurs when the pattern of nonresponse leads to error in a poll estimate. For example, college graduates are more likely than those without a degree to participate in surveys, leading to the potential that the share of college graduates in the resulting sample will be too high.

Mode of interview. This refers to the format in which respondents are presented with and respond to survey questions. The most common modes are online, live telephone, text message and paper. Some polls use more than one mode.

Weighting. This is a statistical procedure pollsters perform to make their survey align with the broader population on key characteristics like age, race, etc. For example, if a survey has too many college graduates compared with their share in the population, people without a college degree are “weighted up” to match the proper share.

How are election polls being conducted?

Pollsters are making changes in response to the problems in previous elections. As a result, polling is different today than in 2016. Most U.S. polling organizations that conducted and publicly released national surveys in both 2016 and 2022 (61%) used methods in 2022 that differed from what they used in 2016 . And change has continued since 2022.

One change is that the number of active polling organizations has grown significantly, indicating that there are fewer barriers to entry into the polling field. The number of organizations that conduct national election polls more than doubled between 2000 and 2022.

This growth has been driven largely by pollsters using inexpensive opt-in sampling methods. But previous Pew Research Center analyses have demonstrated how surveys that use nonprobability sampling may have errors twice as large , on average, as those that use probability sampling.

The second change is that many of the more prominent polling organizations that use probability sampling – including Pew Research Center – have shifted from conducting polls primarily by telephone to using online methods, or some combination of online, mail and telephone. The result is that polling methodologies are far more diverse now than in the past.

(For more about how public opinion polling works, including a chapter on election polls, read our short online course on public opinion polling basics .)

All good polling relies on statistical adjustment called “weighting,” which makes sure that the survey sample aligns with the broader population on key characteristics. Historically, public opinion researchers have adjusted their data using a core set of demographic variables to correct imbalances between the survey sample and the population.

But there is a growing realization among survey researchers that weighting a poll on just a few variables like age, race and gender is insufficient for getting accurate results. Some groups of people – such as older adults and college graduates – are more likely to take surveys, which can lead to errors that are too sizable for a simple three- or four-variable adjustment to work well. Adjusting on more variables produces more accurate results, according to Center studies in 2016 and 2018 .

A number of pollsters have taken this lesson to heart. For example, recent high-quality polls by Gallup and The New York Times/Siena College adjusted on eight and 12 variables, respectively. Our own polls typically adjust on 12 variables . In a perfect world, it wouldn’t be necessary to have that much intervention by the pollster. But the real world of survey research is not perfect.

Predicting who will vote is critical – and difficult. Preelection polls face one crucial challenge that routine opinion polls do not: determining who of the people surveyed will actually cast a ballot.

Roughly a third of eligible Americans do not vote in presidential elections , despite the enormous attention paid to these contests. Determining who will abstain is difficult because people can’t perfectly predict their future behavior – and because many people feel social pressure to say they’ll vote even if it’s unlikely.

No one knows the profile of voters ahead of Election Day. We can’t know for sure whether young people will turn out in greater numbers than usual, or whether key racial or ethnic groups will do so. This means pollsters are left to make educated guesses about turnout, often using a mix of historical data and current measures of voting enthusiasm. This is very different from routine opinion polls, which mostly do not ask about people’s future intentions.

When major news breaks, a poll’s timing can matter. Public opinion on most issues is remarkably stable, so you don’t necessarily need a recent poll about an issue to get a sense of what people think about it. But dramatic events can and do change public opinion , especially when people are first learning about a new topic. For example, polls this summer saw notable changes in voter attitudes following Joe Biden’s withdrawal from the presidential race. Polls taken immediately after a major event may pick up a shift in public opinion, but those shifts are sometimes short-lived. Polls fielded weeks or months later are what allow us to see whether an event has had a long-term impact on the public’s psyche.

How accurate are polls?

The answer to this question depends on what you want polls to do. Polls are used for all kinds of purposes in addition to showing who’s ahead and who’s behind in a campaign. Fair or not, however, the accuracy of election polling is usually judged by how closely the polls matched the outcome of the election.

By this standard, polling in 2016 and 2020 performed poorly. In both years, state polling was characterized by serious errors. National polling did reasonably well in 2016 but faltered in 2020.

In 2020, a post-election review of polling by the American Association for Public Opinion Research (AAPOR) found that “the 2020 polls featured polling error of an unusual magnitude: It was the highest in 40 years for the national popular vote and the highest in at least 20 years for state-level estimates of the vote in presidential, senatorial, and gubernatorial contests.”

