FakeSpread: A framework to analyse the propagation of fake news on the web
Abstract
The advancement of social media as a communication tool drives the dissemination of news, making it faster, scalable and consequently making it difficult to assess the veracity of information, making fake news a true phenomenon with increasingly serious developments in the political, social and human context. Identifying the source of fake news and understanding the paths that make it popular is an important strategy to combat the fake news industry. The objective of this work is to analyze the spread of fake news on the Web, using graph theory metrics, considering its network structure. We present as a case study an analysis of the fake news propagation by websites listed by the CPMI of fake news, proposed by the Brazilian Congress.
Keywords:
Fake news, disinformation, graph theory
References
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Conroy, N. J., Rubin, V. L., & Chen, Y. (2015). Automatic deception detection: Methods for finding fake news. Proceedings of the Association for Information Science and Technology, 52, 1-4.
Duong, C. T., Nguyen, Q. V. H., Wang, S., & Stantic, B. (2017). Provenance-Based Rumor Detection. Databases Theory and Applications, 125–137.
Gdi, Global Disinformation Index (2019). The Quarter Billion Dollar Question: How is Disinformation Gaming Ad Tech?
Hu, Y. (2005). Efficient, high-quality force-directed graph drawing. Mathematica Journal, 10(1):37–71.
Kleinberg, J., Easley, D. Networks, Crowds, and Markets: Reasoning about a Highly Cambridge University Press, 2010.
Newman, M. J. Networks: An Introduction. [S.l.]: Oxford University Press, 2010.
Ratkiewicz, J., Conover, M., Meiss, M., Gonçalves, B., Patil, S., Flammini, A., & Menczer, F. (2011). Truthy: Mapping the spread of astroturf in microblog streams. Proceedings of the 20th International Conference Companion on World Wide Web (WWW ’11), 249–252.
Ribeiro, A. G. (2014). Mulher morta após boato em rede social é enterrada em Guarujá, SP. Disponível em
Sanchotene, C., Machado da Silveira, A. C., & de Lima Lavarda, S. (2017). Quando as notícias mais compartilhadas são falsas: a circulação de boatos durante a semana do impeachment no Facebook. Comunicação & Informação, 20(3), 99-112.
Shalders, A. (2020). O que se sabe sobre o inquérito que levou às buscas e quebras de sigilo contra apoiadores de Bolsonaro. Disponível em https://bbc.in/30WZYzC>. Acesso: 10.ago.2020.
Shao, C., Ciampaglia, G. L., Flammini, A., & Menczer, F.(2016). Hoaxy: A Platform for Tracking Online Misinformation.
Shu, K., Bernard, H. R., & Liu, H. (2018). Studying Fake News via Network Analysis: Detection and Mitigation. Emerging Research Challenges and Opportunities in Computational Social Network Analysis and Mining, 43–65.
Siqueira, A. (2020). Sleeping Giants Brasil: “Governo dissemina ódio com dinheiro público” Disponível em
Tardaguila, C., Mares, C. (2018). Dez notícias falsas com 865 mil ompartilhamentos: o lixo digital do 1º turno. Disponível em
Published
2020-10-26
How to Cite
CORDEIRO, Anderson ; SAMPAIO, Jonice de Oliveira ; RUBACK, Lívia.
FakeSpread: A framework to analyse the propagation of fake news on the web. In: WORKSHOP ON ASPECTS OF HUMAN-COMPUTER INTERACTION FOR THE SOCIAL WEB (WAIHCWS), 11. , 2020, Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 9-16.
ISSN 2596-0296.
DOI: https://doi.org/10.5753/waihcws.2020.12342.
