Abstract
The fast spreading of fake news stories on social media can cause inestimable social harm. Developing effective methods to detect them early is of paramount importance. A major challenge of fake news early detection is fully utilizing the limited data observed at the early stage of news propagation and then learning useful patterns from it for identifying fake news. In this article, we propose a novel deep neural network to detect fake news early. It has three novel components: (1) a status-sensitive crowd response feature extractor that extracts both text features and user features from combinations of users’ text response and their corresponding user profiles, (2) a position-aware attention mechanism that highlights important user responses at specific ranking positions, and (3) a multi-region mean-pooling mechanism to perform feature aggregation based on multiple window sizes. Experimental results on two real-world datasets demonstrate that our proposed model can detect fake news with greater than 90% accuracy within 5 minutes after it starts to spread and before it is retweeted 50 times, which is significantly faster than state-of-the-art baselines. Most importantly, our approach requires only 10% labeled fake news samples to achieve this effectiveness under PU-Learning settings.
- Sadia Afroz, Michael Brennan, and Rachel Greenstadt. 2012. Detecting hoaxes, frauds, and deception in writing style online. In Proceedings of the IEEE Symposium on Security and Privacy (SP’12). IEEE, Los Alamitos, CA, 461--475.Google ScholarDigital Library
- Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014. Neural machine translation by jointly learning to align and translate. arXiv:1409.0473.Google Scholar
- Marc Jonathan Blitz. 2018. Lies, line drawing, and deep fake news. Oklahoma Law Review 71 (2018), 59.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, New York, NY, 675--684.Google ScholarDigital Library
- Tong Chen, Lin Wu, Xue Li, Jun Zhang, Hongzhi Yin, and Yang Wang. 2017. Call attention to rumors: Deep attention based recurrent neural networks for early rumor detection. arXiv:1704.05973.Google Scholar
- Gobinda G. Chowdhury. 2003. Natural language processing. Annual Review of Information Science and Technology 37, 1 (2003), 51--89.Google ScholarCross Ref
- Niall J. Conroy, Victoria L. Rubin, and Yimin Chen. 2015. Automatic deception detection: Methods for finding fake news. Proceedings of the Association for Information Science and Technology 52, 1 (2015), 1--4.Google ScholarCross Ref
- Joydip Dhar, Ankur Jain, and Vijay K. Gupta. 2016. A mathematical model of news propagation on online social network and a control strategy for rumor spreading. Social Network Analysis and Mining 6, 1 (2016), 57.Google ScholarCross Ref
- Rim El Ballouli, Wassim El-Hajj, Ahmad Ghandour, Shady Elbassuoni, Hazem Hajj, and Khaled Shaban. 2017. CAT: Credibility analysis of Arabic content on Twitter. In Proceedings of the 3rd Arabic Natural Language Processing Workshop (WANLP’17) Co-Located with EACL 2017). 62.Google ScholarCross Ref
- Boris Galitsky. 2015. Detecting rumor and disinformation by web mining. In Proceedings of the 2015 AAAI Spring Symposium Series.Google Scholar
- Han Guo, Juan Cao, Yazi Zhang, Junbo Guo, and Jintao Li. 2018. Rumor detection with hierarchical social attention network. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management. ACM, New York, NY, 943--951.Google ScholarDigital Library
- Aditi Gupta, Ponnurangam Kumaraguru, Carlos Castillo, and Patrick Meier. 2014. TweetCred: Real-time credibility assessment of content on Twitter. In Proceedings of the International Conference on Social Informatics. 228--243.Google ScholarCross Ref
- Manish Gupta, Peixiang Zhao, and Jiawei Han. 2012. Evaluating event credibility on Twitter. In Proceedings of the 2012 SIAM International Conference on Data Mining. 153--164.Google ScholarCross Ref
- Cho-Jui Hsieh, Nagarajan Natarajan, and Inderjit S. Dhillon. 2015. PU Learning for matrix completion. In Proceedings of the 32nd International Conference on Machine Learning (ICML’15). 2445--2453.Google Scholar
- Xia Hu, Jiliang Tang, Yanchao Zhang, and Huan Liu. 2013. Social spammer detection in microblogging. In Proceedings of the 23rd International Joint Conference on Artificial Intelligence (IJCAI’13), Vol. 13. 2633--2639.Google Scholar
