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The paper introduces a structured framework for claim-level discourse analysis in dense health narratives, addressing the limitations of existing topic- or sentiment-based representations. It identifies an average of 13.22 atomic claims per minute in social media health videos and proposes tuples that link each claim to thematic aspects, stance, and multidimensional pragmatic attributes. A benchmark of 1,191 manually annotated claims from 60 videos across four health domains is created, and experiments show that large language models perform well on thematic categorization and stance prediction but struggle with high-dimensional pragmatic profiling, indicating a need for task-specific inference strategies.
The paper introduces the Persuasion Index (PI), a taxonomy of 15 persuasion dimensions grounded in psychological and communication theories, implemented with 55 lexicon- and rule-based sub-features. PI is modular, allowing individual detectors to be swapped while preserving its theoretical framework. Evaluations on four English argumentative datasets show that PI provides a shared, lightweight feature space that captures meaningful predictive signals and reveals consistent dimension-level associations with persuasion outcomes, with variations across topics and stances.
Cancer-related discussions on social media provide an important space for information exchange and peer support, but also facilitate the spread of misinformation that may influence prevention, screening, and treatment decisions. Existing research on cancer misinformation often relies on narrow definitions, small-scale datasets, or binary labeling frameworks.
The paper investigates how to detect enthymemes—arguments with unstated premises or conclusions—in controversial political tweets about vaccination and immigration. Despite extensive fine‑tuning, models achieve an F1 score of only about 0.67, prompting the authors to propose new evaluation metrics that compare predictions to the full distribution of annotator votes and a model that directly predicts this distribution. The study also shows that models rely on explicit argumentative structure, often missing implicit links that readers must infer, raising concerns for content moderation systems that depend on majority labels.
arXiv:2609.36902v1 Announce Type: new Abstract: Existing multimodal fake news detection methods often introduce external information to assist detection. However, most of them rely on entity-level re...
Enthymemes, arguments with unstated premises or conclusions, are pervasive in persuasive discourse, yet their annotation remains notoriously subjective. We present a resource of 1,482 tweets from politically controversial discourse, annotated by five annotators for the presence of enthymemes and their argument structure, designed to study label variation.