arXiv:2606.12186v1 Announce Type: new Abstract: 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. We first revisit the definition of enthymemes and propose annotation guidelines anchored in Walton's argumentation schemes, offering a structured and constrained approach that nonetheless preser
The proliferation of politically controversial discourse online, particularly within social media, necessitates better tools for analyzing and understanding arguments, which AI can now start to address.
Improving AI's ability to detect and analyze enthymemes can enhance automated content moderation, combat disinformation, and provide deeper insights into public opinion and persuasion tactics.
The availability of a new, structured dataset for enthymeme detection in political discourse refines the methodologies for training AI models in argumentative reasoning and persuasion analysis.
- · AI researchers in NLP and argumentation
- · Social media platforms
- · Political science researchers
- · Fact-checking organizations
- · Propagandists relying on implicit arguments
- · Algorithms lacking nuanced understanding of persuasion
AI models gain enhanced capability for identifying implicit arguments in text.
Improved AI moderation tools are developed to flag or contextualize biased or misleading political content based on implicit premises.
The public's media literacy and critical thinking skills may indirectly improve as AI tools provide clearer insights into persuasive tactics.
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