- Alankar Jain, Vivek Borkar, and Dinesh Garg. 2016a. Fast rumor source identification via random walks. Social Network Analysis and Mining 6, 1 (2016), 62.Google ScholarCross Ref
- Suchita Jain, Vanya Sharma, and Rishabh Kaushal. 2016b. Towards automated real-time detection of misinformation on Twitter. In Proceedings of the International Conference on Advances in Computing, Communications, and Informatics (ICACCI’16). IEEE, Los Alamitos, CA, 2015--2020.Google ScholarCross Ref
- F. Jin, E. Dougherty, P. Saraf, Y. Cao, and N. Ramakrishnan. 2013. Epidemiological modeling of news and rumors on Twitter. In Proceedings of the 7th Workshop on Social Network Mining and Analysis. ACM, New York, NY, Article 8, 9 pages.Google Scholar
- Zhiwei Jin, Juan Cao, Han Guo, Yongdong Zhang, and Jiebo Luo. 2017a. Multimodal fusion with recurrent neural networks for rumor detection on microblogs. In Proceedings of the 2017 ACM Multimedia Conference. ACM, New York, NY, 795--816.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 Proceedings of the IEEE International Conference on Data Mining (ICDM’14). IEEE, Los Alamitos, CA, 230--239.Google ScholarDigital Library
- Zhiwei Jin, Juan Cao, Yongdong Zhang, Jianshe Zhou, and Qi Tian. 2017b. Novel visual and statistical image features for microblogs news verification. IEEE Transactions on Multimedia 19, 3 (2017), 598--608.Google ScholarDigital Library
- Sejeong Kwon, Meeyoung Cha, and Kyomin Jung. 2017. Rumor detection over varying time windows. PLoS One 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. In Proceedings of the IEEE 13th International Conference on Data Mining (ICDM’13). IEEE, Los Alamitos, CA, 1103--1108.Google ScholarCross Ref
- Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. 2015. Deep learning. Nature 521, 7553 (2015), 436.Google Scholar
- Wee Sun Lee and Bing Liu. 2003. Learning with positive and unlabeled examples using weighted logistic regression. In Proceedings of the 20th International Conference on Machine Learning (ICML’03), Vol. 3. 448--455.Google Scholar
- Chaoliang Li and Shigang Liu. 2017. A comparative study of the class imbalance problem in Twitter spam detection. Concurrency and Computation: Practice and Experience 30, 5 (2017), e4281.Google ScholarCross Ref
- Xiaoli Li and Bing Liu. 2003. Learning to classify texts using positive and unlabeled data. In Proceedings of the 18th International Joint Conference on Artificial Intelligence (IJCAI’03), Vol. 3. 587--592.Google Scholar
- Xiao-Li Li and Bing Liu. 2005. Learning from positive and unlabeled examples with different data distributions. In Proceedings of the 16th European Conference on Machine Learning (ECML’05). 218--229.Google ScholarDigital Library
- Bing Liu, Yang Dai, Xiaoli Li, Wee Sun Lee, and Philip S. Yu. 2003. Building text classifiers using positive and unlabeled examples. In Proceedings of the 3rd IEEE International Conference on Data Mining (ICDM’03). IEEE, Los Alamitos, CA, 179--186.Google Scholar
- Bing Liu, Wee Sun Lee, Philip S. Yu, and Xiaoli Li. 2002. Partially supervised classification of text documents. In Proceedings of the 19th International Conference on Machine Learning (ICML’02), Vol. 2. 387--394.Google Scholar
- Shigang Liu, Yu Wang, Jun Zhang, Chao Chen, and Yang Xiang. 2017b. Addressing the class imbalance problem in Twitter spam detection using ensemble learning. Computers 8 Security 69 (2017), 35--49.Google Scholar
- Xiaomo Liu, Armineh Nourbakhsh, Quanzhi Li, Rui Fang, and Sameena Shah. 2015a. Real-time rumor debunking on Twitter. In Proceedings of the 24th ACM International Conference on Information and Knowledge Management. ACM, New York, NY, 1867--1870.Google ScholarDigital Library
- Yahui Liu, Xiaolong Jin, Huawei Shen, and Xueqi Cheng. 2017a. Do rumors diffuse differently from non-rumors? A systematically empirical analysis in Sina Weibo for rumor identification. In Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining. 407--420.Google ScholarCross Ref
- 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
- Yang Liu and Songhua Xu. 2016. Detecting rumors through modeling information propagation networks in a social media environment. IEEE Transactions on Computational Social Systems 3, 2 (2016), 46--62.Google ScholarCross Ref
- Yang Liu, Songhua Xu, and Georgia Tourassi. 2015b. Detecting rumors through modeling information propagation networks in a social media environment. In Proceedings of the International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction. 121--130.Google ScholarCross Ref
