Motifs are distinctive, recurring, widely used idiom-like words or phrases, often originating from folklore, whose meaning are anchored in a narrative. Motifs have significance as communicative devices because they concisely imply a constellation of culturally relevant information. Their broad usage suggests their cognitive importance as touchstones of cultural knowledge. We present GOLEM, the first dataset annotated for motific information. The dataset comprises 7,955 English articles (2,039,424 words). The corpus identifies 26,078 motif candidates across 34 motif types from three cultural or national groups: Jewish, Irish, and Puerto Rican. Each motif candidate is labeled with the type of usage (Motific, Referential, Eponymic, or Unrelated), resulting in 1,723 actual motific instances. Annotation was performed by individuals identifying as members of each group and achieved a Fleiss’ kappa of >0.55. We demonstrate that classification of candidate type is a challenging task for LLMs using a few-shot approach; recent models such as T5, FLAN-T5, GPT-2, and Llama 2 (7B) achieved a performance of 41% accuracy at best. These data will support development of new models and approaches for detecting (and reasoning about) motific information in text. We release the corpus, the annotation guide, and the code to support other researchers building on this work.

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.