A Study of Rumor Detection based on Social Network Topic Models Relationship

  • Diogo Nolasco UFRJ
  • Jonice Oliveira UFRJ


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 scientific topic is a rumor. We propose the use of a topic model method on social and scientific domains and correlate the topics found to detect the most prone to be rumors. Results applied in the Zika epidemic scenario show evidence that the least correlated topics contain a mix of rumors and local community discussions.

Palavras-chave: Modelagem de Tópicos, Detecção de Rumores, Análise de Redes Sociais, Mineração de Texto


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NOLASCO, Diogo; OLIVEIRA, Jonice. A Study of Rumor Detection based on Social Network Topic Models Relationship. In: BRAZILIAN WORKSHOP ON SOCIAL NETWORK ANALYSIS AND MINING (BRASNAM), 9. , 2020, Cuiabá. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2020 . p. 166-177. ISSN 2595-6094. DOI: https://doi.org/10.5753/brasnam.2020.11172.

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