Abstract
Fake news is a phenomenon which is having a significant impact on our social life, in particular in the political world. Fake news detection is an emerging research area which is gaining interest but involved some challenges due to the limited amount of resources (i.e., datasets, published literature) available. We propose in this paper, a fake news detection model that use n-gram analysis and machine learning techniques. We investigate and compare two different features extraction techniques and six different machine classification techniques. Experimental evaluation yields the best performance using Term Frequency-Inverted Document Frequency (TF-IDF) as feature extraction technique, and Linear Support Vector Machine (LSVM) as a classifier, with an accuracy of 92%.
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References
The Verge: Your short attention span could help fake news spread (2017). https://www.theverge.com/2017/6/26/15875488/fake-news-viral-hoaxes-bots-information-overload-twitter-facebook-social-media. Accessed 16 Aug 2017
Lemann, N.: Solving the Problem of Fake News. The New Yorker (2017). http://www.newyorker.com/news/news-desk/solving-the-problem-of-fake-news
Schulten, K.: Skills and Strategies—Fake News vs. Real News: Determining the Reliability of Sources. The Learning Network (2017). https://learning.blogs.nytimes.com/2015/10/02/skills-and-strategies-fake-news-vs-real-news-determining-the-reliability-of-sources/. Accessed 16 Aug 2017
Levin, S.: Pay to sway: report reveals how easy it is to manipulate elections with fake news. The Guardian (2017). https://www.theguardian.com/media/2017/jun/13/fake-news-manipulate-elections-paid-propaganda
Gu, L., Kropotov, V., Yarochkin, F.: The fake news machine, how propagandists abuse the internet and manipulate the public. In: 1st ed. [pdf] Trend Micro, p. 81 (2017). https://documents.trendmicro.com/assets/white_papers/wp-fake-news-machine-how-propagandists-abuse-the-internet.pdf?_ga=2.117063430.1073547711.1497355570–1028938869.1495462143
Schow, A.: The 4 Types of ‘Fake News’. Observer (2017). http://observer.com/2017/01/fake-news-russia-hacking-clinton-loss/
Rubin, V.L., Chen, Y., Conroy, N.J.: Deception detection for news: three types of fakes. In: Proceedings of the 78th ASIS&T Annual Meeting: Information Science with Impact: Research in and for the Community (ASIST 2015). Article 83, p. 4, American Society for Information Science, Silver Springs (2015)
Horne, B.D., Adali, S.: This just in: fake news packs a lot in title, uses simpler, repetitive content in text body, more similar to satire than real news. In: the 2nd International Workshop on News and Public Opinion at ICWSM (2017)
Burfoot, C., Baldwin, T.: Automatic satire detection: are you having a laugh? In: Proceedings of the ACL-IJCNLP 2009 Conference Short Papers, 4 August 2009, Suntec, Singapore (2009)
Wang, W.Y.: Liar, Liar Pants on fire: a new Benchmark dataset for fake news detection. arXiv preprint (2017). arXiv:1705.00648
Rubin., Victoria, L., et al.: Fake news or truth? Using satirical cues to detect potentially misleading news. In: Proceedings of NAACL-HLT (2016)
Gottfried, J., Shearer, E.: News use across social media platforms. Pew Res. Cent. 26 (2016)
Gottfried, J., et al.: The 2016 presidential campaign–a news event that’s hard to miss. Pew Res. Cent. 4 (2016)
Silverman, C., Singer-Vine, J.: Most americans who see fake news believe it, new survey says. BuzzFeed News (2016)
Dewey, C.: Facebook has repeatedly trended fake news since firing its human editors. Washington Post (2016)
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Ahmed, H., Traore, I., Saad, S. (2017). Detection of Online Fake News Using N-Gram Analysis and Machine Learning Techniques. In: Traore, I., Woungang, I., Awad, A. (eds) Intelligent, Secure, and Dependable Systems in Distributed and Cloud Environments. ISDDC 2017. Lecture Notes in Computer Science(), vol 10618. Springer, Cham. https://doi.org/10.1007/978-3-319-69155-8_9
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DOI: https://doi.org/10.1007/978-3-319-69155-8_9
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