Abstract
Nowadays, social media has been becoming the main news source for millions of people all over the world. Users easily can create and share their information on social platforms. Information on social media can spread rapidly in the community. However, the spreading of misleading information is a critical issue. There are much intentionally written to mislead the readers, that are called fake news. The fake news represents the most forms of false or unverified information. The extensive spread of fake news has negative impacts on society. Detecting and blocking early fake news is very essential to avoid the negative effect on the community. In this paper, we exploit the news content, the wisdom of crowds in the social interaction and the user’s credibility characteristics to automatically detect fake news on Twitter. First, the user profile is exploited to measure the credibility level. Second, the users’ interactions for a post such as Comment, Favorite, Retweet are collected to determine the user’s opinion and exhortation level. Finally, a Support Vector Machine (SVM) model with the Radial Basis Function (RBF) kernel is applied to determine the authenticity of the news. Experiments conducted on a Twitter dataset and demonstrated the effectiveness of the proposed method.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Afroz, S., Brennan, M., Greenstadt, R.: Detecting hoaxes, frauds, and deception in writing style online. In: 2012 IEEE Symposium on Security and Privacy, pp. 461–475. IEEE (2012)
Bondielli, A., Marcelloni, F.: A survey on fake news and rumour detection techniques. Inf. Sci. 497, 38–55 (2019)
Della Vedova, M.L., Tacchini, E., Moret, S., Ballarin, G., DiPierro, M., de Alfaro, L.: Automatic online fake news detection combining content and social signals. In: 2018 22nd Conference of Open Innovations Association (FRUCT), pp. 272–279. IEEE (2018)
Hardalov, M., Koychev, I., Nakov, P.: In search of credible news. In: Dichev, C., Agre, G. (eds.) AIMSA 2016. LNCS (LNAI), vol. 9883, pp. 172–180. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-44748-3_17
Jin, Z., Cao, J., Zhang, Y., Luo, J.: News verification by exploiting conflicting social viewpoints in microblogs. In: Thirtieth AAAI Conference on Artificial Intelligence (2016)
Kwon, S., Cha, M., Jung, K., Chen, W., Wang, Y.: Prominent features of rumor propagation in online social media. In: 2013 IEEE 13th International Conference on Data Mining, pp. 1103–1108. IEEE (2013)
Pérez-Rosas, V., Kleinberg, B., Lefevre, A., Mihalcea, R.: Automatic detection of fake news. arXiv preprint arXiv:1708.07104 (2017)
Qin, Y., Wurzer, D., Lavrenko, V., Tang, C.: Spotting rumors via novelty detection. arXiv preprint arXiv:1611.06322 (2016)
Rashkin, H., Choi, E., Jang, J.Y., Volkova, S., Choi, Y.: Truth of varying shades: analyzing language in fake news and political fact-checking. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 2931–2937 (2017)
Ruchansky, N., Seo, S., Liu, Y.: CSI: a hybrid deep model for fake news detection. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp. 797–806. ACM (2017)
Shu, K., Bernard, H.R., Liu, H.: Studying fake news via network analysis: detection and mitigation. In: Agarwal, N., Dokoohaki, N., Tokdemir, S. (eds.) Emerging Research Challenges and Opportunities in Computational Social Network Analysis and Mining. LNSN, pp. 43–65. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-94105-9_3
Shu, K., Sliva, A., Wang, S., Tang, J., Liu, H.: Fake news detection on social media: a data mining perspective. ACM SIGKDD Explor. Newslett. 19(1), 22–36 (2017)
Shu, K., Wang, S., Liu, H.: Understanding user profiles on social media for fake news detection. In: 2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR), pp. 430–435. IEEE (2018)
Shu, K., Wang, S., Liu, H.: Beyond news contents: the role of social context for fake news detection. In: Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, pp. 312–320. ACM (2019)
Tacchini, E., Ballarin, G., Della Vedova, M.L., Moret, S., de Alfaro, L.: Some like it hoax: automated fake news detection in social networks. arXiv preprint arXiv:1704.07506 (2017)
Tran, V.C., Hwang, D., Nguyen, N.T.: Hashtag recommendation approach based on content and user characteristics. Cybern. Syst. 49(5–6), 368–383 (2018)
Wei, X., Zhang, Z., Zeng, D.D.: Combining crowd and machine intelligence to detect false news in social media. Available at SSRN 3355763 (2019)
Zollo, F., et al.: Emotional dynamics in the age of misinformation. PLoS ONE 10(9), e0138740 (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Tran, V.C., Nguyen, V.D., Nguyen, N.T. (2020). Automatic Fake News Detection by Exploiting User’s Assessments on Social Networks: A Case Study of Twitter. In: Fujita, H., Fournier-Viger, P., Ali, M., Sasaki, J. (eds) Trends in Artificial Intelligence Theory and Applications. Artificial Intelligence Practices. IEA/AIE 2020. Lecture Notes in Computer Science(), vol 12144. Springer, Cham. https://doi.org/10.1007/978-3-030-55789-8_33
Download citation
DOI: https://doi.org/10.1007/978-3-030-55789-8_33
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-55788-1
Online ISBN: 978-3-030-55789-8
eBook Packages: Computer ScienceComputer Science (R0)