  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.
