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.
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.
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.
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.
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.
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