Year: 2026
Type: conference-paper
Abstract: This article presents our study on task 10: Psycholinguistic conspiracy marker extraction and detection (Ghosh et al., 2026), which includes token-level extraction tasks and sentence-level conspiracy detection tasks.Focusing on conspiracy theory texts on social media, this paper proposes a classification method that combines semantic encoding with large language model reasoning and generation.Semantic features are extracted using DeBERTa-v3, and explanatory reasoning text is generated through ConspEmoLLM-v2.The two are then combined for classification, thereby enhancing the model's ability to recognize implicit conspiratorial logic.For the extraction subtask, this paper provides systematic comparison results of several mainstream pre-trained models, mainly conducting baseline model comparisons and performance analysis.
Author: Xintong Pan
Institution: University of Tübingen
Language: English
Subfield: Sociology and Political Science
Field: Social Sciences
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W7166816033