Abstract
The devastating effect of spreading fake news related to politics, health, and customer reviews cannot be neglected over social media on the decision-making approach of an individual. The problem of fake news needs the attention of social media administrators, law enforcement agencies, and academic researchers. To handle this issue, researchers suggested various artificial intelligence techniques. However, most of the studies used only a specific type of news that leads to dataset biases. This study used three different standard datasets collected from Kaggle and GitHub. Preprocessed the datasets to remove unwanted text. Then these preprocessed datasets are applied on three classifiers: passive aggressive, machine learning, and naïve Bayes of 30–70, 40–60, 50–50, 60–40, and 70–30, respectively. To evaluate the performance accuracy, precision and recall are used. Results clearly show that this study outperforms the state-of-the-art techniques.
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
Similar content being viewed by others
References
Aggarwal A, Rajadesingan A, Kumaraguru P (2012) PhishAri: automatic realtime phishing detection on twitter. eCrime Researchers Summit (eCrime)
Ajao O, Bhowmik D, Zargari S (2018) Fake news identification on twitter with hybrid cnn and rnn models. Proceedings of the 9th International Conference on Social Media and Society
Al-garadi MA, Varathan KD, Ravana SD (2016) Cybercrime detection in online communications: the experimental case of cyberbullying detection in the Twitter network. Comput Hum Behav 63:433–443
Bessi A, Ferrara E (2016) Social bots distort the 2016 US Presidential election online discussion
Bessi A, Ferrara E (2016) Social bots distort the 2016 US Presidential election online discussion. First Monday 21(11–7)
Boididou C, Papadopoulos S, Zampoglou M, Apostolidis L, Papadopoulou O, Kompatsiaris Y (2018) Detection and visualization of misleading content on Twitter. Int J Multimedia Inf Retrieval 7(1):71–86. https://doi.org/10.1007/s13735-017-0143-x
Buczak AL, Baugher B, Guven E, Ramac-Thomas LC, Elbert Y, Babin SM, Lewis SH (2015) Fuzzy association rule mining and classification for the prediction of malaria in South Korea. BMC Med Inform Decis Mak 15(1):47
Ding X, Liu B, Yu PS (2008) A holistic lexicon-based approach to opinion mining. Proceedings of the 2008 international conference on web search and data mining
Domonoske C (2016) Students have ‘dismaying’inability to tell fake news from real, study finds. National Public Radio 23
Edkins B (2016) Americans Believe ey Can Detect Fake News. Studies Show ey Can’t.(December 2016). In
Eysenbach G (2008) Credibility of health information and digital media: new perspectives and implications for youth. MacArthur foundation digital media and learning initiative
Eysenbach G (2008) Credibility of health information and digital media: new perspectives and implications for youth. Digital Media Youth Credibility 123–154
Fernández-Luque L, Bau T (2015) Health and social media: perfect storm of information. Healthcare Inf Res 21(2):67–73
Ferrara E (2017) Disinformation and social bot operations in the run up to the 2017 French presidential election
Girgis S, Amer E, Gadallah M (2018) Deep learning algorithms for detecting fake news in online text. 2018 13th international conference on computer engineering and systems (ICCES)
Heydari A, Ali Tavakoli M, Salim N, Heydari Z (2015) Detection of review spam: a survey. Expert Syst Appl 42(7):3634–3642
Howard PN, Kollanyi B (2016) Bots,# StrongerIn, and# Brexit: computational propaganda during the UK-EU referendum. Available at SSRN 2798311
Howard, P. N., & Kollanyi, B. (2016). Bots,# strongerin, and# brexit: Computational propaganda during the uk-eu referendum. Browser Download This Paper.
Ishtiaq U, Kareem SA, Abdullah ERMF, Mujtaba G, Jahangir R, Ghafoor HY (2019) Diabetic retinopathy detection through artificial intelligent techniques: a review and open issues. Multimedia Tools Appl 1–44
Ivanitskaya L, Boyle IO, Casey AM (2006) Health information literacy and competencies of information age students: results from the interactive online Research Readiness Self-Assessment (RRSA). J Med Internet Res 8(2):e6
Kannan S, Gurusamy V (2014) Preprocessing techniques for text mining. Conference Paper. India
Lauw H, Shafer JC, Agrawal R, Ntoulas A (2010) Homophily in the digital world: A LiveJournal case study. IEEE Internet Comput 14(2):15–23
Liu, B. (2007). Web data mining: exploring hyperlinks, contents, and usage data. Springer Science & Business Media.
McCallum A, Nigam K (1998) A comparison of event models for naive bayes text classification. AAAI-98 workshop on learning for text categorization
Ramasubramanian C, Ramya R (2013) Effective pre-processing activities in text mining using improved porter’s stemming algorithm. Int J Adv Res Comput Commun Eng 2(12):4536–4538
Ruchansky N, Seo S, Liu Y (2017) Csi: a hybrid deep model for fake news detection. Proceedings of the 2017 ACM on conference on information and knowledge management
Scott J (2017) Social network analysis. Sage
Shu K, Sliva A, Wang S, Tang J, Liu H (2017) Fake news detection on social media: a data mining perspective. ACM SIGKDD Explorations Newsl 19(1):22–36
Tucker J, Guess A, Barberá P, Vaccari C, Siegel A, Sanovich S, Stukal D, Nyhan B (2018) Social media, political polarization, and political disinformation: a review of the scientific literature
Vijayarani S, Ilamathi MJ, Nithya M (2015) Preprocessing techniques for text mining-an overview. Int J Comput Sci Commun Netw 5(1):7–16
Vijayarani S, Janani R (2016) Text mining: open source tokenization tools-an analysis. Adv Comput Intell: Int J (ACII) 3(1):37–47
Viviani M, Pasi G (2017) Credibility in social media: opinions, news, and health information—a survey. Wiley Interdisciplinary Rev: Data Mining and Knowl Discovery 7(5)
Vosoughi S, Roy D, Aral S (2018) The spread of true and false news online. Science 359(6380):1146–1151
Yang C, Harkreader R, Zhang J, Shin S, Gu G (2012) Analyzing spammers’ social networks for fun and profit: a case study of cyber criminal ecosystem on twitter. Proceedings of the 21st international conference on World Wide Web
Yardi S, Romero D, Schoenebeck G (2009) Detecting spam in a twitter network. First Monday 15(1)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Ghafoor, H.Y., Jaffar, A., Jahangir, R., Iqbal, M.W., Abbas, M.Z. (2022). Fake News Identification on Social Media Using Machine Learning Techniques. In: Ullah, A., Anwar, S., Rocha, Á., Gill, S. (eds) Proceedings of International Conference on Information Technology and Applications. Lecture Notes in Networks and Systems, vol 350. Springer, Singapore. https://doi.org/10.1007/978-981-16-7618-5_8
Download citation
DOI: https://doi.org/10.1007/978-981-16-7618-5_8
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-7617-8
Online ISBN: 978-981-16-7618-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)