  Conspiracy theories, as a type of misinformation, are narratives that
explains an event or situation in an irrational or malicious manner. While most
previous work examined conspiracy theory in social media short texts, limited
attention was put on such misinformation in long news documents. In this paper,
we aim to identify whether a news article contains conspiracy theories. We
observe that a conspiracy story can be made up by mixing uncorrelated events
together, or by presenting an unusual distribution of relations between events.
Achieving a contextualized understanding of events in a story is essential for
detecting conspiracy theories. Thus, we propose to incorporate an event
relation graph for each article, in which events are nodes, and four common
types of event relations, coreference, temporal, causal, and subevent
relations, are considered as edges. Then, we integrate the event relation graph
into conspiracy theory identification in two ways: an event-aware language
model is developed to augment the basic language model with the knowledge of
events and event relations via soft labels; further, a heterogeneous graph
attention network is designed to derive a graph embedding based on hard labels.
Experiments on a large benchmark dataset show that our approach based on event
relation graph improves both precision and recall of conspiracy theory
identification, and generalizes well for new unseen media sources.
