ConspEmoLLM: Conspiracy Theory Detection Using an Emotion-Based Large
  Language Model
Zhiwei Liu Boyang Liu Paul Thompson Kailai Yang Raghav Jain Sophia Ananiadou
The internet has brought both benefits and harms to society. A prime example
of the latter is misinformation, including conspiracy theories, which flood the
web. Recent advances in natural language processing, particularly the emergence
of large language models (LLMs), have improved the prospects of accurate
misinformation detection. However, most LLM-based approaches to conspiracy
theory detection focus only on binary classification and fail to account for
the important relationship between misinformation and affective features (i.e.,
sentiment and emotions). Driven by a comprehensive analysis of conspiracy text
that reveals its distinctive affective features, we propose ConspEmoLLM, the
first open-source LLM that integrates affective information and is able to
perform diverse tasks relating to conspiracy theories. These tasks include not
only conspiracy theory detection, but also classification of theory type and
detection of related discussion (e.g., opinions towards theories). ConspEmoLLM
is fine-tuned based on an emotion-oriented LLM using our novel ConDID dataset,
which includes five tasks to support LLM instruction tuning and evaluation. We
demonstrate that when applied to these tasks, ConspEmoLLM largely outperforms
several open-source general domain LLMs and ChatGPT, as well as an LLM that has
been fine-tuned using ConDID, but which does not use affective features. This
project will be released on https://github.com/lzw108/ConspEmoLLM/.