Fake News Analysis Using Machine Learning
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Hadeer Ahmed, Issa Traore, et al. Detection of online fake news using n-gram analysis and machine learning techniques. International Conference on Intelligent, Secure, and Dependable Systems in Distributed and Cloud Environments. 2017.
Hunt Allcott and Matthew Gentzkow. Social media and fake news in the 2016 election. Journal of Economic Perspectives. 2017;31(2):211-236.
Yimin Chen, Niall J Conroy, et al. Misleading online content: Recognizing clickbait as false news. Proceedings of the 2015 ACM on Workshop on Multimodal Deception Detection. 2015.
Mathieu Cliche. The sarcasm detector. https://www.thesarcasmdetector.com/about. 2014.
Johannes F¨urnkranz. A study using n-gram features for text categorization. Austrian Research Institute for Artifical Intelligence. 1998;3(1998):1–10.
Peter Bourgonje, Julian Moreno Schneider, et al. From clickbait to fake news detection: an approach based on detecting the stance of headlines to articles. Proceedings of the 2017 EMNLP Workshop: Natural Language Processing meets Journalism. 2017.
Song Feng, Ritwik Banerjee, et al. Syntactic stylometry for deception detection. In Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics. 2012;2:171-175.
Ángel Hernández-Castañeda and Hiram Calvo. Deceptive text detection using continuous semantic space models. Intelligent Data Analysis. 2017;21(3):679-695.
Ethar Qawasmeh, Mais Tawalbeh, et al. Automatic Identification of Fake News Using Deep Learning. 2019 Sixth International Conference on Social Networks Analysis, Management and Security (SNAMS). 22-25 Oct. 2019; Granada, Spain, New York: IEEE; 2019.
William Yang Wang. “Liar, Liar Pants on Fire”: A New Benchmark Dataset for Fake News Detection. Computation and Language. https://arxiv.org/abs/1705.00648. 2017.
Costin BUSIOC, Stefan RUSETI, Mihai DASCALU. A Literature Review of NLP Approaches to Fake News Detection and Their Applicability to Romanian Language News Analysis. 2020, Romanian Ministry of Education and Research, CNCS - UEFISCDI, project number PN-III-P1-1.1-TE-2019-1794, within PNCDI III.
Alim Al Ayub Ahmed, Ayman Aljabouh, et al. Detecting Fake News using Machine Learning: A Systematic Literature Review. Computers and Society. 2021. https://arxiv.org/abs/2102.04458.
Razan Masood and Ahmet Aker. The Fake News Challenge: Stance Detection using Traditional Machine Learning Approaches. KMIS 2018 - 10th International Conference on Knowledge Management and Information Sharing. 2018.
Sohan De Sarkar, Fan Yang, et al. Attending Sentences to detect Satirical Fake News. Proceedings of the 27th International Conference on Computational Linguistics. Association for Computational Linguistics. August 20-26, 2018. Santa Fe, New Mexico, USA.
Abdullah-All-Tanvir, Ehesas Mia Mahir, et al. Detecting Fake News using Machine Learning and Deep Learning Algorithms. 2019 7th International Conference on Smart Computing & Communications (ICSCC). 28-30 June 2019. Sarawak, Malaysia, New York: IEEE; 2019.
Nadia K. Conroy, Victoria L. Rubin, et al. Automatic deception detection: Methods for finding fake news. Proceedings of the Association for Information Science and Technology. 2015;52(1):1-4.
Ethan Fast, Binbin Chen, et al. Empath: Understanding topic signals in large-scale text. Computation and Language. 2016. https://arxiv.org/abs/1602.06979.
Mykhailo Granik and Volodymyr Mesyura. Fake news detection using naive bayes classifier. 2017 IEEE First Ukraine Conference on Electrical and Computer Engineering (UKRCON). 29 May-2 June 2017; Kyiv, Ukraine, New York: IEEE; 2017.
Ángel Hernández-Castañeda and Hiram Calvo. Deceptive text detection using continuous semantic space models. Intelligent Data Analysis. 2017;21(3):679-695.
Johan Hovold. Naive Bayes spam filtering using word-position-based attributes and length-sensitive classification thresholds. Proceedings of the 15th NODALIDA conference. Joensuu 2005.
Armand Joulin, Edouard Grave, et al. Bag of tricks for efficient text classification. 2016. Computation and Language. https://arxiv.org/abs/1607.01759.
DOI: https://doi.org/10.37628/ijods.v7i1.720
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