ConspEmoLLM: Conspiracy Theory Detection Using Emotion-Based Large Language Model

Core Concepts

ConspEmoLLM is an emotion-based large language model that outperforms other models in detecting conspiracy theories by leveraging affective features.

Abstract

Abstract: Misinformation, including conspiracy theories, poses a significant threat to society. ConspEmoLLM integrates affective information for diverse conspiracy theory detection tasks. Introduction: Rise of the internet and social media has facilitated rapid spread of misinformation. Conspiracy theories like those related to COVID-19 have increased during the pandemic. Methods: ConDID dataset facilitates instruction-tuning and evaluation of LLMs for conspiracy theory detection. Affective analysis reveals distinct sentiment and emotion features in conspiracy text. Experiments: Evaluation results show that ConspEmoLLM outperforms other models in F1 score for various tasks. Explicitly adding affective information reduces performance, while implicit use enhances it. Conclusion: ConspEmoLLM demonstrates state-of-the-art performance in detecting conspiracy theories using affective information. Future work includes expanding datasets and exploring alternative methods to incorporate affective information.

Stats

During the COVID-19 pandemic, there was a correlation between public anger level and rumor propagation. (Dong et al., 2020) Zaeem et al. (2020) observed a positive correlation between negative emotions and fake news dissemination.

Quotes

"Conspiracy theorists ignore scientific evidence and tend to interpret events as secretive actions." - Giachanou et al., 2023 "Affective information is crucial for detecting misinformation." - Liu et al., 2023

Key Insights Distilled From

ConspEmoLLM

by Zhiwei Liu,B... at arxiv.org 03-12-2024

https://arxiv.org/pdf/2403.06765.pdf

Deeper Inquiries

How can ConspEmoLLM be adapted to detect misinformation beyond conspiracy theories

ConspEmoLLM can be adapted to detect misinformation beyond conspiracy theories by expanding its training data to include a wider range of misinformation sources. By incorporating datasets that cover various types of false information, such as fake news, propaganda, and misleading claims, ConspEmoLLM can learn to identify common patterns in deceptive content. Additionally, the model's instruction-tuning process can be modified to focus on detecting general misinformation rather than specific conspiracy theories. This adaptation would involve creating new prompts that target broader categories of false information and adjusting the fine-tuning process accordingly.

What are the potential drawbacks of relying on affective features for misinformation detection

While leveraging affective features for misinformation detection can offer valuable insights into the emotional aspects of deceptive content, there are potential drawbacks to relying solely on these features. One drawback is the subjective nature of emotions and sentiments, which may vary across individuals and cultures. This subjectivity could lead to biases in the model's predictions if not carefully addressed during training. Moreover, emotional cues alone may not always provide conclusive evidence of misinformation; some misleading content may not exhibit strong emotional tones or could manipulate emotions strategically to deceive readers. Therefore, it is essential to combine affective analysis with other linguistic and contextual signals for more robust detection of misinformation.

How can emotional cues be effectively leveraged in unrelated contexts using large language models

To effectively leverage emotional cues in unrelated contexts using large language models (LLMs), several strategies can be implemented: Contextual Understanding: LLMs should be trained on diverse datasets covering a wide range of topics beyond conspiracy theories. By exposing the model to varied contexts, it can learn how emotions manifest differently across different subjects. Fine-Tuning Techniques: Implementing fine-tuning methods that emphasize emotion recognition in general text rather than specific domains like conspiracies will help LLMs adapt their understanding of emotions across different topics. Multi-Task Learning: Incorporating multi-task learning approaches where LLMs simultaneously perform emotion analysis tasks alongside unrelated text classification tasks can enhance their ability to recognize emotional nuances in various contexts. Data Augmentation: Including augmented data sets with emotionally rich but non-conspiracy-related texts will expose LLMs to a broader spectrum of emotional expressions outside typical conspiracy theory narratives. By implementing these strategies thoughtfully during training and fine-tuning processes, ConspEmoLLM or similar models can effectively leverage emotional cues for detecting misinformation across diverse content domains while maintaining accuracy and reliability in their assessments.