Identifying Conspiracy Theories News based on Event Relation Graph (2023)

Abstract: 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.

## introduction conspiracy theories are narratives that attempt to explain the significant social or political events as being secretly plotted by malicious groups at the expense of an unwitting populations @xcite . a variety of conspiracy theories ranging from science-related moon landing to political-related pizzagate @xcite are widespread throughout the world @xcite . it was estimated that more than half of the us population believed in at least one conspiracy theory @xcite . the widespread presence of conspiracy theories can cause harm to both individuals and society as a whole @xcite , such as reducing science acceptance, introducing polarization, driving violence, obstructing justice, and bringing public health risks @xcite etc. thus, developing novel models to detect conspiracy theories becomes important and necessary. most previous work studies conspiracy theories primarily from psychology or communication perspectives, often concentrating on social media short texts @xcite . few work focused on developing computational models to detect conspiracy theories, especially for lengthy news articles. however, a substantial portion of conspiracy theories originate from long documents on news websites @xcite , and then disseminate virally through various social media channels @xcite . therefore, we focus on identifying conspiracy theories within these long documents from news websites, and aim to comprehend the underlying narratives. this is a challenging task which not only requires semantic understanding @xcite but also necessitates logical reasoning @xcite . one observation we have for identifying conspiracy theories is that a conspiracy story can be made up by mixing unrelated events together. the concept of "event" refers to a specific occurrence or action that happens around us, and is essential in story telling and narrative understanding @xcite . conspiracy theories, however, usually put typically unrelated events together to fabricate a false story. take the article in figure 1 as an example, the author includes both "murder" event figure 1 : an example of conspiracy theory news article, and its corresponding event relation graph. events are shown in bold text. event relation graph consists of events as nodes, and four types of event-event relations as links. a conspiracy story can be made up by mixing unrelated events (nodes) together, or by presenting an irrational conspiracy logic between events (event relations). and "treatment on ventilators" event in the article, trying to persuade the readers that treatment on ventilators is equivalent to murdering, which is questionable and immediately against our intuition. to effectively guide the model to recognize the irrationality of mixing unrelated events like "murder" and "treatment on ventilators" together, maybe by drawing upon rich world knowledge already encoded in a underlying language model, we propose to encode events information for conspiracy theories identification. furthermore, we observe that conspiracy theories often rely on presenting relations between events in an unusual way. revisiting the example in figure 1 , the author intends to convince readers through an irrational logical chain of events: people were infected with covid-19 and required hospitalization, but died while being treated on ventilators, therefore it is the ventilator treatment that led to their death. however, the normal rational logic is that people's death just co-occurred with treatment at the same time, it was not really the treatment that caused these deaths. the conspiracy theories here presents an invalidate and baseless causal relation by emphasizing the co-occurring temporal relation. to sensitize the model to unusual and illogical event-event relations, we propose to equip the model with event relations knowledge. in particular, we incorporate four common types of event relations that are crucial for narrative understanding and logical reasoning @xcite : coreference -whether two event mentions designate the same occurrence, temporal -chronological orders (before, after or overlap) between events, causalcausality between events, and subevent -containment from a parent event to a child event. a statistical analysis of events involving the four types of event relations is shown in our contributions are summarized as follows: event and event relations have been studied for decades. an event refers to an action or occurrence in the real world @xcite . the four types of event relations we consider, coreference, temporal, causal and subevent relations, are all commonly seen event-event relations in stories. however, the four types of event relations were previously studied in isolation @xcite @xcite . instead, we consider all the four types of event relations to depict event-level discourse structures and achieve contextualized understanding of events in a narrative. our approach is enabled by the recently introduced large dataset maven-ere @xcite that has the four event relations annotated within individual articles. ## methodology in this section, we explain the details of event relation graph construction for each article. the event relation graph is incorporated within two steps. firstly, an event-aware language model is developed based on the soft labels, with events and event relations knowledge augmented. secondly, a heterogeneous graph neural network is designed to derive a graph embedding based on the hard labels. figure 2 illustrates our proposed methodology. ## conclusion this paper presents the first attempt to identify conspiracy theories in news articles. following our observation that conspiracy theories can be fabricated by mixing uncorrelated events together or presenting an unusual distribution of relations between events, we construct an event relation graph for each article and incorporate the graph structure for conspiracy theories identification. experimental re-sults demonstrate the effectiveness of our approach. future work needs to develop more sophisticated methods to improve event relation graph construction and better extract implicit event relations.