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Online discussions frequently involve conspiracy theories, which can contribute to the proliferation of belief in them. However, not all discussions surrounding conspiracy theories promote them, as some are intended to debunk them. Existing research has relied on simple proxies or focused on a constrained set of signals to identify conspiracy theories, which limits our understanding of conspiratorial discussions across different topics and online communities. This work establishes a general scheme for classifying discussions related to conspiracy theories based on authors' perspectives on the conspiracy belief, which can be expressed explicitly through narrative elements, such as the agent, action, or objective, or implicitly through references to known theories, such as chemtrails or the New World Order. We leverage human-labeled ground truth to train a BERT-based model for classifying online CTs, which we then compared to the Generative Pre-trained Transformer machine (GPT) for detecting online conspiratorial content. Despite GPT's known strengths in its expressiveness and contextual understanding, our study revealed significant flaws in its logical reasoning, while also demonstrating comparable strengths from our classifiers. We present the first large-scale classification study using posts from the most active conspiracy-related Reddit forums and find that only one-third of the posts are classified as positive. This research sheds light on the potential applications of large language models in tasks demanding nuanced contextual comprehension.
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Conspiracies Online: User Discussions in a Conspiracy Community Following Dramatic Events
Online communities play a crucial role in disseminating conspiracy theories. New theories often emerge in the aftermath of catastrophic events. Despite evidence of their widespread appeal, surprisingly little is known about who participates in these event-specific conspiratorial discussions or how do these discussions evolve over time. We study r/conspiracy, an active Reddit community of more than 200,000 users dedicated to conspiratorial discussions. By focusing on four tragic events and 10 years of discussions, we find three distinct user cohorts: joiners, who never participated in Reddit but joined r/conspiracy only after the event; converts who were active Reddit users but joined r/conspiracy only after the event; and veterans, who are longstanding r/conspiracy members. While joiners and converts have a shorter lifespan in the community in comparison to the veterans, joiners are more active during their shorter tenure, becoming increasingly engaged over time. Finally, to investigate how these events affect users’ conspiratorial discussions, we adopted a causal inference approach to analyze user comments around the time of the events. We find that discussions happening after the event exhibit signs of emotional shock, increased language complexity, and simultaneous expressions of certainty and doubtfulness. Our work provides insight on how online communities may detect new conspiracy theories that emerge ensuing dramatic events, and in the process stop them before they spread.
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- PAR ID:
- 10082962
- Date Published:
- Journal Name:
- Twelfth International AAAI Conference on Web and Social Media
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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