Weighted Accuracy Algorithmic Approach in Counteracting Fake News and Disinformation

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/10.1145/3231053.323108210.1145/3231053.3231082Search in Google Scholar

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/s13635-017-0062-710.1186/s13635-017-0062-7Search in Google Scholar

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/10.1145/3102304.310233710.1145/3102304.3102337Search in Google Scholar

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/10.3115/1599081.159908710.3115/1599081.1599087Search in Google Scholar

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/10.1177/009365021245360010.1177/0093650212453600Search in Google Scholar

6. Brambrick, N. (2018). Support Vector Machines: A Simple Explanation, KDnuggets. Access: https://www.kdnuggets.com/2016/07/support-vector-machines-simple-explanation.htmlSearch in Google Scholar

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.2016.775220710.1109/ASONAM.2016.7752207Search in Google Scholar

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/pra2.2015.14505201008110.1002/pra2.2015.145052010081Search in Google Scholar

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/pra2.2015.14505201008210.1002/pra2.2015.145052010082Search in Google Scholar

10. Fake News (2020). Wikipedia, As of May 25, 2020. Access: https: //en.wikipedia.org/wiki/FakenewsSearch in Google Scholar

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/anthology/I13-1039.pdfSearch in Google Scholar

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/S0305741011001019, https://doi.org/10.1145/3231053.3231082.10.1017/S0305741011001019Search in Google Scholar

13. InfoWars (2020). Wikipedia, as of July 28. Access: https://en.wikipedia.org/wiki/InfoWarsSearch in Google Scholar

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.1475-679X.2012.00450.x10.1111/j.1475-679X.2012.00450.xSearch in Google Scholar

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.Search in Google Scholar

16. Nah, F.F.H. (2015). Fake-website detection tools: Identifying elements that promote individuals use and enhance their performance 1. Introduction.Search in Google Scholar

17. Pogue, D. (2017). How to stamp out fake news. Scientific American, 316(2), 24-28. https://doi.org/10.1038/scientificamerican0217-2410.1038/scientificamerican0217-2428118338Search in Google Scholar

18. Ray, S., Srivastava, T., Dar, P., Shaikh, F. (2017). Understanding Support Vector Machine algorithm from examples (along with code). Access: https://www.analyticsvidhya.com/blog/2017/09/understaing-support-vector-machine-example-code/Search in Google Scholar

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.org/paper/A-simple-but-tough-to-beat-baseline-for-the-Fake-Riedel-Augenstein/95a72b91db11f33384fd7b159561bf182c8c8d95Search in Google Scholar

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/eur/the-sage-handbook-of-social-media-research-methods/book245370-handbook-of-social-media-research-methods/book245370Access: https://core.ac.uk/download/pdf/61692768.pdf10.4135/9781473983847.n21Search in Google Scholar

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?id=2857070.285715310.1002/pra2.2015.145052010083Search in Google Scholar

22. Saxena, R. (2017). How the Naive Bayes Classifier works in Machine Learning. Access: https://dataaspirant.com/2017/02/06/naive-bayes-classifier-machine-learning/naive-bayes-classifier-machine-learning/Search in Google Scholar

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/10.1145/3137597.313760010.1145/3137597.3137600Search in Google Scholar