Topical Rumor Detection based on Social Network Topic Models Relationship
Keywords:Text Mining, Topic Modeling, Social Networks, Topic Labeling, Topic Correlation
The rumor detection problem on social networks has attracted considerable attention in recent years with the rise of concerns about fake news and disinformation. Most previous works focused on detecting rumors by individual messages, classifying whether a post or blog entry is considered a rumor or not. This paper proposes a method for rumor detection on topic-level that identifies whether a social topic related to a reference or authoritative topic is a rumor. We propose the use of a topic model method on social, scientific and political domains and correlate the topics found to detect the most prone to be rumors. Two scenarios were analyzed; the Zika epidemic scenario where our reference set of topics are scientific and the Brazilian presidential speeches where our reference set is extracted from the political speeches themselves. Results applied in the Zika epidemic scenario show evidence that the least correlated topics contain a mix of rumors and local community discussions. The Brazilian presidential speeches scenario suggests a strong correlation between rumor topics from both the speeches and the social domains.
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