Nujud Aloshban
ABSTRACT
Automatic detection of fake news is an important issue given the disproportionate effect of fake news on democratic processes, individuals and institutions. Research on automated fact-checking has proposed different approaches based on traditional machine learning methods, using hand-crafted lexical features. Nevertheless, these approaches focus on analyzing the text claim without considering the facts that are not explicitly given but can be derived from it. For example, external evidence that is retrieved from the Web as a knowledge source of the claim can provide complementary context of the claim and gives convincing reasons from it to support or oppose. Recent approaches study this deficit by incorporating supportive evidence (article) corresponding to the claim. However, these methods are either requiring substantial feature modeling, not considering several supporting evidences, or even not analyzing the language of the supporting evidence deeply.
To this end, we propose an end-to-end framework, named Automatic Fake News Classification Through Self-Attention (ACT), which exploits different supportive articles to a claim which mimics manual fact-checking processes. The model presents an approach that computes the claim credibility by aggregating over the prediction generated by every claim-retrieved article pair. The article input is represented by using self-attention on the top of a bidirectional LSTM neural network. By using the self-attention, the model concentrates on nuanced linguistic features and does not require any feature engineering, lexicons or any other manual intervention. Moreover, different aspects of the supporting article are extracted into multiple vector representations. Hence, different meaningful article representations can be extracted into a two-dimensional matrix to represent the article. In the end, a majority vote over the several external articles of a given claim is applied to assess the claim’s credibility. We conduct experiments on three different real-world datasets, compare them to the state-of-the-art approaches and analyze our results, which shows performance improvements.
Supplemental Material
- Hunt Allcott, Matthew Gentzkow, and Chuan Yu. 2019. Trends in the diffusion of misinformation on social media. Research & Politics 6, 2 (2019), 2053168019848554.Google Scholar
Cross Ref
- Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014. Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473(2014).Google Scholar
- Yoshua Bengio, Patrice Y. Simard, and Paolo Frasconi. 1994. Learning long-term dependencies with gradient descent is difficult. IEEE Trans. Neural Networks 5, 2 (1994), 157–166.Google ScholarDigital Library
- Petter Bae Brandtzæg and Asbjørn Følstad. 2017. Trust and distrust in online fact-checking services. Commun. ACM 60, 9 (2017), 65–71.Google ScholarDigital Library
- Tong Chen, Xue Li, Hongzhi Yin, and Jun Zhang. 2018. Call attention to rumors: Deep attention based recurrent neural networks for early rumor detection. In Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer, 40–52.Google ScholarCross Ref
- Jianpeng Cheng, Li Dong, and Mirella Lapata. 2016. Long Short-Term Memory-Networks for Machine Reading. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, EMNLP 2016, Austin, Texas, USA, November 1-4, 2016. 551–561.Google ScholarCross Ref
- Jan K Chorowski, Dzmitry Bahdanau, Dmitriy Serdyuk, Kyunghyun Cho, and Yoshua Bengio. 2015. Attention-based models for speech recognition. In Advances in neural information processing systems. 577–585.Google Scholar
- Ronan Collobert, Jason Weston, Léon Bottou, Michael Karlen, Koray Kavukcuoglu, and Pavel P. Kuksa. 2011. Natural Language Processing (almost) from Scratch. CoRR abs/1103.0398(2011). http://arxiv.org/abs/1103.0398Google Scholar
- Cícero Nogueira dos Santos and Maira Gatti. 2014. Deep Convolutional Neural Networks for Sentiment Analysis of Short Texts. In COLING 2014, 25th International Conference on Computational Linguistics, Proceedings of the Conference: Technical Papers, August 23-29, 2014, Dublin, Ireland. 69–78.Google Scholar
- Cícero Nogueira dos Santos, Ming Tan, Bing Xiang, and Bowen Zhou. 2016. Attentive Pooling Networks. abs/1602.03609 (2016). http://arxiv.org/abs/1602.03609Google Scholar
- Felix A Gers, Nicol N Schraudolph, and Jürgen Schmidhuber. 2002. Learning precise timing with LSTM recurrent networks. Journal of machine learning research 3, Aug (2002), 115–143.Google Scholar
- Naeemul Hassan, Bill Adair, James T Hamilton, Chengkai Li, Mark Tremayne, Jun Yang, and Cong Yu. 2015. The quest to automate fact-checking. world (2015).Google Scholar
- Naeemul Hassan, Gensheng Zhang, Fatma Arslan, Josue Caraballo, Damian Jimenez, Siddhant Gawsane, Shohedul Hasan, Minumol Joseph, Aaditya Kulkarni, Anil Kumar Nayak, Vikas Sable, Chengkai Li, and Mark Tremayne. 2017. ClaimBuster: The First-ever End-to-end Fact-checking System. PVLDB 10, 12 (2017), 1945–1948.Google ScholarDigital Library
