Automated Detection of Conspiracy Theories in German-Language Telegram Messages Using Large Language Models
Large language models can effectively detect conspiracy theories in German-language Telegram messages, with supervised fine-tuning and prompt-based approaches achieving comparable performance.
Classifying Conspiracy Theories in Online Discussions: Identifying Established and Emerging Narratives at Scale
Online discussions frequently involve conspiracy theories, which can contribute to the proliferation of belief in them. This work establishes a general scheme for classifying discussions related to conspiracy theories based on authors' perspectives, which can be expressed explicitly through narrative elements or implicitly through references to known theories.
ConspEmoLLM: Conspiracy Theory Detection Using Emotion-Based Large Language Model
ConspEmoLLM is an emotion-based large language model designed for accurate conspiracy theory detection by integrating affective information.
ConspEmoLLM: Conspiracy Theory Detection Using Emotion-Based Large Language Model
ConspEmoLLM is an emotion-based large language model that outperforms other models in detecting conspiracy theories by leveraging affective features.
Conspiracy Theory Detection Using Emotion-Based Large Language Model
The author proposes ConspEmoLLM, an emotion-based LLM for detecting conspiracy theories, outperforming other models by leveraging affective features.
