Fake news: a comprehensive and interdisciplinary survey
DOI:
https://doi.org/10.14571/brajets.v14.n3.502-520Abstract
Creating fake news for the purpose of manipulating someone or a group of people has historically not been new. However, in recent years, the creation of fake news, or known worldwide as fake news, has intensified and its spread has been increasingly accelerated. In addition, the level of complexity in detecting this fake news has also increased significantly with deepfakes. This has attracted the attention of the scientific community, showing that it is a very relevant research topic, given its impacts on global society. This work consists as a first paper in Portuguese, of an interdisciplinary literature review involving fake news and deepfakes, where concepts, their classifications, ways of propagation and the relationship between fake news and deepfakes in different areas of knowledge are presented. The results show that the definitive solution is far from being reached and there is still much to be done in search of an end or minimizing the impacts caused by fake news and deepfakes on society as a whole.References
Afchar, D., Nozick, V., Yamagishi, J., & Echizen, I. (2018, December). Mesonet: a compact facial video forgery detection network. In 2018 IEEE International Workshop on Information Forensics and Security (WIFS) (pp. 1-7). IEEE.
Ahmed, H., Traore, I., & Saad, S. (2017, October). Detection of online fake news using n-gram analysis and machine learning techniques. In International conference on intelligent, secure, and dependable systems in distributed and cloud environments (pp. 127-138). Springer, Cham.
Allcott, H., & Gentzkow, M. (2017). Social media and fake news in the 2016 election. Journal of economic perspectives, 31(2), 211-36.
Alpaydin, E. (2020). Introduction to machine learning. MIT press.
Aphiwongsophon, S., & Chongstitvatana, P. (2018, July). Detecting fake news with machine learning method. In 2018 15th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON) (pp. 528-531). IEEE.
Bakshy, E., Messing, S., & Adamic, L. A. (2015). Exposure to ideologically diverse news and opinion on Facebook. Science, 348(6239), 1130-1132.
Filho, C. de B., & Calabrez, P. (2017). Em Busca de Nós Mesmos. Porto Alegre: Citadel Grupo Editorial.
Basu, S. (1997). The conservatism principle and the asymmetric timeliness of earnings. Journal of accounting and economics, 24(1), 3-37.
Boehm, L. E. (1994). The validity effect: A search for mediating variables. Personality and Social Psychology Bulletin, 20(3), 285-293.
Castells, (2002). A sociedade em rede. São Paulo: Paz e Terra.
Castillo, S. I. V., de Oliveira Santos, D., & de Castro, H. C. D. O. (2020). Fake News no contexto da pandemia de COVID-19: considerações a partir da cultura política. Rizoma, 8(1), 165-184.
Colman, A. M. (2015). A dictionary of psychology. Oxford quick reference.
Costa, T., 2020. As 10 redes sociais mais usadas no Brasil [2019]. [online] Rock Content - BR. Available at: <https://rockcontent.com/br/blog/redes-sociais-mais-usadas-no-brasil/> [Accessed 26 November 2020].
De Carvalho, L. B. (2020). A democracia frustrada: fake news, política e liberdade de expressão nas redes sociais.
De Castro, J. C. L. (2017). A flexibilização da notícia na era dos algoritmos. In 40º Congresso Brasileiro de Ciências da Comunicação. Curitiba. Retrieved from https://portalintercom.org.br/anais/nacional2017/resumos/R12-2755-1.pdf
De Souza Vieira, L., De Aquino, S. D., & Lins, S. L. B. (2020, September). O que é notícia? Definições que emergem da audiência. In 18º ENCONTRO DA SBPJOR.
Dias, T., & Oliveira, L. D. (2019). O Monopólio da Verdade na Era das' Fake News'. Revista Ratio Juris, 14(28), 109-126.
Farkas, J., & Schou, J. (2018). Fake news as a floating signifier: Hegemony, antagonism and the politics of falsehood. Javnost-The Public, 25(3), 298-314.
