ABSTRACT
In recent years, an unhealthy phenomenon characterized as the massive spread of fake news or unverified information (i.e., rumors) has become increasingly a daunting issue in human society. The rumors commonly originate from social media outlets, primarily microblogging platforms, being viral afterwards by the wild, willful propagation via a large number of participants. It is observed that rumorous posts often trigger versatile, mostly controversial stances among participating users. Thus, determining the stances on the posts in question can be pertinent to the successful detection of rumors, and vice versa. Existing studies, however, mainly regard rumor detection and stance classification as separate tasks. In this paper, we argue that they should be treated as a joint, collaborative effort, considering the strong connections between the veracity of claim and the stances expressed in responsive posts. Enlightened by the multi-task learning scheme, we propose a joint framework that unifies the two highly pertinent tasks, i.e., rumor detection and stance classification. Based on deep neural networks, we train both tasks jointly using weight sharing to extract the common and task-invariant features while each task can still learn its task-specific features. Extensive experiments on real-world datasets gathered from Twitter and news portals demonstrate that our proposed framework improves both rumor detection and stance classification tasks consistently with the help of the strong inter-task connections, achieving much better performance than state-of-the-art methods.
- Rob Abbott, Marilyn Walker, Pranav Anand, Jean E Fox Tree, Robeson Bowmani, and Joseph King. 2011. How can you say such things!: Recognizing disagreement in informal political argument Proceedings of the Workshop on Languages in Social Media. Association for Computational Linguistics, 2--11. Google ScholarDigital Library
- Rie Kubota Ando and Tong Zhang. 2005. A framework for learning predictive structures from multiple tasks and unlabeled data. Journal of Machine Learning Research Vol. 6, Nov (2005), 1817--1853. Google ScholarDigital Library
- Isabelle Augenstein, Tim Rockt"aschel, Andreas Vlachos, and Kalina Bontcheva. 2016. Stance detection with bidirectional conditional encoding. arXiv preprint arXiv:1606.05464 (2016).Google Scholar
- Bernd Bohnet and Joakim Nivre. 2012. A transition-based system for joint part-of-speech tagging and labeled non-projective dependency parsing. In Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning. Association for Computational Linguistics, 1455--1465. Google ScholarDigital Library
- Rich Caruana. 1998. Multitask learning. Learning to learn. Springer, 95--133. Google ScholarDigital Library
- Carlos Castillo, Marcelo Mendoza, and Barbara Poblete. 2011. Information credibility on twitter. In Proceedings of WWW. Google ScholarDigital Library
- Yi-Chin Chen, Zhao-Yang Liu, and Hung-Yu Kao. 2017. IKM at SemEval-2017 Task 8: Convolutional Neural Networks for stance detection and rumor verification. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017). 465--469.Google ScholarCross Ref
- Kyunghyun Cho, Bart van Merriënboer, Dzmitry Bahdanau, and Yoshua Bengio. 2014. On the properties of neural machine translation: Encoder-decoder approaches. arXiv preprint arXiv:1409.1259 (2014).Google Scholar
- Ju-han Chuang and Shukai Hsieh. 2015. Stance classification on ptt comments. In Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation.Google Scholar
- Ronan Collobert and Jason Weston. 2008. A unified architecture for natural language processing: Deep neural networks with multitask learning. In Proceedings of the 25th international conference on Machine learning. ACM, 160--167. Google ScholarDigital Library
- Ronan Collobert, Jason Weston, Léon Bottou, Michael Karlen, Koray Kavukcuoglu, and Pavel Kuksa. 2011. Natural language processing (almost) from scratch. Journal of Machine Learning Research Vol. 12, Aug (2011), 2493--2537. Google ScholarDigital Library
- Nicholas DiFonzo and Prashant Bordia. 2007. Rumor, gossip and urban legends. Diogenes, Vol. 54, 1 (2007), 19--35.Google ScholarCross Ref
- Daxiang Dong, Hua Wu, Wei He, Dianhai Yu, and Haifeng Wang. 2015. Multi-Task Learning for Multiple Language Translation. ACL (1). 1723--1732.Google Scholar
