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ORIGINAL ARTICLE
WEIGHTED ACCURACY ALGORITHMIC APPROACH IN COUNTERACTING FAKE NEWS AND DISINFORMATION
| 1 | School of Economics, School of Law and Intellectual Property, Zhejiang Gongshang University, China |
CORRESPONDING AUTHOR
Kwadwo Osei Bonsu
School of Economics, School of Law and Intellectual Property, Zhejiang Gongshang University, 18 Xuezheng St, Jianggan District, Hangzhou, Zheji, 310018, Hangzhou, China
School of Economics, School of Law and Intellectual Property, Zhejiang Gongshang University, 18 Xuezheng St, Jianggan District, Hangzhou, Zheji, 310018, Hangzhou, China
Submission date: 2020-08-01
Acceptance date: 2021-02-19
Online publication date: 2021-03-31
Publication date: 2021-03-31
Economic and Regional Studies 2021;14(1):99–107
KEYWORDS
artificial intelligencenatural language processingmachine learning algorithmfake newsdisinformationdigital revolution
TOPICS
ABSTRACT
Subject and purpose of work: Fake news and disinformation are polluting information
environment. Hence, this paper proposes a methodology for fake news detection through the
combined weighted accuracies of seven machine learning algorithms. Materials and methods: This paper uses natural language processing to analyze the text content
of a list of news samples and then predicts whether they are FAKE or REAL. Results: Weighted accuracy algorithmic approach has been shown to reduce overfitting. It was
revealed that the individual performance of the different algorithms improved after the data
was extracted from the news outlet websites and 'quality' data was filtered by the constraint
mechanism developed in the experiment. Conclusions: This model is different from the existing mechanisms in the sense that it automates
the algorithm selection process and at the same time takes into account the performance of all the
algorithms used, including the less performing ones, thereby increasing the mean accuracy of all
the algorithm accuracies.
PEER REVIEW INFORMATION
Article has been screened for originality
iThenticate
REFERENCES (23)
1.
Al Messabi, K., Aldwairi, M., Al Yousif, A., Thoban, A., Belqasmi, F. (2018). Malware detection using DNS records and domain name features. ICFNDS ‘18: Proceedings of the 2nd International Conference on Future Networks and Distributed Systems, 29, 1-7. https://doi.org/10.1145/323105....
2.
Aldwairi, M., Abu-Dalo, A.M., Jarrah, M. (2017). Pattern matching of signature-based IDS using Myers algorithm under MapReduce framework. EURASIP Journal on Information Security, 9, 1-11. https://doi.org/10.1186/s13635....
3.
Aldwairi, M., Alsaadi, H.H. (2017). Flukes: Autonomous log forensics, intelligence and visualization tool. In: Proceedings of the International Conference on Future Networks and Distributed Systems, pp. 33(133), 1-6. https://doi.org/10.1145/310230....
4.
Bachenko, J., Fitzpatrick, E., Schonwetter, M. (2008). Verification and implementation of language-based deception indicators in civil and criminal narratives. COLING ‘08: Proceedings of the 22nd International Conference on Computational Linguistics, 1, 41-48. https://doi.org/10.3115/159908....
5.
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. https://doi.org/10.1177/009365....
6.
Brambrick, N. (2018). Support Vector Machines: A Simple Explanation, KDnuggets. Access: https://www.kdnuggets.com/2016....
7.
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), pp. 9–16. https://doi.org/10.1109/ASONAM....
8.
Chen, Y., Conroy, N.J., Rubin, V.L. (2015). News in an Online World: The Need for an “Automatic Crap Detector”. In: The Proceedings of the Association for Information Science and Technology Annual Meeting (ASIST2015), 52(1), 1-4. https://doi.org/10.1002/pra2.2....
9.
Conroy, N., Rubin, V., Chen, Y. (2015). Automatic deception detection: Methods for finding fake news. Proceedings of the Association for Information Science and Technology, 52(1), 1-4. https://doi.org/10.1002/pra2.2....
10.
Fake News (2020). Wikipedia, As of May 25, 2020. Access: https: //en.wikipedia.org/wiki/Fakenews.
11.
Feng, V., Hirst, G. (2013). Detecting Deceptive Opinions with Profile Compatibility. IJCNLP, International Joint Conference on Natural Language Processing, pages 338–346, Nagoya, Japan, 14-18 October 2013. Access: https://www.aclweb.org/antholo....
12.
Hassid, J. (2011). Four models of the fourth estate: A typology of contemporary Chinese journalists. The China Quarterly, 208, 813-832. https://doi.org/10.1017/S03057..., https://doi.org/10.1145/323105....
14.
Larcker, D.F., Zakolyukina, A.A. (2012). Detecting Deceptive Discussions in Conference Calls. Journal of Accounting Research, 50(2), 495-540. https://doi.org/10.1111/j.1475....
15.
Lewis, S.C. (2011). Journalists, Social Media, and the Use of Humor on Twitter. The Electronic Journal of Communication / La Revue Electronic de Communication, 21, 1-2.
16.
Nah, F.F.H. (2015). Fake-website detection tools: Identifying elements that promote individuals use and enhance their performance 1. Introduction.
17.
Pogue, D. (2017). How to stamp out fake news. Scientific American, 316(2), 24-28. https://doi.org/10.1038/scient....
18.
Ray, S., Srivastava, T., Dar, P., Shaikh, F. (2017). Understanding Support Vector Machine algorithm from examples (along with code). Access: https://www.analyticsvidhya.co....
19.
Riedel, B., Augenstein, I., Spithourakis, G.P., Riedel, S. (2017). A simple but tough-to-beat baseline for the Fake News Challenge stance detection task. ArXiv, abs/1707.03264. Access: https://www.semanticscholar.or....
20.
Rubin, V.L. (2017). Deception Detection and Rumor Debunking for Social Media. In: L. Sloan, A. Quan-Haase (Eds.) (2017), The SAGE Handbook of Social Media Research Methods. London: SAGE. Access: https://uk.sagepub.com/en-gb/e... Access: https://core.ac.uk/download/pd....
21.
Rubin, V.L., Chen, Y., Conroy, N.J. (2015). Deception detection for news: three types of fakes. In: The Proceedings of the 78th ASIS&T Annual Meeting: Information Science with Impact: Research in and for the Community, 83, 1-4. Access: http://dl.acm.org/citation.cfm....
22.
Saxena, R. (2017). How the Naive Bayes Classifier works in Machine Learning. Access: https://dataaspirant.com/2017/....
23.
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. https://doi.org/10.1145/313759....
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