Ferreira, R. R. (2018). Rede de mentiras: a propagação de fake news na pré-campanha presidencial brasileira. Observatorio (OBS*), 12(5).
Floridi, L. (2018). Artificial intelligence, deepfakes and a future of ectypes. Philosophy & Technology, 31(3), 317-321.
Gabielkov, M., Ramachandran, A., Chaintreau, A., & Legout, A. (2016, June). Social clicks: What and who gets read on Twitter?. In Proceedings of the 2016 ACM SIGMETRICS international conference on measurement and modeling of computer science (pp. 179-192).
Ge, C. H. E. N., Jiarong, X. I. E., Guangli, D. A. I., Zheng, P., Xiaqing, H. U., Hongpeng, L. U., ... & Xiaomin, C. H. E. N. (2020). Validity of the use of wrist and forehead temperatures in screening the general population for covid-19: A prospective real-world study. Iranian Journal of Public Health.
Grinberg, N., Joseph, K., Friedland, L., Swire-Thompson, B., & Lazer, D. (2019). Fake news on Twitter during the 2016 US presidential election. Science, 363(6425), 374-378.
Güera, D., & Delp, E. J. (2018, November). Deepfake video detection using recurrent neural networks. In 2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) (pp. 1-6). IEEE.
Guess, A., Nyhan, B., & Reifler, J. (2018). Selective exposure to misinformation: Evidence from the consumption of fake news during the 2016 US presidential campaign. European Research Council, 9(3), 4.
Hall, M. (2020, June). Application of the Benford’s law to Social bots and Information Operations activities. In 2020 International Conference on Cyber Situational Awareness, Data Analytics and Assessment (CyberSA) (pp. 1-8). IEEE.
Henry, N., Powell, A., & Flynn, A. (2018). AI can now create fake porn, making revenge porn even more complicated. The Conversation, 28.
Himma-Kadakas, M. (2017). Alternative facts and fake news entering journalistic content production cycle. Cosmopolitan Civil Societies: An Interdisciplinary Journal, 9(2), 25-40.
Huang, D., Zhu, Y., & Mustafaraj, E. (2019, September). How Dependable are" First Impressions" to Distinguish between Real and Fake NewsWebsites?. In Proceedings of the 30th ACM Conference on Hypertext and Social Media (pp. 201-210).
Jones, C. (2017). Bill would help California schools teach about ‘fake news,’ media literacy. Retrieved 30 March 2021, from https://edsource.org/2017/bill-would-help-california-schools-teach-about-fake-newsmedia-literacy/582363
Kahneman, D. (2011). Rápido e devagar: duas formas de pensar. Objetiva.
Kong, S. H., Tan, L. M., Gan, K. H., & Samsudin, N. H. (2020, April). Fake News Detection using Deep Learning. In 2020 IEEE 10th Symposium on Computer Applications & Industrial Electronics (ISCAIE) (pp. 102-107). IEEE.
Korshunov, P., & Marcel, S. (2018). Deepfakes: a new threat to face recognition? assessment and detection. arXiv preprint arXiv:1812.08685.
Kuran, T., & Sunstein, C. R. (1998). Availability cascades and risk regulation. Stan. L. Rev., 51, 683.
Lazer, D. M., Baum, M. A., Benkler, Y., Berinsky, A. J., Greenhill, K. M., Menczer, F., ... & Zittrain, J. L. (2018). The science of fake news. Science, 359(6380), 1094-1096.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521(7553), 436-444.
Ledig, C., Theis, L., Huszár, F., Caballero, J., Cunningham, A., Acosta, A., ... & Shi, W. (2017). Photo-realistic single image super-resolution using a generative adversarial network. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 4681-4690).
Leite, S., Lourenço, P., & Ferreira, M. (2018). O estado da arte do fenômeno fake news. In 1st Brazil-France-Francophone Belgium Journalism Research Conference. Retrieved from http://sbpjor.org.br/congresso/index.php/BFFB/sbpjor2018/paper/view/1492/793
Lelo, T. V. (2020). A influência do partidarismo na recepção de fake news e fact-checking em contexto de polarização política. Observatorio (OBS*), 14(3).
