Yu-Wun Tseng
ABSTRACT
In recent years, fake news detection has attracted a great deal of attention due to the myriad amounts of misinformation. Some previous methods have focused on modeling the news content, while others have combined user comments and user information on social media. However, existing methods ignore some important clues for detecting fake news, such as temporal information on social media and external knowledge related to the news. To this end, we propose a Knowledge-Aware Hierarchical Attention Network (KAHAN) that integrates this information into the model to establish fact-based associations with entities in the news content. Specifically, we introduce two hierarchical attention networks to model news content and user comments respectively, in which news content and user comments are represented by different aspects for modeling various degrees of semantic granularity. Besides, to process the random occurrences of user comments at post-level, we further designed a time-based subevent division algorithm to aggregate user comments at subevent-level to learn temporal patterns. Moreover, News towards Entities (N-E) attention and Comments towards Entities (C-E) attention are introduced to measure the importance of external knowledge. Finally, we detected the veracity of the news by combining the three aspects of news: content, user comments, and external knowledge. We conducted extensive experiments and ablation studies on two real-world datasets and showed that our proposed method outperformed the previous methods and empirically validated each component of KAHAN1.
- 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
- Alexandre Bovet and Hernán A Makse. 2019. Influence of fake news in Twitter during the 2016 US presidential election. Nature communications 10, 1 (2019), 1–14.Google Scholar
- 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 PAKDD (Workshops)(Lecture Notes in Computer Science), Vol. 11154. Springer, 40–52.Google Scholar
- Kyunghyun Cho, Bart Van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014. Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078(2014).Google Scholar
- Francesco Ducci, Mathias Kraus, and Stefan Feuerriegel. 2020. Cascade-LSTM: A Tree-Structured Neural Classifier for Detecting Misinformation Cascades. In KDD. ACM, 2666–2676.Google Scholar
- Yaqian Dun, Kefei Tu, Chen Chen, Chunyan Hou, and Xiaojie Yuan. 2021. KAN: Knowledge-aware Attention Network for Fake News Detection. In AAAI. AAAI Press, 81–89.Google Scholar
- Han Guo, Juan Cao, Yazi Zhang, Junbo Guo, and Jintao Li. 2018. Rumor Detection with Hierarchical Social Attention Network. In CIKM. ACM, 943–951.Google Scholar
- Linmei Hu, Tianchi Yang, Luhao Zhang, Wanjun Zhong, Duyu Tang, Chuan Shi, Nan Duan, and Ming Zhou. 2021. Compare to The Knowledge: Graph Neural Fake News Detection with External Knowledge. In ACL/IJCNLP (1). Association for Computational Linguistics, 754–763.Google Scholar
- Ling Min Serena Khoo, Hai Leong Chieu, Zhong Qian, and Jing Jiang. 2020. Interpretable Rumor Detection in Microblogs by Attending to User Interactions. In AAAI. AAAI Press, 8783–8790.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. AAAI Press, 354–361.Google Scholar
- Yi-Ju Lu and Cheng-Te Li. 2020. GCAN: Graph-aware Co-Attention Networks for Explainable Fake News Detection on Social Media. In ACL. Association for Computational Linguistics, 505–514.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. IJCAI/AAAI Press, 3818–3824.Google Scholar
- Jing Ma, Wei Gao, and Kam-Fai Wong. 2018. Rumor Detection on Twitter with Tree-structured Recursive Neural Networks. In ACL (1). Association for Computational Linguistics, 1980–1989.Google Scholar
