Xinyi Zhou
Vir V. Phoha
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Abstract
Massive dissemination of fake news and its potential to erode democracy has increased the demand for accurate fake news detection. Recent advancements in this area have proposed novel techniques that aim to detect fake news by exploring how it propagates on social networks. Nevertheless, to detect fake news at an early stage, i.e., when it is published on a news outlet but not yet spread on social media, one cannot rely on news propagation information as it does not exist. Hence, there is a strong need to develop approaches that can detect fake news by focusing on news content. In this article, a theory-driven model is proposed for fake news detection. The method investigates news content at various levels: lexicon-level, syntax-level, semantic-level, and discourse-level. We represent news at each level, relying on well-established theories in social and forensic psychology. Fake news detection is then conducted within a supervised machine learning framework. As an interdisciplinary research, our work explores potential fake news patterns, enhances the interpretability in fake news feature engineering, and studies the relationships among fake news, deception/disinformation, and clickbaits. Experiments conducted on two real-world datasets indicate the proposed method can outperform the state-of-the-art and enable fake news early detection when there is limited content information.
- Amol Agrawal. 2016. Clickbait detection using deep learning. In Proceedings of the 2nd International Conference on Next Generation Computing Technologies (NGCT’16). IEEE, 268--272.Google Scholar
- Sanjeev Arora, Yingyu Liang, and Tengyu Ma. 2016. A simple but tough-to-beat baseline for sentence embeddings. In The International Conference on Learning Representations (ICLR’17).Google Scholar
- Péter Bálint and Géza Bálint. 2009. The Semmelweis-reflex. Orvosi Het. 150, 30 (2009), 1430.Google ScholarCross Ref
- Lawrence E. Boehm. 1994. The validity effect: A search for mediating variables. Person. Soc. Psychol. Bull. 20, 3 (1994), 285--293.Google ScholarCross Ref
- Finn Brunton. 2013. Spam: A Shadow History of the Internet. The Mit Press.Google ScholarCross Ref
- Sonia Castelo, Thais Almeida, Anas Elghafari, Aécio Santos, Kien Pham, Eduardo Nakamura, and Juliana Freire. 2019. A topic-agnostic approach for identifying fake news pages. In Proceedings of the World Wide Web Conference. ACM, 975--980.Google Scholar
- Carlos Castillo, Marcelo Mendoza, and Barbara Poblete. 2011. Information credibility on Twitter. In Proceedings of the 20th International Conference on World Wide Web. ACM, 675--684.Google ScholarDigital Library
- Abhijnan Chakraborty, Bhargavi Paranjape, Sourya Kakarla, and Niloy Ganguly. 2016. Stop clickbait: Detecting and preventing clickbaits in online news media. In Proceedings of the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. IEEE Press, 9--16.Google ScholarCross Ref
- Abhijnan Chakraborty, Rajdeep Sarkar, Ayushi Mrigen, and Niloy Ganguly. 2017. Tabloids in the era of social media?: Understanding the production and consumption of clickbaits in Twitter. Proc. ACM on Hum.-comput. Interact. 1, CSCW (2017), 30.Google Scholar
- 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. ACM, 785--794.Google ScholarDigital Library
- Yimin Chen, Niall J. Conroy, and Victoria L. Rubin. 2015. Misleading online content: Recognizing clickbait as false news. In Proceedings of the ACM Workshop on Multimodal Deception Detection. ACM, 15--19.Google Scholar
- Giovanni Luca Ciampaglia, Prashant Shiralkar, Luis M. Rocha, Johan Bollen, Filippo Menczer, and Alessandro Flammini. 2015. Computational fact checking from knowledge networks. PloS One 10, 6 (2015), e0128193.Google ScholarCross Ref
