Conspiracy Detection Beyond Text: Exploring the Feasibility of Adding Psycho-Linguistic Features to Enhance Conspiracy Detection Models

Conspiracy theories pose a significant societal challenge, particularly online where their spread can be hard to detect. Robust detection models are crucial for effectively identifying these theories. In this study, we investigate incorporating emotional sentiment and moral framing features into a text-based conspiracy detection model. We hypothesize that incorporating these psycho-linguistic elements would enhance the model’s performance. Our results reveal significant psycho-linguistic differences between conspiracy and non-conspiracy texts. Conspiracy texts contain higher levels of anger and are framed through the moral lens of cheating, while non-conspiracy texts contain higher levels of joy and are framed through the moral lenses of care and harm. Our model’s ability to classify conspiratorial text improves after integrating emotional sentiment and moral framing into the text-based conspiracy detection model. This work demonstrates the potential value of incorporating psycho-linguistic features into text-based models to enhance conspiracy theory detection.