How big were the errors? Polls conducted in the last two weeks before the election suggested that Biden’s margin over Trump was nearly twice as large as it ended up being in the final national vote tally.

Errors of this size make it difficult to be confident about who is leading if the election is closely contested, as many U.S. elections are .

Pollsters are rightly working to improve the accuracy of their polls. But even an error of 4 or 5 percentage points isn’t too concerning if the purpose of the poll is to describe whether the public has favorable or unfavorable opinions about candidates , or to show which issues matter to which voters. And on questions that gauge where people stand on issues, we usually want to know broadly where the public stands. We don’t necessarily need to know the precise share of Americans who say, for example, that climate change is mostly caused by human activity. Even judged by its performance in recent elections, polling can still provide a faithful picture of public sentiment on the important issues of the day.

The 2022 midterms saw generally accurate polling, despite a wave of partisan polls predicting a broad Republican victory. In fact, FiveThirtyEight found that “polls were more accurate in 2022 than in any cycle since at least 1998, with almost no bias toward either party.” Moreover, a handful of contrarian polls that predicted a 2022 “red wave” largely washed out when the votes were tallied. In sum, if we focus on polling in the most recent national election, there’s plenty of reason to be encouraged.

Compared with other elections in the past 20 years, polls have been less accurate when Donald Trump is on the ballot. Preelection surveys suffered from large errors – especially at the state level – in 2016 and 2020, when Trump was standing for election. But they performed reasonably well in the 2018 and 2022 midterms, when he was not.

During the 2016 campaign, observers speculated about the possibility that Trump supporters might be less willing to express their support to a pollster – a phenomenon sometimes described as the “shy Trump effect.” But a committee of polling experts evaluated five different tests of the “shy Trump” theory and turned up little to no evidence for each one . Later, Pew Research Center and, in a separate test, a researcher from Yale also found little to no evidence in support of the claim.

Instead, two other explanations are more likely. One is about the difficulty of estimating who will turn out to vote. Research has found that Trump is popular among people who tend to sit out midterms but turn out for him in presidential election years. Since pollsters often use past turnout to predict who will vote, it can be difficult to anticipate when irregular voters will actually show up.

The other explanation is that Republicans in the Trump era have become a little less likely than Democrats to participate in polls . Pollsters call this “partisan nonresponse bias.” Surprisingly, polls historically have not shown any particular pattern of favoring one side or the other. The errors that favored Democratic candidates in the past eight years may be a result of the growth of political polarization, along with declining trust among conservatives in news organizations and other institutions that conduct polls.

Whatever the cause, the fact that Trump is again the nominee of the Republican Party means that pollsters must be especially careful to make sure all segments of the population are properly represented in surveys.

The real margin of error is often about double the one reported. A typical election poll sample of about 1,000 people has a margin of sampling error that’s about plus or minus 3 percentage points. That number expresses the uncertainty that results from taking a sample of the population rather than interviewing everyone . Random samples are likely to differ a little from the population just by chance, in the same way that the quality of your hand in a card game varies from one deal to the next.

The problem is that sampling error is not the only kind of error that affects a poll. Those other kinds of error, in fact, can be as large or larger than sampling error. Consequently, the reported margin of error can lead people to think that polls are more accurate than they really are.

There are three other, equally important sources of error in polling: noncoverage error , where not all the target population has a chance of being sampled; nonresponse error, where certain groups of people may be less likely to participate; and measurement error, where people may not properly understand the questions or misreport their opinions. Not only does the margin of error fail to account for those other sources of potential error, putting a number only on sampling error implies to the public that other kinds of error do not exist.

Several recent studies show that the average total error in a poll estimate may be closer to twice as large as that implied by a typical margin of sampling error. This hidden error underscores the fact that polls may not be precise enough to call the winner in a close election.

Other important things to remember

Transparency in how a poll was conducted is associated with better accuracy . The polling industry has several platforms and initiatives aimed at promoting transparency in survey methodology. These include AAPOR’s transparency initiative and the Roper Center archive . Polling organizations that participate in these organizations have less error, on average, than those that don’t participate, an analysis by FiveThirtyEight found .

Participation in these transparency efforts does not guarantee that a poll is rigorous, but it is undoubtedly a positive signal. Transparency in polling means disclosing essential information, including the poll’s sponsor, the data collection firm, where and how participants were selected, modes of interview, field dates, sample size, question wording, and weighting procedures.