Liv, N., & Greenbaum, D. (2020). Deep fakes and memory malleability: false memories in the service of fake news. AJOB neuroscience, 11(2), 96-104.
Lutz, B., Adam, M. T., Feuerriegel, S., Pröllochs, N., & Neumann, D. (2020). Affective information processing of fake news: Evidence from NeuroIS. In Information Systems and Neuroscience (pp. 121-128). Springer, Cham.
Mackenzie, A., & Bhatt, I. (2020). Lies, bullshit and fake news: Some epistemological concerns. Postdigital Science and Education, 2(1), 9-13.
Manohar, S. (2020). Seeing is Deceiving: The Psychology and Neuroscience of Fake Faces.
Messias, J., Schmidt, L., Oliveira, R. A. R., & Souza, F. B. D. (2013). You followed my bot! Transforming robots into influential users in Twitter.
Murziqin, R., Tabrani, Z. A., Idris, S., Bustamam-Ahmad, K., Mendoza, P. J., Huda, M., ... & Qamariah, H. (2020). How to Get Your Research Published and Then Noticed.
Nascimento, C. E. G. (2020). Fake news, mentira organizada e educação: uma reflexão a partir do pensamento de Hannah Arendt. Revista Docência e Cibercultura, 4(1), 243-263.
Oeiras, T., Leite, C., & Castro, R. D. (2018). Os movimentos sociais na internet: a propagação e refutação de Fake News pós-Impeachment. Temática, 14(10). https://doi.org/10.22478/ufpb.1807-8931.2018v14n10.42308
Parikh, S. B., Patil, V., Makawana, R., & Atrey, P. K. (2019, March). Towards impact scoring of fake news. In 2019 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR) (pp. 529-533). IEEE.
Pariser, E. (2011). The filter bubble: What the Internet is hiding from you. Penguin UK.
Passamani, M. S. (2019). Critérios de classificação de fontes de fake news no brasil e no mundo. São Paulo: Fundação Getúlio Vargas. Retrieved from https://pesquisa-eaesp.fgv.br/publicacoes/pibic/criterios-de-classificacao-de-fontes-de-fake-news-no-brasil-e-no-mundo
Pennycook, G., & Rand, D. G. (2020). Who falls for fake news? The roles of bullshit receptivity, overclaiming, familiarity, and analytic thinking. Journal of personality, 88(2), 185-200.
Pennycook, G., Cannon, T. D., & Rand, D. G. (2018). Prior exposure increases perceived accuracy of fake news. Journal of experimental psychology: general, 147(12), 1865.
Pessoa, G. P., Botinha, R. M., & Costa, F. D. J. (2018). O Ensino Na Era Da Informação: Um Olhar a Partir Da Neurociência. Brazilian Journal of Education, Techbology and Society, BRAJETS (11: 4), 672-679.
Pierre, L. (1999). Cibercultura. São Paulo: Editora, 34, 264.
Pinto, T. M., & Zanetti, D. (2020). Desinformação e fake news: uma revisão de literatura. Anais do Seminário Comunicação e Territorialidades, 1(6).
Rebs, R. R., & Ernst, A. (2017). Haters e o discurso de ódio: entendendo a violência em sites de redes sociais., 6(2), 24-44.
Recuero, R., & Gruzd, A. (2019). Cascatas de Fake News Políticas: um estudo de caso no Twitter. Galáxia (São Paulo), (41), 31-47.
Ruchansky, N., Seo, S., & Liu, Y. (2017, November). Csi: A hybrid deep model for fake news detection. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management (pp. 797-806).
Safieddine, F., & Ibrahim, Y. (Eds.). (2020). Fake News in an Era of Social Media: Tracking Viral Contagion. Rowman & Littlefield Publishers.
Schudson, M. (2003). The sociology of news, 2003.
