INFORMATION CHANGE THE WORLD

International Journal of Information Technology and Computer Science(IJITCS)

ISSN: 2074-9007 (Print), ISSN: 2074-9015 (Online)

Published By: MECS Press

IJITCS Vol.12, No.1, Feb. 2020

An Improved Classification Model for Fake News Detection in Social Media

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Author(s)

Bodunde Akinyemi, Oluwakemi Adewusi, Adedoyin Oyebade

Index Terms

Fake news;classification;stacking ensemble;news instances;news content;social-context features;social media

Abstract

Fake news dissemination is a critical issue in today’s fast-changing network environment. Existing classification models for fake news detection have not completely stopped the spread because of their inability to accurately classify news, thus leading to a high false alarm rate. This study proposed a model that can accurately identify and classify deceptive news articles content infused on social media by malicious users. The news content, social-context features and the respective classification of reported news was extracted from the PHEME dataset using entropy-based feature selection. The selected features were normalized using Min-Max Normalization techniques. A predictive fake news detection model was formulated as a stacked ensemble of three algorithms. The model was simulated and its performance was evaluated by benchmarking with an existing model using detection accuracy, sensitivity, and precision as metrics. The result of the evaluation showed a higher 17.25% detection accuracy, 15.78% sensitivity, but lesser 0.2% precision than the existing model. Thus, the proposed model detects more fake news instances accurately based on news content and social content perspectives. This indicates that the proposed classification model has a better detection rate, reduces the false alarm rate of news instances and thus detects fake news more accurately.

Cite This Paper

Bodunde Akinyemi, Oluwakemi Adewusi, Adedoyin Oyebade, "An Improved Classification Model for Fake News Detection in Social Media", International Journal of Information Technology and Computer Science(IJITCS), Vol.12, No.1, pp.34-43, 2020. DOI: 10.5815/ijitcs.2020.01.05

Reference

[1]A. Perrin, Social Media Usage: 2005-2015. Washington, D.C.: Pew Internet & American Life Project. Retrieved October 12, 2015 from http://www.pewinternet.org/2015/10/08/social-networking-usage-2005-2015/, 2015.

[2]J. Clement, Number of internet users in Nigeria from 2017 to 2023. Available at https://www.statista.com/statistics/183849/internet-users-nigeria/, 2019.

[3]Africa Practice.  “Social Media Landscape in Nigeria.”  Last modified 2014.  Accessed 16 June 2016. http://www.africapractice.com/wp-content/uploads/2014/04/Africa-PracticeSocial-Media-Landscape-Vol-1.pdf, 2014.

[4]J. Thorne, M. Chen, G. Myrianthous, J Pu., X. Wang, A Vlachos, Fake news stance detection using stacked ensemble of classifiers. Proceedings of the 2017 EMNLP Workshop: Natural Language Processing meets Journalism, 2017, pp 80–83, DOI: 10.18653/v1/W17-4214.

[5]S. Schifferes, N. Newman, N. Thurman, D. Corney, A. G¨oker, C. Martin, Identifying and verifying news through social media: Developing a user-centred tool for professional journalists. Digital journalism, 2014, 2(3):406–418.

[6]K. Stahl, Fake news detection in social media. Available at https://www.csustan.edu/sites/default
/files/groups/University%20Honors%20Program/Journals/02_stahl.pdf, 2018.

[7]A. Ceron, L. Curini, S. M. Iacus, G. Porro, Every tweet counts? How sentiment analysis of social media can improve our knowledge of citizens political preferences with an application to Italy and France. New media & society, 2014, 16(2):340–358.

[8]K. Shu, A. Sliva, S. Wang, J. Tang, H. Liu, Fake News Detection on Social Media: A Data Mining Perspective. ACM SIGKDD Explorations Newsletter. June 2017, 19(1): 22-36, 10.1145/3137597.3137600.

[9]B. D. Horne, S. Adali, 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 proceedings of the Eleventh International AAAI Conference on Web and Social Media, 2017.

[10]C. Silverman, Here are 50 of the biggest fake news hits on Facebook from 2016. BuzzFeed, https://www.buzzfeed.com/craigsilverman/top-fake-news-of-2016, 2016.

[11]E. Ferrara, Manipulation and abuse on social media by emilio ferrara with ching-man au yeung as coordinator. ACM SIGWEB Newsletter Spring, 2015, 4.

[12]J Keller, A fake AP tweet sinks the DOWfor an instant. Bloomberg Businessweek, 2013.

[13]F. Álvaro, O. Luciana, The current state of fake news: challenges and opportunities. Procedia Computer Science, 2017, 121:817-825, DOI: 10.1016/j.procs.2017.11.106.

[14]X. Zhou, R. Zafarani, Fake News: A Survey of Research, Detection Methods, and Opportunities. ACM Comput. Surv, 2018, 1:1- 40.

[15]Y. Wu, P. K. Agarwal, C. Li, J. Yang. Toward computational fact-checking. Proceedings of the VLDB Endowment, 2014, 7(7):589-600

[16]M. Granik, V. Mesyura, Fake News Detection Using Naive Bayes Classifier. 2017 IEEE First Ukraine Conference on Electrical and Computer Engineering (UKRCON), 2017: 900-903.

[17]V. Qazvinian, E. Rosengren, D. R. Radev, Q. Mei, Rumor has it: Identifying misinformation in microblogs. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, 2011:1589–1599. 

[18]W. Ferreira and A. Vlachos, Emergent: a novel data-set for stance classification. In proceedings of the 15th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2016:1163–1168, DOI:10.18653/v1/N16-1138

[19]J. Ratkiewicz, M. Conover, M. R. Meiss, B. Goncalves, A. Flammini, F. Menczer, Detecting and tracking political abuse in social media. ICWSM, 2011, 11:297–304.

[20]N. Ruchansky, S. Seo, Y. Liu, CSI: A Hybrid Deep Model for Fake News Detection.  In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management (CIKM '17), 2017:797-806. DOI: 10.1145/3132847.3132877

[21]S. Helmstetter, H. Paulheim, Weakly Supervised Learning for Fake News Detection on Twitter. In proceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 2018: 274-279

[22]E. E Papalexakis, C. Faloutsos, N. D Sidiropoulos, Tensors for data mining and data fusion: Models, applications, and scalable algorithms. ACM Transactions on Intelligent Systems and Technology (TIST), 2017, 8 (2): 16.

[23]S. Hosseinimotlagh, E. E. Papalexakis, Unsupervised content-based identification of fake news articles with tensor decomposition ensembles. Misinformation and Misbehavior Mining on the Web Workshop held in conjunction with WSDM , 2018.

[24]O. Ajao D. Bhowmik, S. Zargari, Fake news identification on twitter with hybrid cnn and rnn models. In Proceedings of the 9th International Conference on Social Media and Society, 2018: 226–230.