- Manqing Dong, Lina Yao, Xianzhi Wang, Boualem Benatallah, and Chaoran Huang. 2019. Similarity-aware deep attentive model for clickbait detection. In Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer, 56--69.Google Scholar
- Xin Dong, Evgeniy Gabrilovich, Geremy Heitz, Wilko Horn, Ni Lao, Kevin Murphy, Thomas Strohmann, Shaohua Sun, and Wei Zhang. 2014. Knowledge vault: A web-scale approach to probabilistic knowledge fusion. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 601--610.Google ScholarDigital Library
- Mengnan Du, Ninghao Liu, and Xia Hu. 2019. Techniques for interpretable machine learning. Commun. ACM 63, 1 (2019), 68--77.Google ScholarDigital Library
- Song Feng, Ritwik Banerjee, and Yejin Choi. 2012. Syntactic stylometry for deception detection. In Proceedings of the 50th Meeting of the Association for Computational Linguistics: Short Papers-Volume 2. Association for Computational Linguistics, 171--175.Google ScholarDigital Library
- Alison Gianotto. 2014. Downworthy: A browser plugin to turn hyperbolic viral headlines into what they really mean. downworthy.snipe.net/. (2014).Google Scholar
- Manish Gupta, Peixiang Zhao, and Jiawei Han. 2012. Evaluating event credibility on Twitter. In Proceedings of the SIAM International Conference on Data Mining. SIAM, 153--164.Google ScholarCross Ref
- Shashank Gupta, Raghuveer Thirukovalluru, Manjira Sinha, and Sandya Mannarswamy. 2018. CIMTDetect: A community infused matrix-tensor coupled factorization based method for fake news detection. Arxiv Preprint Arxiv:1809.05252 (2018).Google Scholar
- Joan B. Hooper. 1974. On assertive predicates. In Syntax and Semantics, Vol. 4. Indiana University Linguistics Club.Google Scholar
- Kokil Jaidka, Tanya Goyal, and Niyati Chhaya. 2018. Predicting email and article clickthroughs with domain-adaptive language models. In Proceedings of the 10th ACM Conference on Web Science. ACM, 177--184.Google Scholar
- Yangfeng Ji and Jacob Eisenstein. 2014. Representation learning for text-level discourse parsing. In Proceedings of the 52nd Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Vol. 1. 13--24.Google Scholar
- Zhiwei Jin, Juan Cao, Yongdong Zhang, and Jiebo Luo. 2016. News verification by exploiting conflicting social viewpoints in microblogs. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI’16). 2972--2978.Google Scholar
- Marcia K. Johnson and Carol L. Raye. 1981. Reality monitoring.Psychol. Rev. 88, 1 (1981), 67.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
- Quoc Le and Tomas Mikolov. 2014. Distributed representations of sentences and documents. In Proceedings of the International Conference on Machine Learning. 1188--1196.Google ScholarDigital Library
- Yang Liu and Yi-Fang Brook Wu. 2018. Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence.Google Scholar
- George Loewenstein. 1994. The psychology of curiosity: A review and reinterpretation. Psychol. Bull. 116, 1 (1994), 75.Google ScholarCross Ref
- Colin MacLeod, Andrew Mathews, and Philip Tata. 1986. Attentional bias in emotional disorders.J. Abnorm. Psychol. 95, 1 (1986), 15.Google ScholarCross Ref
- Steven A. McCornack, Kelly Morrison, Jihyun Esther Paik, Amy M. Wisner, and Xun Zhu. 2014. Information manipulation theory 2: A propositional theory of deceptive discourse production. J. Lang. Soc. Psychol. 33, 4 (2014), 348--377.Google ScholarCross Ref
- Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Efficient estimation of word representations in vector space. Arxiv Preprint Arxiv:1301.3781 (2013).Google Scholar
