Manolito Villagracia Octaviano
ABSTRACT
Fake news has been an issue in every generateon. As the technology evolves, the problem of detecting accurate data for the unreliable news evolves with it and its resolution becomes more significant. This paper explores various feature sets, wherein two new features are introduced to develop an automated fake news detector on news articles. Experiment obtained 96.60% on using XGBoost that has been noted to be comparable to existing works.
- David Westerman, Patric R Spence, and Brandon Van Der Heide. 2014. Social media as information source: Recency of updates and credibility of information. Journal of computer-mediated communication 19, 2 (2014), 171–183.Google Scholar
Digital Library
- 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.Google ScholarDigital Library
- Supanya Aphiwongsophon and Prabhas Chongstitvatana. 2018. Detecting fake news with machine learning method. In 2018 15th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON). IEEE, 528–531.Google ScholarCross Ref
- Junaed Younus Khan, Md Khondaker, Tawkat Islam, Anindya Iqbal, and Sadia Afroz. 2019. A benchmark study on machine learning methods for fake news detection. arXiv preprint arXiv:1905.04749 (2019).Google Scholar
- Jeremiah Joven B Joaquin, Hazel T Biana, and Mark Anthony Dacela. 2020. The Philippine Higher Education Sector in the Time of COVID-19. In Frontiers in Education, Vol. 5. Frontiers, 208.Google ScholarCross Ref
- Kevin M Kniffin, Jayanth Narayanan, Frederik Anseel, John Antonakis, Susan P Ashford, Arnold B Bakker, Peter Bamberger, Hari Bapuji, Devasheesh P Bhave, Virginia K Choi, 2021. COVID-19 and the workplace: Implications, issues, and insights for future research and action. American Psychologist 76, 1 (2021), 63.Google ScholarCross Ref
- Hadeer Ahmed, Issa Traore, and Sherif Saad. 2017. Detection of online fake news using n-gram analysis and machine learning techniques. In International conference on intelligent, secure, and dependable systems in distributed and cloud environments. Springer, 127–138.Google ScholarCross Ref
- Vanya Tiwari, Ruth G Lennon, and Thomas Dowling. 2020. Not Everything You Read Is True! Fake News Detection using Machine learning Algorithms. In 2020 31st Irish Signals and Systems Conference (ISSC). IEEE, 1–4.Google ScholarCross Ref
- Vinícius Barbosa, Carina de Oliveira, and Reinaldo Bezerra Braga. 2020. AuFa-Automatic Detection and Classification of Fake News Using Neural networks. In 8th International Workshop on ADVANCEs in ICT Infrastructures and Services (ADVANCE 2020). 1–8.Google Scholar
- Vian Sabeeh, Mohammed Zohdy, and Rasha Al Bashaireh. 2019. Enhancing the Fake News Detection by Applying Effective Feature Selection Based on Semantic Sources. In 2019 International Conference on Computational Science and Computational Intelligence (CSCI). IEEE, 1365–1370.Google ScholarCross Ref
- Georgios Gravanis, Athena Vakali, Konstantinos Diamantaras, and Panagiotis Karadais. 2019. Behind the cues: A benchmarking study for fake news detection. Expert Systems with Applications 128 (2019), 201–213.Google ScholarDigital Library
- Tom Young, Devamanyu Hazarika, Soujanya Poria, and Erik Cambria. 2018. Recent trends in deep learning based natural language processing. ieee Computational intelligenCe magazine 13, 3 (2018), 55–75.Google Scholar
- Yang Yang, Lei Zheng, Jiawei Zhang, Qingcai Cui, Zhoujun Li, and Philip S Yu. 2018. TI-CNN: Convolutional neural networks for fake news detection. arXiv preprint arXiv:1806.00749 (2018).Google Scholar
- Rohit Kumar Kaliyar, Anurag Goswami, Pratik Narang, and Soumendu Sinha. 2020. FNDNet–a deep convolutional neural network for fake news detection. Cognitive Systems Research 61 (2020), 32–44.Google ScholarDigital Library
- Pritika Bahad, Preeti Saxena, and Raj Kamal. 2019. Fake News Detection using Bi-directional LSTM-Recurrent Neural Network. Procedia Computer Science 165 (2019), 74–82.Google ScholarDigital Library
- Natali Ruchansky, Sungyong Seo, and Yan Liu. 2017. Csi: A hybrid deep model for fake news detection. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. 797–806.Google ScholarDigital Library
- W James Murdoch, Chandan Singh, Karl Kumbier, Reza Abbasi-Asl, and Bin Yu. 2019. Definitions, methods, and applications in interpretable machine learning. Proceedings of the National Academy of Sciences 116, 44 (2019), 22071– 22080.Google ScholarCross Ref
- Bilal Ghanem, Paolo Rosso, and Francisco Rangel. 2020. An emotional analysis of false information in social media and news articles. ACM Transactions on Internet Technology (TOIT) 20, 2 (2020), 1–18.Google ScholarDigital Library
- Jeannette Paschen. 2019. Investigating the emotional appeal of fake news using artificial intelligence and human contributions. Journal of Product & Brand Management (2019).Google Scholar
- James W Pennebaker, Ryan L Boyd, Kayla Jordan, and Kate Blackburn. 2015. The development and psychometric properties of LIWC2015. Technical Report.Google Scholar
- Matteo Cardaioli, Stefano Cecconello, Mauro Conti, Luca Pajola, and Federico Turrin. 2020. Fake news spreaders profiling through behavioural analysis. In CLEF.Google Scholar
- Raffaele Manna, Antonio Pascucci, and Johanna Monti. 2020. Profiling fake news spreaders through stylometry and lexical features. UniOR NLP@ PAN2020. In CLEF.Google Scholar
- Myle Ott, Yejin Choi, Claire Cardie, and Jeffrey T Hancock. 2011. Finding deceptive opinion spam by any stretch of the imagination. arXiv preprint arXiv:1107.4557 (2011).vGoogle ScholarDigital Library
- Victoria L Rubin, Niall Conroy, Yimin Chen, and Sarah Cornwell. 2016. Fake news or truth? using satirical cues to detect potentially misleading news. In Proceedings of the second workshop on computational approaches to deception detection. 7–17.Google ScholarCross Ref
- Abhijnan Chakraborty, Bhargavi Paranjape, Sourya Kakarla, and Niloy Ganguly. 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, 9–16.Google ScholarCross Ref
- Tianqi Chen and Carlos Guestrin. 2016. Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining. 785–794.Google ScholarDigital Library
- Scott Lundberg and Su-In Lee. 2017. A unified approach to interpreting model predictions. arXiv preprint arXiv:1705.07874 (2017).Google Scholar
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