Kai Shu
Huan Liu
ABSTRACT
Consuming news from social media is becoming increasingly popular. Social media appeals to users due to its fast dissemination of information, low cost, and easy access. However, social media also enables the widespread of fake news. Due to the detrimental societal effects of fake news, detecting fake news has attracted increasing attention. However, the detection performance only using news contents is generally not satisfactory as fake news is written to mimic true news. Thus, there is a need for an in-depth understanding on the relationship between user profiles on social media and fake news. In this paper, we study the problem of understanding and exploiting user profiles on social media for fake news detection. In an attempt to understand connections between user profiles and fake news, first, we measure users' sharing behaviors and group representative users who are more likely to share fake and real news; then, we perform a comparative analysis of explicit and implicit profile features between these user groups, which reveals their potential to help differentiate fake news from real news. To exploit user profile features, we demonstrate the usefulness of these user profile features in a fake news classification task. We further validate the effectiveness of these features through feature importance analysis. The findings of this work lay the foundation for deeper exploration of user profile features of social media and enhance the capabilities for fake news detection.
- K. Shu, A. Sliva, S. Wang, J. Tang, and H. Liu, "Fake news detection on social media: A data mining perspective," KDD exploration newsletter, 2017.Google Scholar
- X. Zhou and R. Zafarani, "Fake news: A survey of research, detection methods, and opportunities," arXiv preprint arXiv:1812.00315, 2018.Google Scholar
- C. Castillo, M. Mendoza, and B. Poblete, "Information credibility on twitter," in Proceedings of the 20th international conference on World wide web. ACM, 2011, pp. 675--684.Google ScholarDigital Library
- K. Shu, D. Mahudeswaran, S. Wang, D. Lee, and H. Liu, "Fakenewsnet: A data repository with news content, social context and dynamic information for studying fake news on social media," arXiv preprint arXiv:1809.01286, 2018.Google Scholar
- C. A. Davis, O. Varol, E. Ferrara, A. Flammini, and F. Menczer, "Botornot: A system to evaluate social bots," in WWW, 2016, pp. 273--274.Google Scholar
- K. Shu, S. Wang, and H. Liu, "Understanding user profiles on social media for fake news detection," in MIPR. IEEE, 2018.Google Scholar
- A. Rahimi, T. Cohn, and T. Baldwin, "pigeo: A python geotagging tool," Proceedings of ACL-2016 System Demonstrations, pp. 127--132, 2016.Google Scholar
- K. Sohn, H. Lee, and X. Yan, "Learning structured output representation using deep conditional generative models," in Advances in neural information processing systems, 2015, pp. 3483--3491.Google Scholar
- M. Gentzkow, J. M. Shapiro, and D. F. Stone, "Media bias in the marketplace: Theory," National Bureau of Economic Research, Tech. Rep., 2014.Google ScholarCross Ref
- J. Kulshrestha, M. Eslami, J. Messias, M. B. Zafar, S. Ghosh, K. P. Gummadi, and K. Karahalios, "Quantifying search bias: Investigating sources of bias for political searches in social media," in CSCW'17.Google Scholar
- Y. Ji and J. Eisenstein, "Representation learning for text-level discourse parsing," in ACL'2014, vol. 1, 2014, pp. 13--24.Google Scholar
- J. W. Pennebaker, R. L. Boyd, K. Jordan, and K. Blackburn, "The development and psychometric properties of liwc2015," Tech. Rep., 2015.Google Scholar
- N. Cantor and J. F. Kihlstrom, Personality, cognition and social interaction. Routledge, 2017, vol. 5.Google ScholarCross Ref
- M. Potthast, J. Kiesel, K. Reinartz, J. Bevendorff, and B. Stein, "A stylometric inquiry into hyperpartisan and fake news," arXiv preprint arXiv:1702.05638, 2017.Google Scholar
- A. Gupta, H. Lamba, P. Kumaraguru, and A. Joshi, "Faking sandy: characterizing and identifying fake images on twitter during hurricane sandy," in WWW'13.Google Scholar
- Z. Jin, J. Cao, Y. Zhang, and J. Luo, "News verification by exploiting conflicting social viewpoints in microblogs." in AAAI, 2016, pp. 2972--2978.Google Scholar
- H. A. Schwartz, J. C. Eichstaedt, M. L. Kern, L. Dziurzynski, S. M. Ramones, M. Agrawal, A. Shah, M. Kosinski, D. Stillwell, M. E. Seligman et al., "Personality, gender, and age in the language of social media: The open-vocabulary approach," PloS one, vol. 8, no. 9, p. e73791, 2013.Google ScholarCross Ref
- The role of user profiles for fake news detection
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