Abstract
Now-a-days fake news have become part and parcel of our everyday life due to its quick spreading in different social media. Fake news identification has been emerging as an important research subject due to the widespread dissemination of fake news on social and news media. Current fake news identification techniques primarily rely on the analysis of natural languages and machine learning models to assess the validity of news information in order to detect whether it is real or fake. Many traditional approaches including machine learning applications have been observed yet to detect fake news but the evolutionary based algorithms have gained lot of popularity because of their ability to converge to near optima and have low computational complexity. This motivated us to adopt a new approach with genetic algorithm to solve the fake news detection problem. In this paper, a comparative analysis is presented among SVM, Naïve Bayes, Random Forest and Logistic Regression classifiers to detect fake news applying on different datasets. SVM classifier has achieved the highest accuracy with 61%, 97% and 96% in Liar, Fake Job Posting and Fake News datasets respectively. Again, SVM, Naïve Bayes, Random Forest and Logistic Regression are considered as the fitness function in our novel GA based fake news detection algorithm. In our proposed algorithm, SVM and LR classifiers both achieved 61% accuracy in LIAR dataset and SVM and RF attained the highest accuracy as 97% in the fake job posting dataset.
Similar content being viewed by others
References
Abu-Nimeh S, Chen T, Alzubi O (2011) Malicious and spam posts in online social networks. Computer 44(9):23–28
Ahmed S, Hinkelmann K, Corradini F (2019) Combining machine learning with knowledge engineering to detect fake news in social networks-a survey. In: Proceedings of the AAAI 2019 Spring Symposium, vol 12
Aldwairi M, Alwahedi A (2018) Detecting fake news in social media networks. Procedia Computer Science 141:215–222
Aldwairi M, Hasan M, Balbahaith Z (2020) Detection of drive-by download attacks using machine learning approach. In: Cognitive analytics: concepts, Methodologies, Tools, and Applications. IGI Global, pp 1598–1611
Allcott H, Gentzkow M (2017) Social media and fake news in the 2016 election. Journal of Economic Perspectives 31(2):211–36
Balmas M (2014) When fake news becomes real: Combined exposure to multiple news sources and political attitudes of inefficacy, alienation, and cynicism. Communication Research 41(3):430–454
Bharadwaj P, Shao Z (2019) Fake news detection with semantic features and text mining. International Journal on Natural Language Computing (IJNLC) vol 8
Bhatt G, Sharma A, Sharma S, Nagpal A, Raman B, Mittal A (2017) On the benefit of combining neural, statistical and external features for fake news identification. arXiv:1712.03935
Burgess L (2018) What pizzagate teaches us about literacy. Ph.D thesis
Chakraborty A, Paranjape B, Kakarla S, Ganguly N (2016) Stop clickbait: Detecting and preventing clickbaits in online news media. In: 2016 Ieee/acm international conference on advances in social networks analysis and mining (asonam). IEEE, pp 9–16
Conroy NK, Rubin VL, Chen Y (2015) Automatic deception detection: Methods for finding fake news. Proceedings of the Association for Information Science and Technology 52(1):1–4
Deb K, Agrawal S (1998) Understanding interactions among genetic algorithm parameters. In: FOGA, pp 265–286
Del Vicario M, Bessi A, Zollo F, Petroni F, Scala A, Caldarelli G, Stanley HE, Quattrociocchi W (2016) The spreading of misinformation online. Proceedings of the National Academy of Sciences 113(3):554–559
Dutta S, Bandyopadhyay SK (2020) Fake job recruitment detection using machine learning approach. International Journal of Engineering Trends and Technology, 68
Eiben AE, Michalewicz Z, Schoenauer M, Smith JE (2007) Parameter control in evolutionary algorithms. In: Parameter setting in evolutionary algorithms. Springer, pp 19–46
Goel V, Raj S, Ravichandran P (2018) How whatsapp leads mobs to murder in india. The New York Times, 18
Gorbach J (2018) Not your grandpa’s hoax: a comparative history of fake news. Am J 35(2):236–249
Gravanis G, Vakali A, Diamantaras K, Karadais P (2019) Behind the cues: a benchmarking study for fake news detection. Expert Syst Appl 128:201–213
Gunn SR, et al. (1998) Support vector machines for classification and regression. ISIS Technical Report 14(1):5–16
Gupta A, Lamba H, Kumaraguru P, Joshi A (2013) Faking sandy: characterizing and identifying fake images on twitter during hurricane sandy. In: Proceedings of the 22nd international conference on World Wide Web, pp 729–736
Hassanat A, Almohammadi K, Alkafaween E, Abunawas E, Hammouri A, Prasath V (2019) Choosing mutation and crossover ratios for genetic algorithms—a review with a new dynamic approach. Information 10(12):390