There is evidence that when the public is told that a candidate is extremely likely to win, some people may be less likely to vote . Following the 2016 election, many people wondered whether the pervasive forecasts that seemed to all but guarantee a Hillary Clinton victory – two modelers put her chances at 99% – led some would-be voters to conclude that the race was effectively over and that their vote would not make a difference. There is scientific research to back up that claim: A team of researchers found experimental evidence that when people have high confidence that one candidate will win, they are less likely to vote. This helps explain why some polling analysts say elections should be covered using traditional polling estimates and margins of error rather than speculative win probabilities (also known as “probabilistic forecasts”).

National polls tell us what the entire public thinks about the presidential candidates, but the outcome of the election is determined state by state in the Electoral College . The 2000 and 2016 presidential elections demonstrated a difficult truth: The candidate with the largest share of support among all voters in the United States sometimes loses the election. In those two elections, the national popular vote winners (Al Gore and Hillary Clinton) lost the election in the Electoral College (to George W. Bush and Donald Trump). In recent years, analysts have shown that Republican candidates do somewhat better in the Electoral College than in the popular vote because every state gets three electoral votes regardless of population – and many less-populated states are rural and more Republican.

For some, this raises the question: What is the use of national polls if they don’t tell us who is likely to win the presidency? In fact, national polls try to gauge the opinions of all Americans, regardless of whether they live in a battleground state like Pennsylvania, a reliably red state like Idaho or a reliably blue state like Rhode Island. In short, national polls tell us what the entire citizenry is thinking. Polls that focus only on the competitive states run the risk of giving too little attention to the needs and views of the vast majority of Americans who live in uncompetitive states – about 80%.

Fortunately, this is not how most pollsters view the world . As the noted political scientist Sidney Verba explained, “Surveys produce just what democracy is supposed to produce – equal representation of all citizens.”

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Scott Keeter is a senior survey advisor at Pew Research Center .

Courtney Kennedy is Vice President of Methods and Innovation at Pew Research Center .

How do people in the U.S. take Pew Research Center surveys, anyway?

How public polling has changed in the 21st century, what 2020’s election poll errors tell us about the accuracy of issue polling, a field guide to polling: election 2020 edition, methods 101: how is polling done around the world, most popular.

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‘This could change everything!’ Nous Research unveils new tool to train powerful AI models with 10,000x efficiency

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Nous Research turned heads earlier this month with the release of its permissive, open-source Llama 3.1 variant Hermes 3 .

Now, the small research team dedicated to making “personalized, unrestricted AI” models has announced another seemingly massive breakthrough: DisTrO (Distributed Training Over-the-Internet), a new optimizer that reduces the amount of information that must be sent between various GPUs (graphics processing units) during each step of training an AI model.

Nous’s DisTrO optimizer means powerful AI models can now be trained outside of big companies, across the open web on consumer-grade connections, potentially by individuals or institutions working together from around the world.

DisTrO has already been tested and shown in a Nous Research technical paper to yield an 857 times efficiency increase compared to one popular existing training algorithm, All-Reduce , as well as a massive reduction in the amount of information transmitted during each step of the training process (86.8 megabytes compared to 74.4 gigabytes) while only suffering a slight loss in overall performance. See the results in the table below from the Nous Research technical paper:

Ultimately, the DisTrO method could open the door to many more people being able to train massively powerful AI models as they see fit.

As the firm wrote in a post on X yesterday : “Without relying on a single company to manage and control the training process, researchers and institutions can have more freedom to collaborate and experiment with new techniques, algorithms, and models. This increased competition fosters innovation, drives progress, and ultimately benefits society as a whole.”

What if you could use all the computing power in the world to train a shared, open source AI model? Preliminary report: https://t.co/b1XgJylsnV Nous Research is proud to release a preliminary report on DisTrO (Distributed Training Over-the-Internet) a family of… pic.twitter.com/h2gQJ4m7lB — Nous Research (@NousResearch) August 26, 2024

The problem with AI training: steep hardware requirements

As covered on VentureBeat previously, Nvidia’s GPUs in particular are in high demand in the generative AI era, as the expensive graphics cards’ powerful parallel processing capabilities are needed to train AI models efficiently and (relatively) quickly. This blog post at APNic describes the process well.

A big part of the AI training process relies on GPU clusters — multiple GPUs — exchanging information with one another about the model and the information “learned” within training data sets.

