Discovering Implicit Meanings of Cultural Motifs from Text (2024)

Abstract: Motifs are distinctive, recurring, widely used idiom-like words or phrases, often originating in folklore and usually strongly anchored to a particular cultural or national group. Motifs are significant communicative devices across a wide range of media—including news, literature, and propaganda—because they can concisely imply a large set of culturally relevant associations. One difficulty of understanding motifs is that their meaning is usually implicit, so for an out-group person the meaning is inaccessible. We present the Motif Implicit Meaning Extractor (MIME), a proof-of-concept system designed to automatically identify a motif’s implicit meaning, as evidenced by textual uses of the motif across a large set data. MIME uses several sources (including motif indices, Wikipedia pages on the motifs, explicit explanations of motifs from in-group informants, and news/social media posts where the motif is used) and can generate a structured report of information about a motif understandable to an out-group person. In addition to a variety of examples and information drawn from structured sources, the report includes implicit information about a motif such as the type of reference (e.g., a person, an organization, etc.), it’s general connotation (strongly negative, slightly negative, neutral, etc.), and it’s associations (typically adjectives). We describe how MIME works and demonstrate its operation on a small set of manually curated motifs. We perform a qualitative evaluation of the output, and assess the difficulty of the problem, showing that explicit motif information provided by cultural informants is critical to high quality output, although mining motif usages in news and social media provides useful additional depth. A system such as MIME, appropriately scaled up, would potentially be quite useful to an out-group person trying to understand in-group usages of motifs, and has wide potential applications in domains such as literary criticism, cultural heritage, marketed and branding, and intelligence analysis.

## introduction motifs can be simply described as recurring cultural "memes" that are grounded in stories. motifs often originate in folklore, but are ubiquitous and can be found anywhere that language is influenced by culture. they are interesting and useful because they provide a compact source of cultural information: they concisely communicate a constellation of related cultural ideas, associations, assumptions, and knowledge. one common western motif that illustrates the importance and information density of motifs is troll under the bridge. one folktale containing the motif, the three billy goats gruff, involves a troll, hiding under a bridge, who tries to devour the goats as they attempt to cross. the motif is found across the folklore of northern europe, especially norway. to members of many western cultures, invoking this motif brings a number of related ideas to mind that are not directly communicated by the surface meaning of the words: the bridge is along the critical path of the heroes and they must cross it to achieve their goal; the troll lives under the bridge, surprising those who attempt to cross it; the troll tries to kill, eat, or otherwise extract some value from the would-be crossers; the troll is a squatter, not the officially sanctioned master of the bridge; and the troll usually meets his end at the hands the hero. the modern utility of the motif as a communicative device is clearly visible in the common term patent troll, a person or organization that claims illegitimate ownership over ideas and attempts to extract value from companies who have related products related to those ideas. here we see an analogical transfer of cultural attributes from the troll of folklore to the "troll" of patents. although the example above is drawn from folklore, motifs have importance beyond folktales: they occur in modern stories, news articles, opinion pieces, press releases, propaganda, novels, movies, plays-indeed, anywhere that culture impinges on language. one powerful modern example is the use of the pharaoh motif in modern middle eastern discourse. the pharaoh, which refers to the pharaoh who opposes moses in the narrative found in qu'ran, is an arrogant and obstinate tyrant who oppresses the chosen people, defies the will of god, and is punished for it. in modern islamist extremist discourse, the term pharaoh has been invoked against leaders such as anwar sadat of egypt, ariel sharon of israel, and george w. bush, the last of whom osama bin laden referred to as the "pharaoh of the century" @xcite . without understanding the implications of the pharaoh motif, we would be unable to understand both the content of this message (that these leaders are being cast as oppressors) and the cultural group for whom this message was intended (the chosen people, in this case the ummah). work in cognitive psychology has shown that in-group members understand the associations for motifs from their group better than out-group persons @xcite . critically, people can recognize that a motif is being used, regardless of their group membership, and out-group speakers recognize that those motifs carry special meaning and are aware that they're missing out on that meaning. a natural next step, then, is some way of making motif meanings available to out-group persons. unfortunately, current resources describing motifs are not sufficient for out-group people to understand the deeper meaning and context of a motif. let us take an example of a hindu motif, saraswati. if one looks up saraswati on google or wikipedia, one can read that she is the goddess of knowledge, wisdom, music and art, among other things. but this gives no insight into the meaning of the common phrase "kartik has saraswati on his tongue." it might sound like that means kartik is highly knowledgeable or wise, but what it actually means is kartik never tells a lie. existing resources, like motif indices, dictionary definitions, and encyclopedia entries do not usually bring forth the nuance and the gravity of meaning that motifs typically carry. a number of additional