HYBRID MODEL MACHINE LEARNING FOR DETECTING HOAXES
DOI:
https://doi.org/10.51903/jtie.v1i1.142Keywords:
Hybrid Model, Social Media, Machine Learning, Hoax.Abstract
Unlimited availability of content provided by users on social media and websites facilitates aggregation around a broad range of people's interests, worldviews, and common narratives. However, over time, the internet, which is a source of information, has become a source of hoaxes. Since the public is commonly flooded with information, they occasionally find it difficult to distinguish misinformation disseminated on net platforms from true information. They may also rely massively on information providers or platform social media to collect information, but these providers usually do not verify their sources.
The purpose of this research is to propose the use of machine learning techniques to establish hybrid models for detecting hoaxes. The research methodology used here is a feature extraction experiment, in which a series of features will be analyzed and grouped in an experiment to detect hoax news and hoax, especially in the political sphere by considering five modalities.
The outcome of this research indicates that the relation between publisher Prejudice and the attitude of hyper-biased news sources makes them more possible than other sources to spread illusive articles, besides that the correlation between political Prejudice and news credibility is also very strong. This shows that the experiment using a hybrid model to detect hoaxes works. well. To achieve even better results in future research, it is highly recommended to analyze user-based features in terms of attitudes, topics, or credibility.
References
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Zhiwei Jin, Juan Cao, Yongdong Zhang, and Jiebo Luo. News verification by exploiting conflicting social viewpoints in microblogs. In Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, February 12-17, 2016, Phoenix, Arizona, USA., pages 2972–2978, 2016.
Abhishek Bhati, Philip Pearce. (2016). Tourist attractions in Bangkok and Singapore; linking vandalism and setting characteristics. http://creativecommons.org/licenses/by/4.0/
Ankesh Anand, Tanmoy Chakraborty, and Noseong Park. We use neural networks to detect clickbait: You won't believe what happened next! In Advances in Information Retrieval - 39th European Conference on IR Research, ECIR 2017, Aberdeen, UK, April 8-13, 2017, Proceedings, pages 541–547, 2017.
Benjamin D Horne and Sibel Adali. 2017. This is just in: fake news packs a lot in title, uses simpler, repetitive content in the text body, more similar to satire than real news. arXiv preprint arXiv:1703.09398 (2017).32
Borella, CA, & Rossinelli, D. (2017). Fake news, Immigration, and Opinion Polarization. http://essuir.sumdu.edu.ua/handle/123456789/66314
Chau, et al. (2011). "Polonium: Tera-scale graph mining and inference for malware detection." In Proceedings of the 2011 SIAM International Conference on Data Mining, pp. 131-142. Society for Industrial and Applied Mathematics, 2011.
Cozens, P., Saville, G., & Hillier, D. (2005). Crime prevention through environmental design (CPTED): A review and modern bibliography. Property Management, 23(5), 328e356. http://dx.doi.org/10.1108/02637470510631483 .
Craig Silverman. 2016. This analysis shows how viral fake election news stories outperformed real news on Facebook. BuzzFeed News 16 (2016).
D. Zisiadis, S. Kopsidas, A. Varalis, and L. Tassiulas, “Mailbook: A social network against spamming,” in Proceedings of the 2011 International Conference for Internet Technology and Secured Transactions, ICITST 2011, pp. 245–249, are December 2011.
Ekblom, P. (2011a). Deconstructing CPTED and reconstructing it for practice, knowledge management, and research. European Journal on Criminal Policy and Research, 17(1), 7e28. http://dx.doi.org/10.1007/s10610-010-9132-9 .
Fyall, A., Garrod, B., Leask, A., & Wanhill, S. (2008). Conclusion. In A. Fyall, B. Garrod, A. Leask, & S. Wanhill (Eds.), Managing visitor attractions (2nd ed., pp. 347e353). Oxford: Butterworth-Heinemann.
James W Pennebaker, Ryan L Boyd, Kayla Jordan, and Kate Blackburn. 2015. The development and psychometric properties of LIWC 2015. Technical Report.
Jin, et al. (2017). "Novel visual and statistical image features for microblog news verification." IEEE transactions on multimedia 19, no. 3 (2017): 598-608.
Kai Shu, Amy Sliva, Suhang Wang, Jiliang Tang, and Huan Liu. 2017. Fake news detection on social media: A data mining perspective. ACM SIGKDD Explorations Newsletter 19, 1 (2017), 22–36.
