A Study of Rumor Detection based on Social Network Topic Models Relationship
Resumo
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
Referências
Ahsan, M. and Kumari, M. (2019). Rumors and their controlling mechanisms in online social networks: A survey. Online Social Networks and Media
Al-Khalifa, H. S. and Al-Eidan, R. M. (2011). An experimental system for measuring the credibility of news content in Twitter. International Journal of Web Information Systems, v. 7, n. 2, p. 130–151.
Allport, G. and Postman, L. (1947). The psychology of rumor.
Blei, D., Carin, L. and Dunson, D. (2010). Probabilistic topic models. IEEE Signal Processing Magazine, v. 27, p. 55–65.
Blei, D. and Lafferty, J. (2009). Topic models. : classification, clustering, and applications,
Blei, D. M., Ng, A. Y. and Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, v. 3, n. 4–5, p. 993–1022.
Cao, J., Guo, J., Li, X., et al. (10 jul 2018). Automatic Rumor Detection on Microblogs: A Survey.
Castillo, C., Mendoza, M. and Poblete, B. (2011). Information credibility on Twitter. In Proceedings of the 20th International Conference Companion on World Wide Web, WWW 2011.
DiFonzo, N. and Bordia, P. (2007). Rumor psychology: Social and organizational approaches.
Fundação Oswaldo Cruz., R. and Araujo, I. S. (2007). A mídia em meio às ‘emergências’ do vírus Zika: questões para o campo da comunicação e saúde. Fundação Oswaldo Cruz. v. 10
Kullback, S. and Leibler, R. A. (1951). On Information and Sufficiency. The Annals of Mathematical Statistics, v. 22, n. 1, p. 79–86.
Kwon, S., Cha, M., Jung, K., Chen, W. and Wang, Y. (2013). Prominent features of rumor propagation in online social media. In Proceedings - IEEE International Conference on Data Mining, ICDM.
Mendoza, M., Poblete, B. and Castillo, C. (2010). Twitter under crisis: Can we trust what we RT? In SOMA 2010 - Proceedings of the 1st Workshop on Social Media Analytics.
Nolasco, D. and Oliveira, J. (2016). Detecting knowledge innovation through automatic topic labeling on scholar data. In Proceedings of the Annual Hawaii International Conference on System Sciences.
Nolasco, D. and Oliveira, J. (2018). Subevents detection through topic modeling in social media posts. Future Generation Computer Systems,
Nolasco, D. and Oliveira, J. (2020). Mining social influence in science and vice-versa: A topic correlation approach. International Journal of Information Management, v. 51.
Peterson, W. A. and Gist, N. P. (sep 1951). Rumor and Public Opinion. American Journal of Sociology, v. 57, n. 2, p. 159–167.
Ratkiewicz, J., Meiss, M., Conover, M., et al. (2011). Detecting and Tracking Political Abuse in Social Media. In Proceedings of the Fifth International AAAI Conference on Weblogs and Social Media.
Sharma, K., Qian, F., Jiang, H., et al. (2019). Combating fake news: A survey on identification and mitigation techniques. ACM Transactions on Intelligent Systems and Technology
Steyvers, M. and Griffiths, T. (2007). Probabilistic topic models. Handbook of latent semantic analysis,
Takahashi, B., Tandoc, E. C. and Carmichael, C. (2015). Communicating on Twitter during a disaster: An analysis of tweets during Typhoon Haiyan in the Philippines. Computers in Human Behavior, v. 50, n. 2015, p. 392–398.
WHO (2016). WHO - Dispelling rumours around Zika and complications. http://www.who.int/emergencies/zika-virus/articles/rumours/en/, [accessed on Apr 29].
Zubiaga, A., Aker, A., Bontcheva, K., Liakata, M. and Procter, R. (1 feb 2018). Detection and resolution of rumours in social media: A survey. ACM Computing Surveys. Association for Computing Machinery.
Al-Khalifa, H. S. and Al-Eidan, R. M. (2011). An experimental system for measuring the credibility of news content in Twitter. International Journal of Web Information Systems, v. 7, n. 2, p. 130–151.
Allport, G. and Postman, L. (1947). The psychology of rumor.
Blei, D., Carin, L. and Dunson, D. (2010). Probabilistic topic models. IEEE Signal Processing Magazine, v. 27, p. 55–65.
Blei, D. and Lafferty, J. (2009). Topic models. : classification, clustering, and applications,
Blei, D. M., Ng, A. Y. and Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, v. 3, n. 4–5, p. 993–1022.
Cao, J., Guo, J., Li, X., et al. (10 jul 2018). Automatic Rumor Detection on Microblogs: A Survey.
Castillo, C., Mendoza, M. and Poblete, B. (2011). Information credibility on Twitter. In Proceedings of the 20th International Conference Companion on World Wide Web, WWW 2011.
DiFonzo, N. and Bordia, P. (2007). Rumor psychology: Social and organizational approaches.
Fundação Oswaldo Cruz., R. and Araujo, I. S. (2007). A mídia em meio às ‘emergências’ do vírus Zika: questões para o campo da comunicação e saúde. Fundação Oswaldo Cruz. v. 10
Kullback, S. and Leibler, R. A. (1951). On Information and Sufficiency. The Annals of Mathematical Statistics, v. 22, n. 1, p. 79–86.
Kwon, S., Cha, M., Jung, K., Chen, W. and Wang, Y. (2013). Prominent features of rumor propagation in online social media. In Proceedings - IEEE International Conference on Data Mining, ICDM.
Mendoza, M., Poblete, B. and Castillo, C. (2010). Twitter under crisis: Can we trust what we RT? In SOMA 2010 - Proceedings of the 1st Workshop on Social Media Analytics.
Nolasco, D. and Oliveira, J. (2016). Detecting knowledge innovation through automatic topic labeling on scholar data. In Proceedings of the Annual Hawaii International Conference on System Sciences.
Nolasco, D. and Oliveira, J. (2018). Subevents detection through topic modeling in social media posts. Future Generation Computer Systems,
Nolasco, D. and Oliveira, J. (2020). Mining social influence in science and vice-versa: A topic correlation approach. International Journal of Information Management, v. 51.
Peterson, W. A. and Gist, N. P. (sep 1951). Rumor and Public Opinion. American Journal of Sociology, v. 57, n. 2, p. 159–167.
Ratkiewicz, J., Meiss, M., Conover, M., et al. (2011). Detecting and Tracking Political Abuse in Social Media. In Proceedings of the Fifth International AAAI Conference on Weblogs and Social Media.
Sharma, K., Qian, F., Jiang, H., et al. (2019). Combating fake news: A survey on identification and mitigation techniques. ACM Transactions on Intelligent Systems and Technology
Steyvers, M. and Griffiths, T. (2007). Probabilistic topic models. Handbook of latent semantic analysis,
Takahashi, B., Tandoc, E. C. and Carmichael, C. (2015). Communicating on Twitter during a disaster: An analysis of tweets during Typhoon Haiyan in the Philippines. Computers in Human Behavior, v. 50, n. 2015, p. 392–398.
WHO (2016). WHO - Dispelling rumours around Zika and complications. http://www.who.int/emergencies/zika-virus/articles/rumours/en/, [accessed on Apr 29].
Zubiaga, A., Aker, A., Bontcheva, K., Liakata, M. and Procter, R. (1 feb 2018). Detection and resolution of rumours in social media: A survey. ACM Computing Surveys. Association for Computing Machinery.
Publicado
30/06/2020
Como Citar
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.
