mlsanityData to 7 Oct 2026

Subversive Characters and Stereotyping Readers: Characterizing Queer Relationalities with Dialogue-Based Relation Extraction

arXiv 2410.14978 ↗First posted 19 Oct 2024cs.CLcited by 2

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Abstract (arXiv)

Television is often seen as a site for subcultural identification and subversive fantasy, including in queer cultures. How might we measure subversion, or the degree to which the depiction of social relationship between a dyad (e.g. two characters who are colleagues) deviates from its typical representation on TV? To explore this question, we introduce the task of stereotypic relationship extraction. Built on cognitive stylistics, linguistic anthropology, and dialogue relation extraction, in this paper, we attempt to model the cognitive process of stereotyping TV characters in dialogic interactions. Given a dyad, we want to predict: what social relationship do the speakers exhibit through their words? Subversion is then characterized by the discrepancy between the distribution of the model's predictions and the ground truth labels. To demonstrate the usefulness of this task and gesture at a methodological intervention, we enclose four case studies to characterize the representation of queer relationalities in the Big Bang Theory, Frasier, and Gilmore Girls, as we explore the suspicious and reparative modes of reading with our computational methods.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 10 of its 46 references that are in the map. First posted on arXiv 19 Oct 2024.

Builds on

10 of 46 references.

References · 10

  1. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  2. 2020Longformer: The Long-Document Transformercited by 3,362
  3. 2018Techniques for Interpretable Machine Learningcited by 237
  4. 2021SummScreen: A Dataset for Abstractive Screenplay Summarizationcited by 80
  5. 2020Dialogue-Based Relation Extractioncited by 68
  6. 2017A Domain Based Approach to Social Relation Recognitioncited by 18
  7. 2022TVShowGuess: Character Comprehension in Stories as Speaker Guessingcited by 17
  8. 2023Hierarchical Dialogue Understanding with Special Tokens and Turn-level Attentioncited by 6
  9. 2022Unsupervised Learning of Hierarchical Conversation Structurecited by 5
  10. 2024Empirical Analysis of Dialogue Relation Extraction with Large Language Modelscited by 4

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.