- Pamela Donovan. 2007. How idle is idle talk One hundred years of rumor research. Diogenes, Vol. 54, 1 (2007), 59--82.Google ScholarCross Ref
- John Duchi, Elad Hazan, and Yoram Singer. 2011. Adaptive subgradient methods for online learning and stochastic optimization. Journal of Machine Learning Research Vol. 12, Jul (2011), 2121--2159. Google ScholarDigital Library
- Orhan Firat, Kyunghyun Cho, and Yoshua Bengio. 2016. Multi-Way, Multilingual Neural Machine Translation with a Shared Attention Mechanism Proceedings of NAACL-HLT. 866--875.Google Scholar
- Adrien Friggeri, Lada A Adamic, Dean Eckles, and Justin Cheng. 2014. Rumor cascades Proceedings of ICWSM.Google Scholar
- Sardar Hamidian and Mona Diab. 2015. Rumor detection and classification for twitter data The Fifth International Conference on Social Media Technologies, Communication, and Informatics, SOTICS, IARIA. 71--77.Google Scholar
- Aniko Hannak, Drew Margolin, Brian Keegan, and Ingmar Weber. 2014. Get Back! You Don't Know Me Like That: The Social Mediation of Fact Checking Interventions in Twitter Conversations. In ICWSM.Google Scholar
- Jun Hatori, Takuya Matsuzaki, Yusuke Miyao, and Jun'ichi Tsujii. 2012. Incremental joint approach to word segmentation, POS tagging, and dependency parsing in Chinese Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers-Volume 1. Association for Computational Linguistics, 1045--1053. Google ScholarDigital Library
- Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural computation, Vol. 9, 8 (1997), 1735--1780. Google ScholarDigital Library
- Elena Kochkina, Maria Liakata, and Isabelle Augenstein. 2017. Turing at SemEval-2017 Task 8: Sequential Approach to Rumour Stance Classification with Branch-LSTM. arXiv preprint arXiv:1704.07221 (2017).Google Scholar
- Sejeong Kwon, Meeyoung Cha, and Kyomin Jung. 2017. Rumor Detection over Varying Time Windows. PLOS ONE, Vol. 12, 1 (2017), e0168344.Google ScholarCross Ref
- Sejeong Kwon, Meeyoung Cha, Kyomin Jung, Wei Chen, and Yajun Wang. 2013. Prominent features of rumor propagation in online social media Proceedings of ICDM.Google Scholar
- Zhenghua Li, Min Zhang, Wanxiang Che, Ting Liu, and Wenliang Chen. 2014. Joint optimization for Chinese pos tagging and dependency parsing. IEEE/ACM Transactions on Audio, Speech, and Language Processing, Vol. 22, 1 (2014), 274--286. Google ScholarDigital Library
- Pengfei Liu, Xipeng Qiu, and Xuanjing Huang. 2016. Recurrent neural network for text classification with multi-task learning Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence. AAAI Press, 2873--2879. Google ScholarDigital Library
- Xiaodong Liu, Jianfeng Gao, Xiaodong He, Li Deng, Kevin Duh, and Ye-Yi Wang. 2015 a. Representation Learning Using Multi-Task Deep Neural Networks for Semantic Classification and Information Retrieval. In HLT-NAACL. 912--921.Google Scholar
- Xiaomo Liu, Armineh Nourbakhsh, Quanzhi Li, Rui Fang, and Sameena Shah. 2015 b. Real-time Rumor Debunking on Twitter. In Proceedings of CIKM. Google ScholarDigital Library
- Michal Lukasik, Trevor Cohn, and Kalina Bontcheva. 2015. Classifying tweet level judgements of rumours in social media. arXiv preprint arXiv:1506.00468 (2015).Google Scholar
- Michal Lukasik, PK Srijith, Duy Vu, Kalina Bontcheva, Arkaitz Zubiaga, and Trevor Cohn. 2016. Hawkes processes for continuous time sequence classification: an application to rumour stance classification in twitter. Proceedings of 54th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, 393--398.Google ScholarCross Ref
- Minh-Thang Luong, Quoc V Le, Ilya Sutskever, Oriol Vinyals, and Lukasz Kaiser. 2015. Multi-task sequence to sequence learning. arXiv preprint arXiv:1511.06114 (2015).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 Proceedings of IJCAI. Google ScholarDigital Library
- 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 Proceedings of CIKM. Google ScholarDigital Library
- Jing Ma, Wei Gao, and Kam-Fai Wong. 2017. Detect Rumors in Microblog Posts Using Propagation Structure via Kernel Learning Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Vol. Vol. 1. 708--717.Google Scholar