- Michal Lukasik, Kalina Bontcheva, Trevor Cohn, Arkaitz Zubiaga, Maria Liakata, and Rob Procter. 2019. Gaussian processes for rumour stance classification in social media. ACM Transactions on Information Systems 37, 2 (2019), 20.Google ScholarDigital Library
- 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 Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI’16). 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 Proceedings of the 24th ACM International Conference on Information and Knowledge Management. ACM, New York, NY, 1751--1754.Google ScholarDigital Library
- 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, Vol. 1. 708--717.Google ScholarCross Ref
- Jing Ma, Wei Gao, and Kam-Fai Wong. 2018. Rumor detection on Twitter with tree-structured recursive neural networks. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 1980--1989.Google ScholarCross Ref
- Jing Ma, Wei Gao, and Kam-Fai Wong. 2019. Detect rumors on Twitter by promoting information campaigns with generative adversarial learning. In Proceedings of the World Wide Web Conference (WWW’19). 3049--3055.Google ScholarDigital Library
- Benjamin Markines, Ciro Cattuto, and Filippo Menczer. 2009. Social spam detection. In Proceedings of the 5th International Workshop on Adversarial Information Retrieval on the Web. ACM, New York, NY, 41--48.Google ScholarDigital Library
- Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S. Corrado, and Jeff Dean. 2013. Distributed representations of words and phrases and their compositionality. In Advances in Neural Information Processing Systems. 3111--3119.Google Scholar
- Volodymyr Mnih, Nicolas Heess, Alex Graves, and Koray Kavukcuoglu. 2014. Recurrent models of visual attention. In Advances in Neural Information Processing Systems. 2204--2212.Google Scholar
- Kashyap Popat. 2017. Assessing the credibility of claims on the web. In Proceedings of the 26th International Conference on World Wide Web Companion. 735--739.Google ScholarDigital Library
- Vahed Qazvinian, Emily Rosengren, Dragomir R. Radev, and Qiaozhu Mei. 2011. Rumor has it: Identifying misinformation in microblogs. In Proceedings of the Conference on Empirical Methods in Natural Language Processing. 1589--1599.Google Scholar
- Victoria Rubin, Niall Conroy, Yimin Chen, and Sarah Cornwell. 2016. Fake news or truth? Using satirical cues to detect potentially misleading news. In Proceedings of the 2nd Workshop on Computational Approaches to Deception Detection. 7--17.Google ScholarCross Ref
- Victoria L. Rubin. 2017. Deception detection and rumor debunking for social media. In The SAGE Handbook of Social Media Research Methods, L. Sloan and A. Quan-Haase (Eds.). SAGE, London, UK, 342.Google Scholar
- Victoria L. Rubin, Yimin Chen, and Niall J. Conroy. 2015. Deception detection for news: Three types of fakes. Proceedings of the Association for Information Science and Technology 52, 1 (2015), 1--4.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 Conference on Information and Knowledge Management. ACM, New York, NY, 797--806.Google ScholarDigital Library
- Justin Sampson, Fred Morstatter, Liang Wu, and Huan Liu. 2016. Leveraging the implicit structure within social media for emergent rumor detection. In Proceedings of the 25th ACM International Conference on Information and Knowledge Management. ACM, New York, NY, 2377--2382.Google ScholarDigital Library
- 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 19, 1 (2017), 22--36.Google ScholarDigital Library
- Kai Shu, Suhang Wang, and Huan Liu. 2018. Understanding user profiles on social media for fake news detection. In Proceedings of the 2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR’18). IEEE, Los Alamitos, CA, 430--435.Google ScholarCross Ref
- Sam Spencer and R. Srikant. 2016. Maximum likelihood rumor source detection in a star network. In Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP’16). IEEE, Los Alamitos, CA, 2199--2203.Google Scholar
- Nitish Srivastava, Geoffrey E. Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014. Dropout: A simple way to prevent neural networks from overfitting. Journal of Machine Learning Research 15, 1 (2014), 1929--1958.Google ScholarDigital Library
- S. Y. Sun, H. Y. Liu, J. He, and X. Y. Du. 2013. Detecting event rumors on Sina Weibo automatically. In Web Technologies and Applications. Springer, 120--131.Google Scholar
