Van-Hoang Nguyen
Kazunari Sugiyama
Preslav Nakov
ABSTRACT
We propose Factual News Graph (FANG), a novel graphical social context representation and learning framework for fake news detection. Unlike previous contextual models that have targeted performance, our focus is on representation learning. Compared to transductive models, FANG is scalable in training as it does not have to maintain all nodes, and it is efficient at inference time, without the need to re-process the entire graph. Our experimental results show that FANG is better at capturing the social context into a high fidelity representation, compared to recent graphical and non-graphical models. In particular, FANG yields significant improvements for the task of fake news detection, and it is robust in the case of limited training data. We further demonstrate that the representations learned by FANG generalize to related tasks, such as predicting the factuality of reporting of a news medium.
Supplemental Material
- O. Ajao, D. Bhowmik, and S. Zargari. 2019. Sentiment Aware Fake News Detection on Online Social Networks. In ICASSP. 2507--2511.Google Scholar
- Mihael Ankerst, Markus M Breunig, Hans-Peter Kriegel, and Jörg Sander. 1999. OPTICS: Ordering Points to Identify the Clustering Structure. ACM SIGMOD Record, Vol. 28, 2 (1999), 49--60.Google ScholarDigital Library
- Ramy Baly, Georgi Karadzhov, Dimitar Alexandrov, James Glass, and Preslav Nakov. 2018. Predicting Factuality of Reporting and Bias of News Media Sources. In EMNLP. 3528--3539.Google Scholar
- Levi Boxell, Matthew Gentzkow, and Jesse M. Shapiro. 2017. Is the Internet Causing Political Polarization? Evidence from Demographics. Technical Report NBER Working Paper No. 23258. National Bureau of Economic Research.Google Scholar
- Michael M. Bronstein, Joan Bruna, Yann LeCun, Arthur Szlam, and Pierre Vandergheynst. 2017. Geometric Deep Learning: Going beyond Euclidean data. IEEE Signal Processing Magazine, Vol. 34, 4 (2017), 18--42.Google ScholarCross Ref
- Carlos Castillo, Marcelo Mendoza, and Barbara Poblete. 2011. Information Credibility on Twitter. In WWW. 675--684.Google Scholar
- Kareem Darwish, Dimitar Alexandrov, Preslav Nakov, and Yelena Mejova. 2017. Seminar Users in the Arabic Twitter Sphere. In SocInfo. 91--108.Google Scholar
- Leon Derczynski, Kalina Bontcheva, Maria Liakata, Rob Procter, Geraldine Wong Sak Hoi, and Arkaitz Zubiaga. 2017. SemEval-2017 Task 8: RumourEval: Determining rumour veracity and support for rumours. In SemEval-2017. 69--76.Google Scholar
- Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In NAACL. 4171--4186.Google Scholar
- Ming Dong, Bolong Zheng, Nguyen Quoc Viet Hung, Han Su, and Guohui Li. 2019. Multiple Rumor Source Detection with Graph Convolutional Networks. In CIKM. 569--578.Google Scholar
- Aditya Grover and Jure Leskovec. 2016. node2vec: Scalable Feature Learning for Networks. In KDD. 855--864.Google Scholar
- William L. Hamilton, Zhitao Ying, and Jure Leskovec. 2017. Inductive Representation Learning on Large Graphs. In NIPS. 1024--1034.Google Scholar
- Naeemul Hassan, Gensheng Zhang, Fatma Arslan, Josue Caraballo, Damian Jimenez, Siddhant Gawsane, Shohedul Hasan, Minumol Joseph, Aaditya Kulkarni, Anil K. Nayak, Vikas Sable, Chengkai Li, and Mark Tremayne. 2017. ClaimBuster: The First-ever End-to-end Fact-checking System. VLDB, Vol. 10, 12 (2017), 1945--1948.Google ScholarDigital Library
- Xiangnan He, Ming Gao, Min-Yen Kan, Yiqun Liu, and Kazunari Sugiyama. 2014. Predicting the Popularity of Web 2.0 Items based on User Comments. In SIGIR. 233--242.Google Scholar
- Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long Short-Term Memory. Neural Computation, Vol. 9, 8 (1997), 1735--1780.Google ScholarDigital Library
- Zhiwei Jin, Juan Cao, Yu-Gang Jiang, and Yongdong Zhang. 2014. News Credibility Evaluation on Microblog with a Hierarchical Propagation Model. In ICDM. 230--239.Google Scholar
- Zhiwei Jin, Juan Cao, Yongdong Zhang, and Jiebo Luo. 2016. News Verification by Exploiting Conflicting Social Viewpoints in Microblogs. In AAAI. 2972--2978.Google Scholar
- Dhruv Khattar, Jaipal Singh Goud, Manish Gupta, and Vasudeva Varma. 2019. MVAE: Multimodal Variational Autoencoder for Fake News Detection. In WWW. 2915--2921.Google Scholar