- Stefan Helmstetter and Heiko Paulheim. 2018. Weakly Supervised Learning for Fake News Detection on Twitter. In IEEE/ACM 2018 International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018, Barcelona, Spain, August 28-31, 2018. 274–277.Google Scholar
- Karl Moritz Hermann, Tomas Kocisky, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom. 2015. Teaching machines to read and comprehend. In Advances in Neural Information Processing Systems. 1693–1701.Google Scholar
- Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural computation 9, 8 (1997), 1735–1780.Google ScholarDigital Library
- Georgi Karadzhov, Preslav Nakov, Lluís Màrquez, Alberto Barrón-Cedeño, and Ivan Koychev. 2017. Fully Automated Fact Checking Using External Sources. In Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017, Varna, Bulgaria, September 2 - 8, 2017. 344–353.Google ScholarCross Ref
- Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980(2014).Google Scholar
- Lars Konieczny. 2016. Locality and parsing complexity. Journal of psycholinguistic research 29 (2016), 627–645. Issue 2.Google ScholarCross Ref
- Peng Li, Wei Li, Zhengyan He, Xuguang Wang, Ying Cao, Jie Zhou, and Wei Xu. 2016. Dataset and Neural Recurrent Sequence Labeling Model for Open-Domain Factoid Question Answering. CoRR abs/1607.06275(2016). http://arxiv.org/abs/1607.06275 Withdrawn.Google Scholar
- Zhouhan Lin, Minwei Feng, Cícero Nogueira dos Santos, Mo Yu, Bing Xiang, Bowen Zhou, and Yoshua Bengio. 2017. A Structured Self-attentive Sentence Embedding. CoRR abs/1703.03130(2017). http://arxiv.org/abs/1703.03130Google Scholar
- Charles X Ling, Jin Huang, Harry Zhang, 2003. AUC: a statistically consistent and more discriminating measure than accuracy. In IJCAI, Vol. 3. 519–524.Google Scholar
- Jing Ma, Wei Gao, and Kam-Fai Wong. 2017. Detect Rumors in Microblog Posts Using Propagation Structure via Kernel Learning. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, ACL 2017, Vancouver, Canada, July 30 - August 4, Volume 1: Long Papers. 708–717.Google ScholarCross Ref
- Delia Mocanu, Luca Rossi, Qian Zhang, Marton Karsai, and Walter Quattrociocchi. 2015. Collective attention in the age of (mis) information. Computers in Human Behavior 51 (2015), 1198–1204.Google ScholarDigital Library
- Kashyap Popat. 2017. Assessing the Credibility of Claims on the Web. In Proceedings of the 26th International Conference on World Wide Web Companion, Perth, Australia, April 3-7, 2017. 735–739.Google ScholarDigital Library
- Kashyap Popat, Subhabrata Mukherjee, Andrew Yates, and Gerhard Weikum. 2018. DeClarE: Debunking Fake News and False Claims using Evidence-Aware Deep Learning. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, October 31 - November 4, 2018. 22–32.Google ScholarCross Ref
- Martin Potthast, Johannes Kiesel, Kevin Reinartz, Janek Bevendorff, and Benno Stein. 2017. A Stylometric Inquiry into Hyperpartisan and Fake News. abs/1702.05638 (2017). http://arxiv.org/abs/1702.05638Google Scholar
- Hannah Rashkin, Eunsol Choi, Jin Yea Jang, Svitlana Volkova, and Yejin Choi. 2017. Truth of varying shades: Analyzing language in fake news and political fact-checking. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. 2931–2937.Google ScholarCross Ref
- 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, CIKM 2017, Singapore, November 06 - 10, 2017. 797–806.Google ScholarDigital Library
- Michela Del Vicario, Alessandro Bessi, Fabiana Zollo, Fabio Petroni, Antonio Scala, Guido Caldarelli, H. Eugene Stanley, and Walter Quattrociocchi. 2016. The spreading of misinformation online. PNAS 113, 3 (2016), 554–559.Google ScholarCross Ref
- 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.Google ScholarCross Ref
- Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron Courville, Ruslan Salakhudinov, Rich Zemel, and Yoshua Bengio. 2015. Show, attend and tell: Neural image caption generation with visual attention. In International conference on machine learning. 2048–2057.Google ScholarDigital Library
- 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
Recommendations
Attention-based C-BiLSTM for fake news detection
AbstractSocial media platforms have radically transformed the creation and dissemination of news. Users can easily access this news in a fast and efficient manner. However, some users might post negative and fraudulent content in the form of ...
Highlights- Tackles fake news detection problem in a multi-class environment.
- Employed pre-...
Learning Textual Representations from Multiple Modalities to Detect Fake News Through One-Class Learning
WebMedia '21: Proceedings of the Brazilian Symposium on Multimedia and the WebFake news can rapidly spread through internet users. Approaches proposed in the literature for content classification usually learn models considering textual and contextual features from real and fake news to minimize the spread of disinformation. One ...
Comments