- Marcelo Mendoza, Barbara Poblete, and Carlos Castillo. 2010. Twitter Under Crisis: Can we trust what we RT. In Proceedings of the first workshop on social media analytics. ACM, 71--79. Google ScholarDigital Library
- Saif Mohammad, Svetlana Kiritchenko, Parinaz Sobhani, Xiao-Dan Zhu, and Colin Cherry. 2016. SemEval-2016 Task 6: Detecting Stance in Tweets. SemEval@ NAACL-HLT. 31--41.Google Scholar
- Meredith Ringel Morris, Scott Counts, Asta Roseway, Aaron Hoff, and Julia Schwarz. 2012. Tweeting is believing: understanding microblog credibility perceptions Proceedings of the ACM 2012 conference on Computer Supported Cooperative Work. ACM, 441--450. Google ScholarDigital Library
- Vahed Qazvinian, Emily Rosengren, Dragomir R Radev, and Qiaozhu Mei. 2011. Rumor has it: Identifying misinformation in microblogs Proceedings of EMNLP. Google ScholarDigital Library
- Sarvesh Ranade, Rajeev Sangal, and Radhika Mamidi. 2013. Stance Classification in Online Debates by Recognizing Users' Intentions. SIGDIAL Conference. 61--69.Google Scholar
- Bhavtosh Rath, Wei Gao, Jing Ma, and Jaideep Srivastava. 2015. From Retweet to Believability: Utilizing Trust to Identify Rumor Spreaders on Twitter Advances in Social Networks Analysis and Mining (ASONAM), 2017 IEEE/ACM International Conference on. IEEE, 179--186. Google ScholarDigital Library
- Sara Rosenthal and Kathy McKeown. 2015. I Couldn't Agree More: The Role of Conversational Structure in Agreement and Disagreement Detection in Online Discussions. In SIGDIAL Conference. 168--177.Google ScholarCross Ref
- Ralph L Rosnow. 1991. Inside rumor: A personal journey. American Psychologist Vol. 46, 5 (1991), 484.Google ScholarCross Ref
- Prashanth Vijayaraghavan, Ivan Sysoev, Soroush Vosoughi, and Deb Roy. 2016. Deepstance at semeval-2016 task 6: Detecting stance in tweets using character and word-level cnns. arXiv preprint arXiv:1606.05694 (2016).Google Scholar
- Ke Wu, Song Yang, and Kenny Q Zhu. 2015. False rumors detection on sina weibo by propagation structures Proceedings of ICDE.Google Scholar
- Fan Yang, Yang Liu, Xiaohui Yu, and Min Yang. 2012. Automatic detection of rumor on sina weibo. In Proceedings of the ACM SIGKDD Workshop on Mining Data Semantics. Google ScholarDigital Library
- Li Zeng, Kate Starbird, and Emma S Spiro. 2016. # Unconfirmed: Classifying Rumor Stance in Crisis-Related Social Media Messages Tenth International AAAI Conference on Web and Social Media.Google Scholar
- Zhe Zhao, Paul Resnick, and Qiaozhu Mei. 2015. Enquiring Minds: Early Detection of Rumors in Social Media from Enquiry Posts Proceedings of WWW. Google ScholarDigital Library
- Arkaitz Zubiaga, Elena Kochkina, Maria Liakata, Rob Procter, and Michal Lukasik. 2016 a. Stance classification in rumours as a sequential task exploiting the tree structure of social media conversations. arXiv preprint arXiv:1609.09028 (2016).Google Scholar
- Arkaitz Zubiaga, Maria Liakata, Rob Procter, Geraldine Wong Sak Hoi, and Peter Tolmie. 2016 b. Analysing how people orient to and spread rumours in social media by looking at conversational threads. PloS one, Vol. 11, 3 (2016), e0150989.Google ScholarCross Ref
Index Terms
- Detect Rumor and Stance Jointly by Neural Multi-task Learning
Recommendations
Multimodal Fusion with Recurrent Neural Networks for Rumor Detection on Microblogs
MM '17: Proceedings of the 25th ACM international conference on MultimediaMicroblogs have become popular media for news propagation in recent years. Meanwhile, numerous rumors and fake news also bloom and spread wildly on the open social media platforms. Without verification, they could seriously jeopardize the credibility of ...
VRoC: Variational Autoencoder-aided Multi-task Rumor Classifier Based on Text
WWW '20: Proceedings of The Web Conference 2020Social media became popular and percolated almost all aspects of our daily lives. While online posting proves very convenient for individual users, it also fosters fast-spreading of various rumors. The rapid and wide percolation of rumors can cause ...
Gaussian Processes for Rumour Stance Classification in Social Media
Social media tend to be rife with rumours while new reports are released piecemeal during breaking news. Interestingly, one can mine multiple reactions expressed by social media users in those situations, exploring their stance towards rumours, ...
Comments