- Thomas N. Kipf and Max Welling. 2017. Semi-Supervised Classification with Graph Convolutional Networks. In ICLR.Google Scholar
- Elena Kochkina, Maria Liakata, and Arkaitz Zubiaga. 2018. PHEME dataset for Rumour Detection and Veracity Classification. (6 2018). https://figshare.com/articles/dataset/PHEME_dataset_for_Rumour_Detection_and_Veracity_Classification/6392078Google Scholar
- Vivek Kulkarni, Junting Ye, Steven Skiena, and William Yang Wang. 2018. Multi-view Models for Political Ideology Detection of News Articles. In EMNLP. 3518--3527.Google Scholar
- Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. RoBERTa: A Robustly Optimized BERT Pretraining Approach. arXiv:1907.11692 (2019).Google Scholar
- 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 AAAI. 354--361.Google Scholar
- Minh-Thang Luong, Hieu Pham, and Christopher D. Manning. 2015. Effective Approaches to Attention-based Neural Machine Translation. In EMNLP. 1412--1421.Google Scholar
- Jing Ma, Wei Gao, Prasenjit Mitra, Sejeong Kwon, Bernard J. Jansen, Kam-Fai Wong, and Meeyoung Cha. 2016. Detecting Rumors from Microblogs with Recurrent Neural Networks. In IJCAI. 3818--3824.Google Scholar
- Jing Ma, Wei Gao, Zhongyu Wei, Yueming Lu, and Kam-Fai Wong. 2015. Detect Rumors Using Time Series of Social Context Information on Microblogging Websites. In CIKM. 1751--1754.Google Scholar
- Saif Mohammad, Svetlana Kiritchenko, Parinaz Sobhani, Xiaodan Zhu, and Colin Cherry. 2016. SemEval-2016 Task 6: Detecting Stance in Tweets. In SemEval-2016. 31--41.Google Scholar
- Mitra Mohtarami, Ramy Baly, James Glass, Preslav Nakov, Lluís Màrquez, and Alessandro Moschitti. 2018. Automatic Stance Detection Using End-to-End Memory Networks. In NAACL-HLT. 767--776.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
- Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 2014. GloVe: Global Vectors for Word Representation. In EMNLP. 1532--1543.Google Scholar
- Bryan Perozzi, Rami Al-Rfou, and Steven Skiena. 2014. DeepWalk: Online Learning of Social Representations. In KDD. 701--710.Google Scholar
- Kashyap Popat, Subhabrata Mukherjee, Jannik Strötgen, and Gerhard Weikum. 2016. Credibility Assessment of Textual Claims on the Web. In CIKM. 2173--2178.Google Scholar
- Kashyap Popat, Subhabrata Mukherjee, Jannik Strötgen, and Gerhard Weikum. 2017. Where the Truth Lies: Explaining the Credibility of Emerging Claims on the Web and Social Media. In WWW. 1003--1012.Google Scholar
- Kashyap Popat, Subhabrata Mukherjee, Jannik Strötgen, and Gerhard Weikum. 2018. CredEye: A Credibility Lens for Analyzing and Explaining Misinformation. In WWW. 155--158.Google Scholar
- Natali Ruchansky, Sungyong Seo, and Yan Liu. 2017. CSI: A Hybrid Deep Model for Fake News Detection. In CIKM. 797--806.Google Scholar
- Gerald Salton and Michael J. McGill. 1983. Introduction to Modern Information Retrieval. McGraw-Hill.Google ScholarDigital Library
- 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, Amy Sliva, Suhang Wang, Jiliang Tang, and Huan Liu. 2017. Fake News Detection on Social Media: A Data Mining Perspective. ACM SIGKDD Explorations Newsletter, Vol. 19, 1 (2017), 22--36.Google ScholarDigital Library
- Kai Shu, Suhang Wang, and Huan Liu. 2019. Beyond News Contents: The Role of Social Context for Fake News Detection. In WSDM. 312--320.Google Scholar
- Parinaz Sobhani, Diana Inkpen, and Xiaodan Zhu. 2017. A Dataset for Multi-Target Stance Detection. In EACL. 551--557.Google Scholar
- Zoe Thomas. 2020. WHO says fake coronavirus claims causing `infodemic'. BBC News (13th Feb, 2020). https://www.bbc.com/news/technology-51497800Google Scholar
- James Thorne and Andreas Vlachos. 2018. Automated Fact Checking: Task Formulations, Methods and Future Directions. In COLING. 3346--3359.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 KDD. 849--857.Google ScholarDigital Library
- Fan Yang, Yang Liu, Xiaohui Yu, and Min Yang. 2012. Automatic Detection of Rumor on Sina Weibo. In MDS. 1--7.Google Scholar
- Chunyuan Yuan, Qianwen Ma, Wei Zhou, Jizhong Han, and Songlin Hu. 2019. Jointly Embedding the Local and Global Relations of Heterogeneous Graph for Rumor Detection. In ICDM. 796--805.Google Scholar
Index Terms
- FANG: Leveraging Social Context for Fake News Detection Using Graph Representation
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