Abstract
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.
Highlights
The traffic and discussions generated on social networks have been increasing with their development over time
The proposal presented in this work is based on topic modeling techniques that are used to do rumor detection at a topic level, i. e., detecting rumor topics
The scenario covered by these datasets is relative to the context of the Zika epidemic from 2015 to 2016, which contains a variety of topics like reports, propagation to various countries, associated diseases, and influence on the 2016 Olympic Games organization
Summary
The traffic and discussions generated on social networks have been increasing with their development over time. The means of automatically detecting the information's credibility and monitor public subjects has been getting increased attention. The proposal presented in this work is based on topic modeling techniques that are used to do rumor detection at a topic level, i. An overview of rumors and topic models are presented . Rumor There are various definitions of rumor in different areas. There is the view that a rumor is a story or statement in general circulation without confirmation or certainty to facts [DiFonzo and Bordia 2007]. The existence of different definitions makes it hard to compare the effectiveness of different methods for rumor detection. There are some typical definitions usually found in the literature (as in Figure 1) of this research area[Cao et al 2018]:
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