sources of information are needed, including example usages, to provide the larger context of motif meaning. to address this problem, we propose the motif implicit meaning extractor (mime), a proof-ofconcept system that can extract and organize various implicit meaning of motifs for presentation to an out-group audience for better understanding. the mime takes as a query a chunk of text that contains a motif of interest, and uses information from various sources like encyclopedia entries, explanations by in-group informants, and online discourse (e.g., news or social media) to generate a structured report on the common meaning and usage of the motif. appendix a shows a sample mime report. the paper is organized as follows. we first review related work on motifs and their computational processing ( §2). then we list the kinds of implicit information that we would like to extract ( §3, followed by the data we collected to support this extraction ( §4). next we explain in detail the system architecture ( §5). we then describe our qualitative evaluation, showing that critical information presented in the reports is more likely present in explicit in-group explanations and implicit usage, rather than in wikipedia entries or news usages ( §5.5). finally, we discuss limitations of this proofof-concept and map out future work ( §7). ## types of implicit information there are several categories of implicit information, listed below, we would like to expose about motifs to improve understanding by out-group persons. ## results and discussion since this task is a version of targeted information extraction and the output is a human-readable report, it is challenging to apply traditional evaluation metrics to assess effectiveness. we performed a qualitative analysis of the reports, comparing them against the explanations provided by the in-group informants as well as our own general understanding of the meaning of motifs. in general, mime did well at finding implicit information for motifs that have several instances of proper usage in news. it was able to identify the type of reference of the motifs fairly accurately (only 1 answer incorrect/missing, because of insufficient data), which is useful because this information was not provided by the in-group explanations. mime was also able to reveal the general connotation of the motifs correctly across most of the motifs (6 incorrect). in some cases, like leprechaun, from usages in the news data it was able to infer a connotation closer to that provided by the in-group explanations, despite the popular understood meaning being slightly different. this shows that the mime approach adds value beyond what is provided in the explicit in-group explanations. we also evaluated the the proportion of information derived from our three sources. we examined the three main types of implicit informationtype of reference, general connotation, and associations-and counted how often that information could be traced back to a specific type of source (wikipedia, explanations, or online news). the summary of these results are shown in tables 3 and 4 . the results show that the bulk of the associations (roughly 80%) come from the in-group explanations, with approximately 14% coming from news. this result shows the importance of the in-group explanations, and that the meaning of motifs, in general, is not discoverable from usage in modern news alone. while the wikipedia text was not particularly information dense for this task, in our qualitative analysis it played a crucial role of providing additional context in terms of clear-toread summaries and background, thereby providing some level of utility. this is especially visible for the puerto rican motifs, where the wikipedia data was especially devoid of depth, and the reports are notably less rich than the other two groups. overall, we see that the results show the potential of this approach, consistent with its proof-of-concept stage of development. ## limitations and future work as a proof-of-concept, mime has a number of important limitations. first, by design the system does not attempt to extract information that must be obtained via multiple steps of inference. second, the system sometimes reports associations that are inconsistent with those reported by the cultural informants. in our qualitative evaluation this is mostly because the motif seems to have been used incorrectly by out-group persons in online news. a few of these mistakes can be attributed to an adjective that is near the motif but was grammatically attached elsewhere. a more sophisticated approach to attribute extraction could mitigate this issue, or collecting further example usages from cultural informants and assigning higher weight to one key future step is to look more comprehensively at the discourse context of the motifs instances. for instance, we not only have sentiment values and linguistic properties for the sentences with motifs themselves, but also for other sentences that form part of the discourse that involves the motif. using this discourse relation information, we will be able to more accurately predict the associations for the motifs. we could not implement such an approach because of lack of sufficient examples of motifs in narrative text; with more data of real-world narrative usage of motifs, this could potentially be implemented in the future. the importance of the in-group explanations for implicit associations is also revealing. looking only at usages in modern discourse would make it very hard to extract these associations; on the other hand, asking in-group persons to explain every motif is time-consuming and laborious. here, one untapped source of information would be the original narratives themslves, from which the original meaning of the motifs are derived. ideally, a next step would be to mine this information for the original context and meaning of the motifs. one main limitation of mime, of course, is that it requires manual identification and annotation of the motifs. recent work by @xcite demonstrated a preliminary motif detection system which perhaps could form the foundation for a scaling up of the mime to as-yet-unanalyzed motifs.