Pattern Identification of Bot Messages for Media Literacy

• Eric Ferreira dos Santos UFRJ
• Danilo Silva de Carvalho UFRJ
• Jonice Oliveira UFRJ

DOI: https://doi.org/10.5753/webmedia_estendido.2021.17607

Resumo

The massive use of online social media networks is a reality nowadays. Their increasing usage also raises growth in malicious activities in social media, one of which is the use of automated users (bots) that disseminate false information and can insert bias in analyses done on gathered social media data. Based on the concept of media literacy, this research presents a method to teach the human user to identify a pattern of a text produced by a bot, providing a tool (guide) to analyze social media text. Users who learned to identify a bot user with the guide had an average of 90% accuracy in the classification of new messages, against 57% of the participants who had no contact with the guide. The produced guide received a usefulness rating between 4 and 5 by the participants (scale from 1 to 5, with 5 being the highest value).

Palavras-chave: bot detection, message pattern, online social media, media literacy

Referências

2017. "Relembre casos de violência provocados por boatos na rede" in Portuguese. [link].

2018. Nigerian police say "fake news" on Facebook is killing people. [link].

Muhammad Al-Qurishi, Majed Alrubaian, Sk Md Mizanur Rahman, Atif Alamri, and Mohammad Mehedi Hassan. 2018. A prediction system of Sybil attack in social network using deep-regression model. Future Generation Computer Systems 87 (2018), 743–753.

Abdulrahman Alarifi , Mansour Alsaleh, and AbdulMalik Al-Salman. 2016. Twitter turing test: Identifying social machines. Information Sciences 372 (2016), 332–346.

Mansour Alsaleh, Abdulrahman Alarifi , Abdul Malik Al-Salman, Mohammed Alfayez, and Abdulmajeed Almuhaysin. 2014. Tsd: Detecting sybil accounts in twitter. In 2014 13th International Conference on Machine Learning and Applications. IEEE, 463–469.

Isaac David, Oscar S. Siordia, and Daniela Moctezuma. 2016. Features combination for the detection of malicious Twitter accounts. In 2016 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC). IEEE, 1–6.

Eric Ferreira Dos Santos, Danilo Carvalho, and Jonice Oliveira. 2021. Pattern Identification of Bot Messages for Media Literacy. In WebMedia ’21: Brazilian Symposium on Multimedia and Web Proceedings.

Eric Ferreira Dos Santos, Danilo Carvalho, Livia Ruback, and Jonice Oliveira. 2019. Uncovering Social Media Bots: a Transparency-focused Approach. In Companion Proceedings of The 2019 World Wide Web Conference. 545–552.

Zafar Gilani, Ekaterina Kochmar, and Jon Crowcroft. 2017. Classification of twitter accounts into automated agents and human users. In Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017. 489–496.

Rodrigo Augusto Igawa, Sylvio Barbon Jr, Kátia Cristina Silva Paulo, Guilherme Sakaji Kido, Rodrigo Capobianco Guido, Mario Lemes Proença Júnior, and Ivan Nunes da Silva. 2016. Account classification in online social networks with LBCA and wavelets. Information Sciences 332 (2016), 72–83. [

Mücahit Kantepe and Murat Can Ganiz. 2017. Preprocessing framework for Twitter bot detection. In 2017 Int. Conference on Computer Science and Engineering (UBMK). IEEE, 630–634.

Simon Kemp. 2019. Report: Social media use is increasing despite privacy fears. [link].

Bernardo Pereira Lauand and Jonice Oliveira. 2014. "Inferindo as Condições de Trânsito através da Análise de Sentimentos no Twitter" in Portuguese. iSys-Revista Brasileira de Sistemas de Informação 7, 3 (2014), 56–74.

Sonia Livingstone. 2004. Media literacy and the challenge of new information and communication technologies. The communication review 7, 1 (2004), 3–14.

Juan Martinez-Romo and Lourdes Araujo. 2013. Detecting malicious tweets in trending topics using a statistical analysis of language. Expert Systems with Applications 40, 8 (2013), 2992–3000.

Claudia Bauzer Medeiros. 2008. Grand research challenges in computer science in brazil. Computer 41, 6 (2008), 59–65.

Amanda Minnich, Nikan Chavoshi, Danai Koutra, and Abdullah Mueen. 2017. BotWalk: Effi cient adaptive exploration of Twitter bot networks.In Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017. 467–474.

Fred Morstatter, Liang Wu, Tahora H. Nazer, Kathleen M. Carley, and Huan Liu. 2016. A new approach to bot detection: Striking the balance between precision and recall. In Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016. IEEE, 533–540. https://doi.org/10.1109/ASONAM.2016.7752287

The Star Online. 2018. When fake news sparks violence: India grapples with online rumours. [link].

Mariam Orabi, Djedjiga Mouheb, Zaher Al Aghbari, and Ibrahim Kamel. 2020. Detection of Bots in Social Media: A Systematic Review. Information Processing & Management 57, 4 (2020), 102250. https://doi.org/10.1016/j.ipm.2020.102250

Marcos Pontes. 2020. PORTARIA Nº 1.122, DE 19 DE MARÇO DE 2020. [link].

Onur Varol, Emilio Ferrara, Clayton A Davis, Filippo Menczer, and Alessandro Flammini. 2017. Online human-bot interactions: Detection, estimation, and characterization. arXiv preprint arXiv:1703.03107 (2017).