However, this “inter-GPU communication” requires that GPU clusters be architected, or set up, in a precise way in controlled conditions, minimizing latency and maximizing throughput. Hence why companies such as Elon Musk’s Tesla are investing heavily in setting up physical “superclusters” with many thousands (or hundreds of thousands) of GPUs sitting physically side-by-side in the same location — typically a massive airplane hangar-sized warehouse or facility.

Because of these requirements, training generative AI — especially the largest and most powerful models — is typically an extremely capital-heavy endeavor, one that only some of the most well-funded companies can engage in, such as Tesla, Meta, OpenAI, Microsoft, Google, and Anthropic.

The training process for each of these companies looks a little different, of course. But they all follow the same basic steps and use the same basic hardware components. Each of these companies tightly controls its own AI model training processes, and it can be difficult for incumbents, much less laypeople outside of them, to even think of competing by training their own similarly-sized (in terms of parameters, or the settings under the hood) models.

But Nous Research, whose whole approach is essentially the opposite — making the most powerful and capable AI it can on the cheap, openly, freely, for anyone to use and customize as they see fit without many guardrails — has found an alternative.

What DisTrO does differently

While traditional methods of AI training require synchronizing full gradients across all GPUs and rely on extremely high bandwidth connections, DisTrO reduces this communication overhead by four to five orders of magnitude.

The paper authors haven’t fully revealed how their algorithms reduce the amount of information at each step of training while retaining overall model performance, but plan to release more on this soon.

The reduction was achieved without relying on amortized analysis or compromising the convergence rate of the training, allowing large-scale models to be trained over much slower internet connections — 100Mbps download and 10Mbps upload, speeds available to many consumers around the world.

The authors tested DisTrO using the Meta Llama 2, 1.2 billion large language model (LLM) architecture and achieved comparable training performance to conventional methods with significantly less communication overhead.

They note that this is the smallest-size model that worked well with the DisTrO method, and they “do not yet know whether the ratio of bandwidth reduction scales up, down, or stays constant as model size increases.”

Yet, the authors also say that “our preliminary tests indicate that it is possible to get a bandwidth requirements reduction of up to 1000x to 3000x during the pre-training,” phase of LLMs, and “for post-training and fine-tuning, we can achieve up to 10000x without any noticeable degradation in loss.”

They further hypothesize that the research, while initially conducted on LLMs, could be used to train large diffusion models (LDMs) as well: think the Stable Diffusion open source image generation model and popular image generation services derived from it such as Midjourney .

Still need good GPUs

To be clear: DisTrO still relies on GPUs — only instead of clustering them all together in the same location, now they can be spread out across the world and communicate over the consumer internet.

Specifically, DisTrO was evaluated using 32x H100 GPUs, operating under the Distributed Data Parallelism (DDP) strategy, where each GPU had the entire model loaded in VRAM .

This setup allowed the team to rigorously test DisTrO’s capabilities and demonstrate that it can match the convergence rates of AdamW+All-Reduce despite drastically reduced communication requirements.

This result suggests that DisTrO can potentially replace existing training methods without sacrificing model quality, offering a scalable and efficient solution for large-scale distributed training.

By reducing the need for high-speed interconnects DisTrO could enable collaborative model training across decentralized networks, even with participants using consumer-grade internet connections.

The report also explores the implications of DisTrO for various applications, including federated learning and decentralized training.

Additionally, DisTrO’s efficiency could help mitigate the environmental impact of AI training by optimizing the use of existing infrastructure and reducing the need for massive data centers.

Moreover, the breakthroughs could lead to a shift in how large-scale models are trained, moving away from centralized, resource-intensive data centers towards more distributed, collaborative approaches that leverage diverse and geographically dispersed computing resources.

What’s next for the Nous Research team and DisTrO?

The research team invites others to join them in exploring the potential of DisTrO. The preliminary report and supporting materials are available on GitHub , and the team is actively seeking collaborators to help refine and expand this groundbreaking technology.

Already, some AI influencers such as @kimmonismus on X (aka chubby) have praised the research as a huge breakthrough in the field, writing, “This could change everything!”

Wow, amazing! This could change everything! https://t.co/2f0PDSaTSm — Chubby♨️ (@kimmonismus) August 27, 2024

With DisTrO, Nous Research is not only advancing the technical capabilities of AI training but also promoting a more inclusive and resilient research ecosystem that has the potential to unlock unprecedented advancements in AI.

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