- Wenbing Tang, Zuohua Ding, and Mengchu Zhou. 2019. A spammer identification method for class imbalanced Weibo datasets. IEEE Access 7 (2019), 29193--29201.Google ScholarCross Ref
- Laura Tolosi, Andrey Tagarev, and Georgi Georgiev. 2016. An analysis of event-agnostic features for rumour classification in Twitter. In Proceedings of the 10th International AAAI Conference on Web and Social Media.Google Scholar
- Soroush Vosoughi. 2015. Automatic Detection and Verification of Rumors on Twitter. Ph.D. Dissertation. Massachusetts Institute of Technology, Cambridge, MA.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
- De Wang, Danesh Irani, and Calton Pu. 2011. A social-spam detection framework. In Proceedings of the 8th Annual Collaboration, Electronic Messaging, Anti-Abuse, and Spam Conference. ACM, New York, NY, 46--54.Google ScholarDigital Library
- Shihan Wang and Takao Terano. 2015. Detecting rumor patterns in streaming social media. In Proceedings of the IEEE International Conference on Big Data (Big Data’15). IEEE, Los Alamitos, CA, 2709--2715.Google ScholarDigital Library
- 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 and Data Mining. ACM, New York, NY, 849--857.Google ScholarDigital Library
- Ke Wu, Song Yang, and Kenny Q. Zhu. 2015. False rumors detection on Sina Weibo by propagation structures. In Proceedings of the 31st IEEE International Conference on Data Engineering.Google Scholar
- Liang Wu, Jundong Li, Xia Hu, and Huan Liu. 2017. Gleaning wisdom from the past: Early detection of emerging rumors in social media. In Proceedings of the 2017 SIAM International Conference on Data Mining. 99--107.Google ScholarCross Ref
- 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. ACM, New York, NY, Article 13, 7 pages.Google ScholarDigital Library
- 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 Proceedings of 33rd AAAI Conference on Artificial Intelligence.Google ScholarDigital Library
- YeKang Yang, Kai Niu, and ZhiQiang He. 2015a. Exploiting the topology property of social network for rumor detection. In Proceedings of the 12th International Joint Conference on Computer Science and Software Engineering (JCSSE’15). IEEE, Los Alamitos, CA, 41--46.Google ScholarCross Ref
- Zhifan Yang, Chao Wang, Fan Zhang, Ying Zhang, and Haiwei Zhang. 2015b. Emerging rumor identification for social media with hot topic detection. In Proceedings of the 2015 12th Web Information System and Application Conference (WISA’15). IEEE, Los Alamitos, CA, 53--58.Google ScholarDigital Library
- Hwanjo Yu, Jiawei Han, and Kevin Chen-Chuan Chang. 2002. PEBL: Positive example based learning for web page classification using SVM. In Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 239--248.Google ScholarDigital Library
- Matthew D. Zeiler. 2012. ADADELTA: An adaptive learning rate method. arXiv:1212.5701.Google Scholar
- Huiling Zhang, Md Abdul Alim, Xiang Li, My T. Thai, and Hien T. Nguyen. 2016. Misinformation in online social networks: Detect them all with a limited budget. ACM Transactions on Information Systems 34, 3 (2016), 18.Google ScholarDigital Library
- Qiao Zhang, Shuiyuan Zhang, Jian Dong, Jinhua Xiong, and Xueqi Cheng. 2015. Automatic detection of rumor on social network. In Natural Language Processing and Chinese Computing. Springer, 113--122.Google Scholar
- Zhe Zhao, Paul Resnick, and Qiaozhu Mei. 2015. Enquiring minds: Early detection of rumors in social media from enquiry posts. In Proceedings of the 24th International Conference on World Wide Web. 1395--1405.Google ScholarDigital Library
- Liang Zheng and Chee Wei Tan. 2015. A probabilistic characterization of the rumor graph boundary in rumor source detection. In Proceedings of the IEEE International Conference on Digital Signal Processing (DSP’15). IEEE, Los Alamitos, CA, 765--769.Google ScholarCross Ref
- Arkaitz Zubiaga, Maria Liakata, and Rob Procter. 2016. Learning reporting dynamics during breaking news for rumour detection in social media. arXiv:1610.07363.Google Scholar
- Arkaitz Zubiaga, Maria Liakata, and Rob Procter. 2017. Exploiting context for rumour detection in social media. In Proceedings of the International Conference on Social Informatics. 109--123.Google ScholarCross Ref
Index Terms
- FNED: A Deep Network for Fake News Early Detection on Social Media
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