TrendsBot: Verificando a veracidade das mensagens do Telegram utilizando Data Stream
Abstract
Fake news is created and shared in large scale through applications such as WhatsApp and Telegram. The massive sharing of such news on these applications is caused by the impact of the headline, along with the ease of sharing and also by the lack of a verification method. Therefore, it is evident the need for a tool that facilitates the judgment of the news integrity. This work proposes TrendsBot: a Telegram bot capable of collecting evidence through Twitter and news portals, supporting users in a quick check of that news. The bot queries keywords through transcribed audios or texts sent to a group, collects evidence related to those words, and relates them to the terms of the news.
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Buntain, C. and Golbeck, J. (2017). Automatically Identifying Fake News in Popular Twitter Threads. Proceedings - 2nd IEEE International Conference on Smart Cloud, SmartCloud 2017, pages 208–215.
Conroy, N. J., Rubin, V. L., and Chen, Y. (2015). Automatic deception detection: Methods for finding fake news. ASIST ’15, pages 82:1–82:4, Silver Springs, MD, USA. American Society for Information Science.
Della Vedova, M. L., Tacchini, E., Moret, S., Ballarin, G., DiPierro, M., and de Alfaro, L.(2018). Automatic online fake news detection combining content and social signals.In2018 22nd Conference of Open Innovations Association (FRUCT), pages 272–279.
Dey, A., Rafi, R. Z., Parash, S. H., Arko, S. K., and Chakrabarty, A. (2018). Fake News Pattern Recognition using Linguistic Analysis. pages 305–309.
Eslahi, M., Salleh, R., and Anuar, N. B. (2013). Bots and botnets: An overview of characteristics, detection and challenges. Proceedings - 2012 IEEE International Conferenceon Control System, Computing and Engineering, ICCSCE 2012, pages 349–354.
Granik, M. and Mesyura, V. (2017). Fake news detection using naive bayes classifier. In2017 IEEE First Ukraine Conference on Electrical and Computer Engineering (UKR-CON), pages 900–903. IEEE.
Hassan, N., Zhang, G., Arslan, F., Caraballo, J., Jimenez, D., Gawsane, S., Hasan, S.,Joseph, M., Kulkarni, A., Nayak, A. K., Sable, V., Li, C., and Tremayne, M. (2017). Claimbuster: The first-ever end-to-end fact-checking system.Proc. VLDB Endow., (12):1945–1948.
Long, Y., Lu, Q., Xiang, R., Li, M., and Huang, C.-R. (2017). Fake news detection through multi-perspective speaker profiles. pages 252–256.
Rubin, V. L., Chen, Y., and Conroy, N. J. (2015). Deception detection for news: Three types of fakes. ASIST ’15, pages 83:1–83:4, Silver Springs, MD, USA. American Society for Information Science.
Sadikoglu, S. and Oktay, S. (2018). Identifying Fake News and Fake Users on Twitter. Procedia Computer Science, 120:204–212.
Sample, C., Columbia, L. L. C., and Indianapolis, I. (2019). A Model for Evaluating Fake News. (February).Trends, I. T. (2017). The Economics of “Fake News”. (December).
Wang, P., Angarita, R., and Renna, I. (2018). Is this the Era of Misinformation yet?Combining Social Bots and Fake News to Deceive the Masses. In The 2018 WebConference Companion, Lyon, France.
Zhang, X. and Ghorbani, A. A. (2019). An overview of online fake news: Characterization, detection, and discussion. Information Processing & Management, 56(March):1–26.
Published
2019-05-06
How to Cite
SANTOS, Wellison R. M.; XAVIER, Marcus R.; DA CUNHA, David C. P.; M. JÚNIOR, José C. F.; ADAUTO, Daniel A. R.; FERRAZ, Carlos A. G..
TrendsBot: Verificando a veracidade das mensagens do Telegram utilizando Data Stream. In: DEMO SESSION - BRAZILIAN SYMPOSIUM ON COMPUTER NETWORKS AND DISTRIBUTED SYSTEMS (SBRC), 2. , 2019, Gramado.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 65-72.
ISSN 2177-9384.
DOI: https://doi.org/10.5753/sbrc_estendido.2019.7771.