Holland J (1975) Adaptation in natural and artificial systems: an introductory analysis with application to biology. Control and Artificial Intelligence
Hu X, Tang J, Gao H, Liu H (2014) Social spammer detection with sentiment information. In: 2014 IEEE International conference on data mining. IEEE, pp 180–189
Huang B, Carley KM (2020) Discover your social identity from what you tweet: a content based approach. arXiv:2003.01797
Klein D, Wueller J (2017) Fake news: a legal perspective. Journal of Internet Law (Apr. 2017)
Kleinbaum DG, Dietz K, Gail M, Klein M, Klein M (2002) Logistic regression. Springer, Berlin
Kwon S, Cha M, Jung K, Chen W, Wang Y (2013) Prominent features of rumor propagation in online social media. In: 2013 IEEE 13Th international conference on data mining. IEEE, pp 1103–1108
Lazer DM, Baum MA, Benkler Y, Berinsky AJ, Greenhill KM, Menczer F, Metzger MJ, Nyhan B, Pennycook G, Rothschild D et al (2018) The science of fake news. Science 359(6380):1094–1096
Lee K, Caverlee J, Webb S (2010) Uncovering social spammers: social honeypots+ machine learning. In: Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval, pp 435–442
Morstatter F, Wu L, Nazer TH, Carley KM, Liu H (2016) A new approach to bot detection: striking the balance between precision and recall. In: 2016 IEEE/ACM International conference on advances in social networks analysis and mining (ASONAM). IEEE, pp 533–540
Murphy KP, et al. (2006) Naive bayes classifiers. University of British Columbia 18(60):1–8
Mustafa W (2003) Optimization of production systems using genetic algorithms. Int J Comput Intell Appl 3(03):233–248
Parikh SB, Patil V, Atrey PK (2019) On the origin, proliferation and tone of fake news. In: 2019 IEEE Conference on multimedia information processing and retrieval (MIPR). IEEE, pp 135–140
Posetti J, Matthews A (2018) A short guide to the history of’fake news’ and disinformation. International Center for Journalists, 7
Qazvinian V, Rosengren E, Radev D, Mei Q (2011) Rumor has it: Identifying misinformation in microblogs. In: Proceedings of the 2011 conference on empirical methods in natural language processing, pp 1589–1599
Riedel B, Augenstein I, Spithourakis GP, Riedel S (2017) A simple but tough-to-beat baseline for the fake news challenge stance detection task. arXiv:1707.03264
Shu K, Sliva A, Wang S, Tang J, Liu H (2017) Fake news detection on social media: a data mining perspective. ACM SIGKDD Explorations Newsletter 19(1):22–36
Soll J (2016) The long and brutal history of fake news. Politico Magazine 18(12):2016
Svetnik V, Liaw A, Tong C, Culberson JC, Sheridan RP, Feuston BP (2003) Random forest: a classification and regression tool for compound classification and qsar modeling. Journal of Chemical Information and Computer Sciences 43(6):1947–1958
Tacchini E, Ballarin G, Della Vedova ML, Moret S, De Alfaro L (2017) Some like it hoax: Automated fake news detection in social networks. arXiv:1704.07506
Thota A, Tilak P, Ahluwalia S, Lohia N (2018) Fake news detection: a deep learning approach. SMU Data Science Review 1(3):10
Wang WY (2017) liar, liar pants on fire: A new benchmark dataset for fake news detection. arXiv:1705.00648
Wendling M (2018) The (almost) complete history of fake news. BBC News, 22
Whitley D (1994) A genetic algorithm tutorial. Statistics and Computing 4(2):65–85
Wu L, Li J, Hu X, Liu H (2017) Gleaning wisdom from the past: Early detection of emerging rumors in social media. In: Proceedings of the 2017 SIAM international conference on data mining. SIAM, pp 99–107
Yang F, Liu Y, Yu X, Yang M (2012) Automatic detection of rumor on sina weibo. In: Proceedings of the ACM SIGKDD workshop on mining data semantics, pp 1–7
Zahedi FM, Abbasi A, Chen Y (2015) Fake-website detection tools: Identifying elements that promote individuals’ use and enhance their performance. J Assoc Inf Syst 16(6):2
Zhang J, Dong B, Philip SY (2020) Fakedetector: Effective fake news detection with deep diffusive neural network. In: 2020 IEEE 36Th international conference on data engineering (ICDE). IEEE, pp 1826–1829
Zhong J, Hu X, Zhang J, Gu M (2005) Comparison of performance between different selection strategies on simple genetic algorithms. In: International conference on computational intelligence for modelling, control and automation and international conference on intelligent agents, web technologies and internet commerce (CIMCA-IAWTIC’06), vol 2. IEEE, pp 1115–1121
Zubiaga A, Aker A, Bontcheva K, Liakata M, Procter R (2018) Detection and resolution of rumours in social media: a survey. ACM Computing Surveys (CSUR) 51(2):1–36
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Choudhury, D., Acharjee, T. A novel approach to fake news detection in social networks using genetic algorithm applying machine learning classifiers. Multimed Tools Appl 82, 9029–9045 (2023). https://doi.org/10.1007/s11042-022-12788-1
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-022-12788-1