Eric Ferreira Dos Santos
Livia Ruback
ABSTRACT
As the Online Social Networks (OSNs) presence continues to grow as a form of mass communication, tensions regarding their usage and perception by different social groups are reaching a turning point. The number of messages that are exchanged between users in these environments are vast and brought a trust problem, where it is difficult to know if the information is from a real person and if what was said is true. Automated users (bots) are part of this issue, as they may be used to spread false and/or harmful messages through an OSN while pretending to be a person. New attempts to automatically identify bots are in constant development, but so are the mechanisms to elude detection. We believe that teaching the user to identify a bot message is an important step in maintaining the credibility of content on social media. In this study, we developed an analysis tool, based on media literacy considerations, that helps the ordinary user to recognize a bot message using only textual features. Instead of simply classifying a user as a bot or human, this tool presents an interpretable reasoning path that helps to educate the user into recognizing suspicious activity. Experimental evaluation is conducted to test the tool’s primary effectiveness (classification) and results are presented. The secondary effectiveness (interpretability) is discussed in qualitative terms.
- Abdulrahman Alarifi, Mansour Alsaleh, and Abdul Malik Al-Salman. 2016. Twitter turing test: Identifying social machines. Information Sciences 372 (dec 2016), 332–346. Google Scholar
Digital Library
- Mansour Alsaleh, Abdulrahman Alarifi, Abdul Malik Al-Salman, Mohammed Alfayez, and Abdulmajeed Almuhaysin. 2014. TSD: Detecting sybil accounts in twitter. In Proceedings - 2014 13th International Conference on Machine Learning and Applications, ICMLA 2014. IEEE, 463–469. Google ScholarDigital Library
- S. Aslan. 2018. Twitter by the Numbers: Stats, Demographics & Fun Facts. https://www.omnicoreagency.com/twitter-statistics/Google Scholar
- Sajid Yousuf Bhat and Muhammad Abulaish. 2013. Community-based features for identifying spammers in online social networks. In Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining - ASONAM ’13. ACM Press, 100–107. Google ScholarDigital Library
- Aaron Black. 2018. A new study suggests fake news might have won Donald Trump the 2016 election. https://www.washingtonpost.com/news/the-fix/wp/2018/04/03/a-new-study-suggests-fake-news-might-have-won-donald-trump-the-2016-election/?noredirect=on&utm_term=.1029a721270fGoogle Scholar
- P. Cooper. 2019. 28 Twitter Statistics All Marketers Need to Know in 2019. https://blog.hootsuite.com/twitter-statistics/Google Scholar
- John P. Dickerson, Vadim Kagan, and V. S. Subrahmanian. 2014. Using sentiment to detect bots on Twitter: Are humans more opinionated than bots?. In ASONAM 2014 - Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. IEEE, 620–627. Google ScholarDigital Library
- Jennifer Fleming. 2014. Media literacy, news literacy, or news appreciation? A case study of the news literacy program at Stony Brook University. Journalism and Mass Communication Educator 69, 2 (jun 2014), 146–165.Google ScholarCross Ref
- C.J. Hutto and E.E. Gilbert. 2014. VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text.Eighth International Conference on Weblogs and Social Media (ICWSM-14) (jun 2014).Google Scholar
- Rodrigo Augusto Igawa, Sylvio Barbon, Katia Cristina Silva Paulo, Guilherme Sakaji Kido, Rodrigo Capobianco Guido, Mario Lemes Proenca Junior, and Ivan Nunes da Silva. 2016. Account classification in online social networks with LBCA and wavelets. Information Sciences 332 (mar 2016), 72–83. Google ScholarDigital Library
- Bernardo Pereira Lauand. 2016. Abordagem Experimental para a Localizacao e Detecao de Eventos em Tweets. Master’s thesis.Google Scholar
- Sonia Livingstone. 2004. Media literacy and the challenge of new information and communication technologies.Communication Review(2004).Google Scholar
- 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 (jun 2013), 2992–3000. Google ScholarDigital Library
- 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. Google ScholarDigital Library
- Art Silverblatt, Andrew Smith, Don Miller, Julie Smith, and Nikole Brown. 2014. Media Literacy: Keys to Interpreting Media Messages (4 ed.). 549 pages.Google Scholar
- J. Sommerland. 2018. Russian Hackers Used Tumblr To Spread ’fake News’ During Us Election, Company Reveals. https://www.independent.co.uk/life-style/gadgets-and-tech/news/tumblr-russian-hacking-us-presidential-election-fake-news-internet-research-agency-propaganda-bots-a8274321.htmlGoogle Scholar
- A. Sulleyman. 2017. TWITTER INTRODUCES 280 CHARACTERS TO ALL USERS. https://www.independent.co.uk/life-style/gadgets-and-tech/news/twitter-280-characters-tweets-start-when-get-latest-a8042716.htmlGoogle Scholar
- J. Swaine. 2018. Twitter admits far more Russian bots posted on election than it had disclosed. https://www.theguardian.com/technology/2018/jan/19/twitter-admits-far-more-russian-bots-posted-on-election-than-it-had-disclosedGoogle Scholar
Index Terms
- Uncovering Social Media Bots: a Transparency-focused Approach
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