Digital Lighthouse Platform: Understanding the Misinformation Phenomenon on WhatsApp

• José Maria da Silva Monteiro Filho Universidade Federal do Ceará (UFC)
• Ivandro Claudino de Sá Universidade Federal do Ceará (UFC)
• Lucas Cabral Carneiro da Cunha Universidade Federal do Ceará (UFC)
• Helena Martins do Rego Barreto Universidade Federal do Ceará (UFC)
• Pedro Jorge Chaves Mourão Universidade Estadual do Ceará (UECE)

DOI: https://doi.org/10.5753/sbbd_estendido.2021.18178

Resumo

In the past few years, the large-scale dissemination of misinformation through social media has become a critical issue, harming the trustworthiness of legit information, social stability, democracy and public health. In many developing countries such as Brazil, India, and Mexico, one of the primary sources of misinformation is the messaging application WhatsApp. In February 2020, the Panorama Mobile Time/Opinion Box survey on mobile messaging in Brazil revealed that WhatsApp was installed on 99% of Brazilian smartphones. Among users of the application, 98% said they access it every day or almost every day. In this context, WhatsApp provides an important feature: the public groups. Many of these groups have been used to spread misinformation, especially as part of articulated political or ideological campaigns. Despite this scenario, due to WhatsApp's private messaging nature, few methods were explicitly developed to investigate the misinformation phenomenon on this platform. This tutorial provides an overview of recent developments in monitoring misinformation spreading, automatic misinformation detection, and identifying misinformation spreaders. In addition, we provide an overview of the leading open problems associated with the misinformation phenomenon and briefly examine some of the existing solutions. We hope that our tutorial can help researchers better understand Brazil's misinformation propagation and use data science methods to face this critical phenomenon.

Palavras-chave: Misinformation, WhatsApp, Data Science

Referências

Cabral, L., Monteiro, J. M., da Silva, J. W. F., Mattos, C. L. C., and Mourão, P. J. C. (2021). Fakewhastapp.br: NLP and machine learning techniques for misinformation detection in brazilian portuguese whatsapp messages. In Filipe, J., Smialek, M., Brodsky, A., and Hammoudi, S., editors, Proceedings of the 23rd International Conference on Enterprise Information Systems, ICEIS 2021, Online Streaming, April 26-28, 2021, Volume 1, pages 63–74. SCITEPRESS.

de Sá, I. C., Monteiro, J. M., da Silva, J. W. F., Medeiros, L. M., Mourão, P. J. C., and da Cunha, L. C. C. (2021). Digital lighthouse: A platform for monitoring public groups in whatsapp. In Filipe, J., Smialek, M., Brodsky, A., and Hammoudi, S., editors, Proceedings of the 23rd International Conference on Enterprise Information Systems, ICEIS 2021, Online Streaming, April 26-28, 2021, Volume 1, pages 297–304. SCITEPRESS.

Guo, B., Ding, Y., Yao, L., Liang, Y., and Yu, Z. (2019). The future of misinformation detection: New perspectives and trends. CoRR, abs/1909.03654.

Martins, A. D. F., da Cunha, L. C. C., Monteiro, J. M., and de Castro Machado, J. (2021a). Detection of misinformation about covid-19 in brazilian portuguese whatsapp messages. In Proceedings of the 26th International Conference on Applications of Natural Language to Information Systems, NLDB 2021. Springer.

Martins, A. D. F., da Cunha, L. C. C., Mourão, P. J. C., de Sá, I. C., Monteiro, J. M., and de Castro Machado, J. (2021b). Covid19.br: A dataset of misinformation about covid19 in brazilian portuguese whatsapp messages. In III Dataset Showcase Workshop, DSW 2021, Rio de Janeiro, RJ, Brazil, October 4-8, 2021. SBC.

Martins, A. D. F., da Cunha, L. C. C., Mourão, P. J. C., Monteiro, J. M., and de Castro Machado, J. (2021c). Detection of misinformation about covid-19 in brazilian portuguese whatsapp messages using deep learning. In XXXVI Simposio Brasileiro de Banco de Dados, SBBD 2021, Rio de Janeiro, RJ, Brazil, October 4-8, 2021. SBC.

Su, Q., Wan, M., Liu, X., and Huang, C.-R. (2020). Motivations, methods and metrics of misinformation detection: An nlp perspective. Natural Language Processing Research, 1:1–13.

Vosoughi, S., Roy, D., and Aral, S. (2018). The spread of true and false news online. Science, 359:1146–1151.