Kai Shu, Suhang Wang, and Huan Liu. 2017. Exploiting Tri-Relationship for Fake News Detection. arXiv preprint arXiv:1712.07709 (2017).
Maksym Gabielkov, Arthi Ramachandran, Augustin Chaintreau, and Arnaud Legout. 2016. Social clicks: What and who gets read on Twitter? ACM SIGMET- RICS Performance Evaluation Review 44, 1 (2016), 179–192.
Manish Gupta, Peixiang Zhao, and Jiawei Han. 2012. Evaluating event credibility on Twitter. In Proceedings of the 2012 SIAM International Conference on Data Mining. SIAM, 153–164.
Martin Potthast, Sebastian Ko ̈psel, Benno Stein, and Matthias Hagen. Clickbait detection. In Advances in Information Retrieval - 38th European Conference on IR Research, ECIR 2016, Padua, Italy, March 20-23, 2016. Proceedings, pages 810–817, 2016.
Natali Ruchansky, Sungyong Seo, and Yan Liu. CSI: A hybrid deep model for fake news detection. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, CIKM 2017, Singapore, November 06 - 10, 2017, pages 797–806, 2017.
Oriol Vinyals, Alexander Toshev, Samy Bengio, and Dumitru Erhan. 2017. Show and Tell: Lessons Learned from the 2015 MSCOCO Image Captioning Challenge. IEEE Trans. Anal Pattern. Mach. Intell. 39, 4 (2017), 652–663.
Potthast, et al. (2017). "A Stylometric Inquiry into Hyperpartisan and Fake news." arXiv preprint arXiv:1702.05638 (2017).
Shearer and Gottfried. 2017. News Use Across Social Media Platforms 2017. (2017).
Symeon Papadopoulos Duc-Tien Dang-Nguyen Giulia Boato Michael Riegler Yiannis Kompatsiaris et al. Christina Boididou, Katerina Andreadou. 2015. Verifying Multimedia Use at MediaEval 2015. In MediaEval.
Tanushree Mitra and Eric Gilbert. 2015. CREDBANK: A Large-Scale Social Media Corpus With Associated Credibility Annotations. In Proceedings of the Ninth International Conference on Web and Social Media, ICWSM 2015, University of Oxford, Oxford, UK, May 26-29, 2015. 258–267.
William Yang Wang. 2017. "Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detection. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, ACL 2017, Vancouver, Canada, July 30 - August 4, Volume 2: Short Papers. 422–426.
Wilson, JQ, & Kelling, GL (1982). Broken windows: Police and neighborhood security. Monthly Atlantic, 249, 29e38.
Wortley, R., & Mazerolle, L. (2012). Environmental criminology and crime analysis. Hoboken: Taylor and Francis.
Xin Liu, Pingjun Zou, Weishan Zhang, Jiehan Zhou, Changying Dai, Feng Wang, Xiaomiao Zhang, "CPSFS: A Credible Personalized Spam Filtering Scheme by Crowdsourcing", Wireless Communications and Mobile Computing, vol. 2017, Article ID 1457870, 9 pages, 2017. https://doi.org/10.1155/2017/1457870
Yimin Chen, Niall J. Conroy, and Victoria L. Rubin. Misleading online content: Recognizing clickbait as ”false news”. In Proceedings of the 2015 ACM Workshop on Multimodal Deception Detection, WMDD@ICMI 2015, Seattle, Washington, USA, November 13, 2015, pages 15–19, 2015.
Zhang, Y., Guan, J., Zhou, S., & Zhang, Z. (2013). Rumor evolution in social networks. Physical Review E - Statistical, Nonlinear, and Soft Matter Physics, 87 (3), Article 032133. (2013). http://discovery.ucl.ac.uk/1387962/1/PhysRevE.87.032133.pdf
Zhiwei Jin, Juan Cao, Yongdong Zhang, and Jiebo Luo. News verification by exploiting conflicting social viewpoints in microblogs. In Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, February 12-17, 2016, Phoenix, Arizona, USA., pages 2972–2978, 2016.
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Published
2022-04-26
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HYBRID MODEL MACHINE LEARNING FOR DETECTING HOAXES. (2022). Journal of Technology Informatics and Engineering, 1(1), 30-49. https://doi.org/10.51903/jtie.v1i1.142
