Text Mining-based Fake News Detection Using News And Social Media Data

Yoonjin Hyun, Namgyu Kim

Abstract


Recently, fake news has attracted worldwide attentions regardless of the fields. The Hyundai Research Institute estimated that the amount of fake news damage reached about 30.9 trillion won per year. The government is making efforts to develop artificial intelligence source technology to detect fake news such as holding “artificial intelligence R&D challenge” competition on the title of “searching for fake news.” Fact checking services are also being provided in various private sector fields. Nevertheless, in academic fields,  there are also many attempts have been conducted in detecting the fake news. Typically, there are different attempts in detecting fake news such as expert-based, collective intelligence-based, artificial intelligence-based, and semantic-based. However, the more accurate the fake news manipulation is, the more difficult it is to identify the authenticity of the news by analyzing the news itself. Furthermore, the accuracy of most fake news detection models tends to be overestimated. Therefore, in this study, we first propose a method to secure the fairness of false news detection model accuracy. Secondly, we propose a method to identify the authenticity of the news using the social data broadly generated by the reaction to the news as well as the contents of the news.


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References


Albright, R., Taming Text with the SVD, SAS Institute Inc., 2006.

Chen, C., Wu K., Srinivasan V., and Zhang, X., “Battling the Internet Water Army: Detection of Hidden Paid Posters,” In Advances in Social Networks Analysis and Mining (ASONAM), 2013 IEEE/ACM International Conference, pp. 116-120, 2013.

Chen, Y., Conroy, N. J., and Rubin, V. L., “Misleading Online Content: Recognizing Clickbait as False News,” In Proceedings of the 2015 ACM on Workshop on Multimodal Deception Detection, pp. 15-19, 2015.

Conroy, N. J., Rubin, V. L., and Chen, Y., “Automatic Deception Detection: Methods for Finding Fake News,” Proceedings of the Association for Information Science and Technology, Vol. 52, No. 1, pp. 1-4, 2016.

Granik, M. and Mesyura, V., “Fake News Detection Using Naive Bayes Classifier,” In Electrical and Computer Engineering (UKRCON), 2017 IEEE First Ukraine Conference, pp. 900-903, 2017.

Hwang, Y. and Kwon, O., “A Study on the Conceptualization and Regulation Measures on Fake News: Focused on Self-Regulation of Internet Service Providers,” Journal of Media Law, Ethics and Policy Research, Vol. 16, No. 1, pp. 53-101, 2017.

Jin, Z., Cao, J., Jiang, Y. G., and Zhang, Y., “News Credibility Evaluation on Microblog with a Hierarchical Propagation Model,” In Data Mining (ICDM), 2014 IEEE International Conference, pp. 230-239, 2014.

Kim, D. J., “Semantic Analysis on Fake News through Portal Site and Social Network,” Master Thesis, 2017.

Kim, H. Y., “An Exploratory Study on Fake News Using Topic Modeling: Focused on Fake News Published in the Online Journalism,” Master Thesis, 2017.

Kwon, M., Jun, Y. W., and Im, H., “Controversy and Guideline Suggestion Surrounding Fake News in the Digital Media Age,” Journal of Korea Multimedia Society, Vol. 18, No. 11, pp. 1419-1426, 2015.

Kwon, S., Cha, M., Jung, K., Chen, W., and Wang, Y., “Prominent Features of Rumor Propagation in Online Social Media,” In Data Mining (ICEM), 2013 IEEE 13th International Conference, pp. 1103-1108, 2013.

Oh, S. U., “Current States and Limitations of Automated Fact Checking Technology,” Journal of Cybercommunication Academic Society, Vol. 34, No. 3, pp. 137-180, 2017.

Park, J. H. and Kim, Y. I., “Development of a Fake News Discrimination System using SVM Classifier,” Proceedings of KIIT Summer Conference, pp. 354-355, 2017.

Rubin, V. L., Chen, Y., and Conroy, N. J., “Deception Detection for News: Three Types of Fakes,” Proceedings of the Association for Information Science and Technology, Vol. 52, No. 1, pp. 1-4, 2016.

Lee, D., Kim, Y., and Kim, K., “Topic Based Hierarchical Network Analysis for Entrepreneur Using Text Mining,” The Journal of Society for e-Business Studies, Vol. 23, No. 3, pp. 33-49, 2018.

Lee, S. and Kim, H. J., “Keyword Extraction from News Corpus using Modified TF-IDF,” The Journal of Society for e-Business Studies, Vol. 14, No. 4, pp. 59-73, 2009.

Salton, G., Wong, A., and Yang, C. S., “A Vector Space Model for Automatic Indexing,” Communications of the ACM, Vol. 18, No. 11, pp. 613-620, 1975.

Sethi, R. J., “Spotting Fake News: A Social Argumentation Framework for Scrutinizing Alternative Facts,” In Web Services (ICWS), 2017 IEEE International Conference, pp. 866-869, 2017.

Weiss, S. M., Indurkhya, N., and Zhang, T., Fundamentals of Predictive Text Mining, Springer, 2010.


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