Shae, Z., & Tsai, J. (2019, July). AI blockchain platform for trusting news. In 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS) (pp. 1610-1619). IEEE.
Shang, W., Liu, M., Lin, W., & Jia, M. (2018, June). Tracing the source of news based on blockchain. In 2018 IEEE/ACIS 17th International Conference on Computer and Information Science (ICIS) (pp. 377-381). IEEE.
Shao, C., Ciampaglia, G. L., Varol, O., Flammini, A., & Menczer, F. (2017). The spread of fake news by social bots. arXiv preprint arXiv:1707.07592, 96, 104.
Shu, K., Sliva, A., Wang, S., Tang, J., & Liu, H. (2017). Fake news detection on social media: A data mining perspective. ACM SIGKDD explorations newsletter, 19(1), 22-36.
Silva, G. (2009). O fenômeno noticioso: objeto singular, natureza plural. Estudos em Jornalismo e Mídia, 6(2), 9-15.
Slovic, P., Finucane, M., Peters, E., & MacGregor, D. G. (2002). Rational actors or rational fools: Implications of the affect heuristic for behavioral economics. The Journal of Socio-Economics, 31(4), 329-342.
Souza, C. de F. de O. B. A. de, Quarto, L. C., Souza, A. H. de, Teixeira, F. L. F., Manhães, F. C., & Tiradentes, J. F. V. N. (2020). Um estudo bibliográfico sobre as fake news no âmbito da saúde. In Produção, Comunicação e Representação do Conhecimento e da Informação (pp. 139–145). Atena Editora. https://doi.org/10.22533/at.ed.14620130211
Subramanian, S. (2017). Inside the Macedonian fake-news complex. Wired magazine, 15.
Sunstein, C. R. (2001). Echo chambers: Bush v. Gore, impeachment, and beyond. Princeton, NJ: Princeton University Press.
Swan, M. (2015). Blockchain: Blueprint for a new economy. " O'Reilly Media, Inc.".
Sydell, L. (2016). We tracked down a fake-news creator in the suburbs. Here's what we learned. National Public Radio, 23.
Tam Cho, W. K., & Gaines, B. J. (2007). Breaking the (Benford) law: Statistical fraud detection in campaign finance. The american statistician, 61(3), 218-223.
Tandoc Jr, E. C., Lim, Z. W., & Ling, R. (2018). Defining “fake news” A typology of scholarly definitions. Digital journalism, 6(2), 137-153.
Tschiatschek, S., Singla, A., Gomez Rodriguez, M., Merchant, A., & Krause, A. (2018, April). Fake news detection in social networks via crowd signals. In Companion Proceedings of the The Web Conference 2018 (pp. 517-524).
Van der Linden, S., Panagopoulos, C., & Roozenbeek, J. (2020). You are fake news: political bias in perceptions of fake news. Media, Culture & Society, 42(3), 460-470.
Varol, O., Ferrara, E., Menczer, F., & Flammini, A. (2017). Early detection of promoted campaigns on social media. EPJ Data Science, 6, 1-19.
Wardle, C., & Derakhshan, H. (2017). Information disorder: Toward an interdisciplinary framework for research and policy making. Council of Europe report, 27, 1-107.
Wardle, C., & Derakhshan, H. (2018). Thinking about ‘information disorder’: formats of misinformation, disinformation, and mal-information. Ireton, Cherilyn; Posetti, Julie. Journalism,‘fake news’& disinformation. Paris: Unesco, 43-54.
Watts, C. (2017). Extremist content and russian disinformation online: Working with tech to find solutions. Statement prepared for the Senate Judiciary Committee, Subcommittee on Crime and Terrorism.
Zheng, Z., Xie, S., Dai, H. N., Chen, X., & Wang, H. (2018). Blockchain challenges and opportunities: A survey. International Journal of Web and Grid Services, 14(4), 352-375.
Zhou, X., & Zafarani, R. (2020). A survey of fake news: Fundamental theories, detection methods, and opportunities. ACM Computing Surveys (CSUR), 53(5), 1-40.