- 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
- Piotr Przybyla. 2020. Capturing the Style of Fake News. In AAAI. AAAI Press, 490–497.Google Scholar
- Feng Qian, ChengYue Gong, Karishma Sharma, and Yan Liu. 2018. Neural User Response Generator: Fake News Detection with Collective User Intelligence. In IJCAI. ijcai.org, 3834–3840.Google Scholar
- Kenneth Rapoza. 2017. Can ‘fake news’ impact the stock market?Forbes News (2017).Google Scholar
- Natali Ruchansky, Sungyong Seo, and Yan Liu. 2017. CSI: A Hybrid Deep Model for Fake News Detection. In CIKM. ACM, 797–806.Google Scholar
- Vinay Setty and Erlend Rekve. 2020. Truth be Told: Fake News Detection Using User Reactions on Reddit. In CIKM. ACM, 3325–3328.Google Scholar
- Kai Shu, Limeng Cui, Suhang Wang, Dongwon Lee, and Huan Liu. 2019. dEFEND: Explainable Fake News Detection. In KDD. ACM, 395–405.Google Scholar
- Kai Shu, Deepak Mahudeswaran, Suhang Wang, Dongwon Lee, and Huan Liu. 2020. FakeNewsNet: A Data Repository with News Content, Social Context, and Spatiotemporal Information for Studying Fake News on Social Media. Big Data 8, 3 (2020), 171–188.Google ScholarCross Ref
- Kai Shu, Deepak Mahudeswaran, Suhang Wang, and Huan Liu. 2020. Hierarchical Propagation Networks for Fake News Detection: Investigation and Exploitation. In ICWSM. AAAI Press, 626–637.Google Scholar
- Kai Shu, Amy Sliva, Suhang Wang, Jiliang Tang, and Huan Liu. 2017. Fake News Detection on Social Media: A Data Mining Perspective. SIGKDD Explor. 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. ACM, 312–320.Google Scholar
- Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is All you Need. In NIPS. 5998–6008.Google Scholar
- Denny Vrandečić and Markus Krötzsch. 2014. Wikidata: a free collaborative knowledgebase. Commun. ACM 57, 10 (2014), 78–85.Google ScholarDigital Library
- Hongwei Wang, Fuzheng Zhang, Xing Xie, and Minyi Guo. 2018. DKN: Deep Knowledge-Aware Network for News Recommendation. In WWW. ACM, 1835–1844.Google ScholarDigital Library
- William Yang Wang. 2017. ”Liar, Liar Pants on Fire”: A New Benchmark Dataset for Fake News Detection. In ACL (2). Association for Computational Linguistics, 422–426.Google Scholar
- Ikuya Yamada, Akari Asai, Jin Sakuma, Hiroyuki Shindo, Hideaki Takeda, Yoshiyasu Takefuji, and Yuji Matsumoto. 2020. Wikipedia2Vec: An Efficient Toolkit for Learning and Visualizing the Embeddings of Words and Entities from Wikipedia. In EMNLP (Demos). Association for Computational Linguistics, 23–30.Google Scholar
- Shuo Yang, Kai Shu, Suhang Wang, Renjie Gu, Fan Wu, and Huan Liu. 2019. Unsupervised Fake News Detection on Social Media: A Generative Approach. In AAAI. AAAI Press, 5644–5651.Google Scholar
- Zichao Yang, Diyi Yang, Chris Dyer, Xiaodong He, Alexander J. Smola, and Eduard H. Hovy. 2016. Hierarchical Attention Networks for Document Classification. In HLT-NAACL. The Association for Computational Linguistics, 1480–1489.Google Scholar
- Wanjun Zhong, Jingjing Xu, Duyu Tang, Zenan Xu, Nan Duan, Ming Zhou, Jiahai Wang, and Jian Yin. 2020. Reasoning Over Semantic-Level Graph for Fact Checking. In ACL. Association for Computational Linguistics, 6170–6180.Google Scholar
- Jie Zhou, Xu Han, Cheng Yang, Zhiyuan Liu, Lifeng Wang, Changcheng Li, and Maosong Sun. 2019. GEAR: Graph-based Evidence Aggregating and Reasoning for Fact Verification. In ACL (1). Association for Computational Linguistics, 892–901.Google Scholar
Index Terms
- KAHAN: Knowledge-Aware Hierarchical Attention Network for Fake News detection on Social Media
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