- Federico Monti, Fabrizio Frasca, Davide Eynard, Damon Mannion, and Michael M. Bronstein. 2019. Fake news detection on social media using geometric deep learning. Arxiv Preprint Arxiv:1902.06673 (2019).Google Scholar
- Maximilian Nickel, Kevin Murphy, Volker Tresp, and Evgeniy Gabrilovich. 2016. A review of relational machine learning for knowledge graphs. Proc. IEEE 104, 1 (2016), 11--33.Google ScholarCross Ref
- Raymond S. Nickerson. 1998. Confirmation bias: A ubiquitous phenomenon in many guises. Rev. Gen. Psychol. 2, 2 (1998), 175.Google ScholarCross Ref
- Jeppe Nørregaard, Benjamin D. Horne, and Sibel Adalı. 2019. NELA-GT-2018: A large multi-labelled news dataset for the study of misinformation in news articles. In Proceedings of the International AAAI Conference on Web and Social Media, Vol. 13. 630--638.Google ScholarCross Ref
- Ray Oshikawa, Jing Qian, and William Yang Wang. 2018. A survey on natural language processing for fake news detection. Arxiv Preprint Arxiv:1811.00770 (2018).Google Scholar
- Shivam B. Parikh and Pradeep K. Atrey. 2018. Media-rich fake news detection: A survey. In Proceedings of the IEEE Conference on Multimedia Information Processing and Retrieval (MIPR’18). IEEE, 436--441.Google Scholar
- Shivam B. Parikh, Vikram Patil, Ravi Makawana, and Pradeep K. Atrey. 2019. Towards impact scoring of fake news. In Proceedings of the IEEE Conference on Multimedia Information Processing and Retrieval (MIPR’19). IEEE, 529--533.Google Scholar
- James W. Pennebaker, Ryan L. Boyd, Kayla Jordan, and Kate Blackburn. 2015. The Development and Psychometric Properties of LIWC’15. Technical Report. The University of Texas at Austin.Google Scholar
- Verónica Pérez-Rosas, Bennett Kleinberg, Alexandra Lefevre, and Rada Mihalcea. 2017. Automatic detection of fake news. Arxiv Preprint Arxiv:1708.07104 (2017).Google Scholar
- Martin Potthast, Johannes Kiesel, Kevin Reinartz, Janek Bevendorff, and Benno Stein. 2017. A stylometric inquiry into hyperpartisan and fake news. Arxiv Preprint Arxiv:1702.05638 (2017).Google Scholar
- Martin Potthast, Sebastian Köpsel, Benno Stein, and Matthias Hagen. 2016. Clickbait detection. In Proceedings of the European Conference on Information Retrieval. Springer, 810--817.Google Scholar
- Kenneth Rapoza. 2017. Can “fake news” impact the stock market? Retrieved from www.forbes.com/sites/kenrapoza/2017/02/26/can-fake-news-impact-the-stock-market/ (9. 7. 2018).Google Scholar
- Marta Recasens, Cristian Danescu-Niculescu-Mizil, and Dan Jurafsky. 2013. Linguistic models for analyzing and detecting biased language. In Proceedings of the 51st Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 1650--1659.Google Scholar
- Victoria L. Rubin. 2010. On deception and deception detection: Content analysis of computer-mediated stated beliefs. Proc. Assoc. Inf. Sci. Technol. 47, 1 (2010), 1--10.Google ScholarCross Ref
- Victoria L. Rubin and Tatiana Lukoianova. 2015. Truth and deception at the rhetorical structure level. J. Assoc. Inf. Sci. Technol. 66, 5 (2015), 905--917.Google ScholarDigital Library
- Natali Ruchansky, Sungyong Seo, and Yan Liu. 2017. CSI: A hybrid deep model for fake news detection. In Proceedings of the ACM Conference on Information and Knowledge Management. ACM, 797--806.Google ScholarDigital Library
- Baoxu Shi and Tim Weninger. 2016. Discriminative predicate path mining for fact checking in knowledge graphs. Knowl-based Syst. 104 (2016), 123--133.Google ScholarDigital Library
- Kai Shu, Limeng Cui, Suhang Wang, Dongwon Lee, and Huan Liu. 2019. dEFEND: Explainable fake news detection. In Proceedings of the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. IEEE Press.Google Scholar
