Combating Fake News on Social Media by Implicit Crowd Signals

• Paulo Freire Instituto Militar de Engenharia
• Ronaldo Goldschmidt Instituto Militar de Engenharia

DOI: https://doi.org/10.5753/eniac.2019.9303

Abstract

Fake News dissemination is an acknowledged problem on social media. One of the main approaches to automatically detect this type of news is based on reputation, especially the one use crowd signals. Although promising, this approach depends on information that is not always available: the explicit opinion of the users about the news concerning whether they are fake or not. To overcome this drawback, this article proposes a crowd signal-based method that does not demand the users' explicit opinion. Experiments provided evidence that the proposed method can detect Fake News without demanding the explicit opinion of the users and without compromising the results obtained by the state-of-the-art crowd signal-based method.

Keywords: Fake news, social media, crowd signals

References

Bhatt, G., Sharma, A., Sharma, S., Nagpal, A., Raman, B., and Mittal, A. (2018). Combining neural, statistical and external features for fake news stance identification. In Comp. Proc. of the The Web Con. Int WWW Con Steering Committee.

Buntain, C. and Golbeck, J. (2017). Automatically identifying fake news in popular twitter threads. In IEEE Int Con on Smart Cloud.

Chia, P. H. and Knapskog, S. J. (2012). Re-evaluating the wisdom of crowds in assessing web security. In The 15th Int Con on Financial Cryptography and Data Security, Berlin, Heidelberg. Springer-Verlag.

Farajtabar, M., Yang, J., Ye, X., Xu, H., Trivedi, R., Khalil, E., Li, S., Song, L., and Zha, H. (2017). Fake news mitigation via point process based intervention. In Int Con on Machine Learning.

Freeman, D. M. (2017). Can you spot the fakes?: On the limitations of user feedback in online social networks. In Int Con on WWW.

Gilda, S. (2017). Evaluating machine learning algorithms for fake news detection. In IEEE Student Con on Research and Development.

Janze, C. and Risius, M. (2017). Automatic detection of fake news on social media platforms. In PACIS.

Kim, J., Tabibian, B., Oh, A., Schölkopf, B., and Gomez-Rodriguez, M. (2018). Leverging the crowd to detect and reduce the spread of fake news and misinformation. In ACM Int Con on Web Search and Data Mining.

Liu, Y. and BrookWu, Y. (2018). Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In AAAI Con on Artificial Intelligence.

Moore, T. and Clayton, R. (2008). Financial cryptography and data security. chapter Evaluating the Wisdom of Crowds in Assessing Phishing Websites.

Nasim, M., Nguyen, A., Lothian, N., Cope, R., and Mitchell, L. (2018). Real-time detection of content polluters in partially observable twitter networks. In Comp. Proc. of the The Web Con.

Pérez-Rosas, V., B. Kleinberg, A. L., and Mihalcea, R. (2018). Automatic detection of fake news. In Int Con on Comput. Linguistics.

Qian, F., Gong, C., Sharma, K., and Liu, Y. (2018). Neural user response generator: Fake news detection with collective user intelligence. In Int Joint Con on Artificial Intelligence.

Rubin, V. L., Conroy, N. J., and Chen, Y. (2015). Towards news verification: Deception detection methods for news discourse.

Ruchansky, N., Seo, S., and Liu, Y. (2017). Csi: A hybrid deep model for fake news detection. In ACM on Con on Inform. and Knowledge Management.

Sethi, R. J. (2017). Crowdsourcing the verification of fake news and alternative facts. In ACM Con on Hypertext and Social Media.

Sharma, K., Qian, F., Jiang, H., Ruchansky, N., Zhang, M., and Liu, Y. (2019). Combating fake news: A survey on identification and mitigation techniques. ACM Trans. Intell. Syst. Technol.

Shu, K., Mahudeswaran, D., and Liu, H. (2019). Fakenewstracker:a tool for fake news collection,detection, and visualization. Com. Mat. Or. The.

Shu, K., Sliva, A., Wang, S., Tang, J., and Liu, H. (2017a). Fake news detection on social media: A data mining perspective. SIGKDD Ex. New.

Shu, K., Wang, S., and Liu, H. (2017b). Exploiting tri-relationship for fake news detection. In arXiv.

Tschiatschek, S., Singla, A., Gomez Rodriguez, M., Merchant, A., and Krause, A. (2018). Fake news detection in social networks via crowd signals. In Comp. Proc. of the The Web Con.

Vosoughi, S., Mohsenvand, M. N., and Roy, D. (2017). Rumor gauge: Predicting the veracity of rumors on twitter. ACM Trans. Know. Disc. Data.

Wang, W. Y. (2017). “liar, liar pants on fire”: A new benchmark dataset for fake news detection. In The Assoc. for Comput. Linguistics.

Woloszyn, V. and Nejdl, W. (2018). Distrustrank: Spotting false news domains. In ACM Con on Web Science.

Wu, L. and Liu, H. (2018). Tracing fake-news footprints: Characterizing social media messages by how they propagate. In ACM Int Con on Web Search and Data Mining.

Zhang, Q., Yilmaz, E., and Liang, S. (2018). Ranking-based method for news stance detection. In Comp. Proc. of the The Web Con.