Fake News Detection using Machine Learning Algorithm

© 2022 by IJCTT Journal
Volume-70 Issue-3
Year of Publication : 2022
Authors : K Phalguna Rao
DOI :  10.14445/22312803/IJCTT-V70I3P103

How to Cite?

K Phalguna Rao, "Fake News Detection using Machine Learning Algorithm," International Journal of Computer Trends and Technology, vol. 70, no. 3, pp. 16-18, 2022. Crossref, https://doi.org/10.14445/22312803/IJCTT-V70I3P103

Recent works have focused on understanding and detection of fake news stories that are information spread widely on social media. To accomplish this goal, these works explore several types of features extracted from news stories, including sources and posts from social media. Presenting a new set of features and measuring the Prediction performance of current approaches and features automatic detection of fake news discussing how fake news detection approaches can be used in practice, highlighting challenges and opportunities.

Machine Learning, Supervised Learning, social media, Prediction.


[1] Economic and Social Research Council. Using Social Media. [Online]. Available: https://esrc.ukri.org/research/impact-toolkit/social-media/using-social-media.
[2] (2019). Gil P. [Online]. Available: https://www.lifewire.com/what-exactly-is-twitter-2483331.
[3] E. C. Tandoc Jr et al., Defining Fake News a Typology of Scholarly Definitions. Digital Journalism. (2017) 1-17.
[4] J. Radianti et al., An Overview of Public Concerns During the Recovery Period after a Major Earthquake: Nepal Twitter Analysis. HICSS `16 Proceedings of the 2016 49th Hawaii International Conference on System Sciences (HICSS). Washington, DC, USA: IEEE. (2016) 136-145.
[5] Alkhodair S A, Ding S H.H, Fung B C M, Liu J, Detecting Breaking News Rumours of Emerging Topics in Social Media Inf. Process. Manag. 57 (2020) 102018.
[6] Jeong-hee Yi et al., Sentiment Analyzer: Extracting Sentiments About a Given Topic Using Natural Language Processing Techniques. In Data Mining, ICDM 2003,Third IEEE International Conference. http://citeseerx.ist.psu.edu. 200. (2003) 427-434.
[7] Tapaswi et al., Treebank Based Deep Grammar Acquisition and Part-of-Speech Tagging for Sanskrit M Sentences. Software Engineering (CONSEG), on Software Engineering (CONSEG), IEEE. (2012) 1-4.
[8] Ranjan et al., Part of Speech Tagging and Local Word Grouping Techniques for Natural Language Parsing in Hindi. In Proceedings of the 1st International Conference on Natural Language Processing (ICON 2003), Semantic Scholar. (2003).
[9] MonaDiab et al., Automatic Tagging of Arabic Text: from Raw Text to Base Phrase Chunks. Proceedings of HLT-NAACL 2004: Short Papers. Boston, Massachusetts, USA: Association for Computational Linguistics. (2004) 149-152.
[10] D. M. J. Lazer et al., The Science of Fake News, Science. 359(6380) (2018) 1094–1096.
[11] N. J. Conroy, V. L. Rubin, and Y. Chen, Automatic Deception Detection: Methods for Finding Fake News, In Proc. Annu Meeting Assoc. Inf. Sci. Technol. (2015) 1–4.
[12] W. Y. Wang, Liar, Liar Pants on Fire: A New Benchmark Dataset for Fake News Detection, In Proc. Annu. Meeting Assoc. Comput. Linguistics. (2017) 422–426.
[13] Edgerly S, Mourão R.R, Thorson E, Tham S.M, When Do Audiences Verify? How Perceptions about the Message and Source Influence Audience Verification of News Headlines. Journal. Mass Commun. 97 (2019) 52–71.
[14] Rawls J, Political Liberalism. Columbian University Press, New York. (1993).
[15] Gumprecht E, Where ‘Fake News’ Flourishes: A Comparison Across Four Western Democracies. Inf. Commun. Soc. 22(13) (2019) 1973–1988.
[16] Loos E.F, Senior Citizens: Digital Immigrants in Their Own Country? Observatorio (OBS*). 6(1) (2012) 1–23.
[17] Newman N, Fletcher R, Kalogeropoulos A, Levy D, Nielsen R.K, Reuters Institute Digital News Report. (2017).
[18] Allcott H, Gentzkow M, Social Media and Fake News in the 2016 Election. J. Econ. Perspect. 31(2) (2017) 211–236.
[19] Bakir V, McStay A, Fake News and the Economy of Emotions: Problems, Causes, Solutions. Digit. J. 6(2) (2018) 154–175.
[20] Guo L, Vargo C, “Fake News” and Emerging Online Media Ecosystem: An Integrated Intermedia Agenda-Setting Analysis of the 2016 US Presidential Election. Commun. Res. 47 (2018) 178–200.
[21] Guess A, Nyhan B, Reifler J, Selective Exposure to Misinformation: Evidence from the Consumption of Fake News During the 2016 US Presidential Campaign. European Research Council. 9 (2018).
[22] Mehta R, Guzmán L.D, Fake or Visual Trickery? Understanding the Quantitative Visual Rhetoric in the News. J. Media Lit. Educ. 10(2) (2018) 104–122.
[23] Broersma M, Graham T, Social Media as a Beat: Tweets as a News Source During the 2010 British and Dutch Elections. Journal. Pract. 6(3) (2012) 403–419.
[24] Roozenbeek J, Van der Linden S, Fake News Game Confers Psychological Resistance Against Online Misinformation. Palgrave Commun. 5(1) (2019) 12.
[25] Tandoc Jr. E.C, Ling R, Westlund O, Duffy A, Goh D, Zheng Wei L, Audiences’ Acts of Authentication in the Age of Fake News: A Conceptual Framework. New Media Soc. 20(8) (2018) 2745–2763.