- Kai Shu, Deepak Mahudeswaran, Suhang Wang, Dongwon Lee, and Huan Liu. 2018. FakeNewsNet: A data repository with news content, social context, and dynamic information for studying fake news on social media. Arxiv Preprint Arxiv:1809.01286 (2018).Google Scholar
- Kai Shu, Suhang Wang, and Huan Liu. 2019. Beyond news contents: The role of social context for fake news detection. In Proceedings of the 12th ACM International Conference on Web Search and Data Mining. ACM, 312--320.Google ScholarDigital Library
- Craig Silverman. 2016. This analysis shows how viral fake election news stories outperformed real news on Facebook. BuzzFeed News 16 (2016).Google Scholar
- Niraj Sitaula, Chilukuri K. Mohan, Jennifer Grygiel, Xinyi Zhou, and Reza Zafarani. 2019. Credibility-based fake news detection. Arxiv Preprint Arxiv:1911.00643 (2019).Google Scholar
- Amos Tversky and Daniel Kahneman. 1974. Judgment under uncertainty: Heuristics and biases. Science 185, 4157 (1974), 1124--1131.Google Scholar
- Udo Undeutsch. 1967. Beurteilung der glaubhaftigkeit von aussagen. Handb. Psychol. 11 (1967), 26--181.Google Scholar
- Soroush Vosoughi, Deb Roy, and Sinan Aral. 2018. The spread of true and false news online. Science 359, 6380 (2018), 1146--1151.Google Scholar
- William Yang Wang. 2017. “Liar, liar pants on fire”: A new benchmark dataset for fake news detection. Arxiv Preprint Arxiv:1705.00648 (2017).Google Scholar
- Yaqing Wang, Fenglong Ma, Zhiwei Jin, Ye Yuan, Guangxu Xun, Kishlay Jha, Lu Su, and Jing Gao. 2018. EANN: Event adversarial neural networks for multi-modal fake news detection. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery 8 Data Mining. ACM, 849--857.Google ScholarDigital Library
- Ke Wu, Song Yang, and Kenny Q. Zhu. 2015. False rumors detection on Sina Eeibo by propagation structures. In Proceedings of the IEEE 31st International Conference on Data Engineering (ICDE’15). IEEE, 651--662.Google Scholar
- Reza Zafarani, Mohammad Ali Abbasi, and Huan Liu. 2014. Social Media Mining: An Introduction. Cambridge University Press.Google ScholarDigital Library
- Reza Zafarani, Xinyi Zhou, Kai Shu, and Huan Liu. 2019. Fake news research: Theories, detection strategies, and open problems. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery 8 Data Mining. ACM, 3207--3208.Google Scholar
- Amy X. Zhang, Aditya Ranganathan, Sarah Emlen Metz, Scott Appling, Connie Moon Sehat, Norman Gilmore, Nick B. Adams, Emmanuel Vincent, Jennifer Lee, Martin Robbins, et al. 2018. A structured response to misinformation: Defining and annotating credibility indicators in news articles. In Proceedings of the Web Conference. International World Wide Web Conferences Steering Committee, 603--612.Google Scholar
- Jiawei Zhang, Limeng Cui, Yanjie Fu, and Fisher B. Gouza. 2018. Fake news detection with deep diffusive network model. Arxiv Preprint Arxiv:1805.08751 (2018).Google Scholar
- Xinyi Zhou, Jindi Wu, and Reza Zafarani. 2020. SAFE: Similarity-aware multi-modal fake news detection. In Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer.Google Scholar
- Xinyi Zhou and Reza Zafarani. 2018. Fake news: A survey of research, detection methods, and opportunities. Arxiv Preprint Arxiv:1812.00315 (2018).Google Scholar
- Xinyi Zhou and Reza Zafarani. 2019. Network-based fake news detection: A pattern-driven approach. SIGKDD Explor. 21, 2 (2019), 48--60.Google ScholarDigital Library
- Miron Zuckerman, Bella M. DePaulo, and Robert Rosenthal. 1981. Verbal and nonverbal communication of deception. In Proceedings of the Advances in Experimental Social Psychology. Vol. 14. Elsevier, 1--59.Google Scholar
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- Fake News Early Detection: A Theory-driven Model
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