Abstract
People nowadays increasingly consume information from social media due to its convenience and fast dissemination. However, social media also accelerate the propagation of disinformation and fake news, causing detrimental effects. Thus, detecting fake news is a critical task for benefiting individuals and society. However, it is a non-trivial task due to the expensive annotation cost and the diverse nature of news domains. Thus, it is important to exploit auxiliary information to help improve prediction performance. We develop a deep architecture to exploit both cross-domain knowledge transfer and within-domain joint modeling of news content, user comments, and user-news interactions for fake news detection. Extensive experiments on real-world datasets demonstrate the effectiveness of the proposed method, even with limited labeled data in the target domain.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
In this paper, we are interested in fake news as a sample medium for disinformation but we recognize that other media exist.
- 2.
The code and partial data are available at https://www.dropbox.com/s/zax0guj0nb9hhmy/Fake
%20News%20Classifier.zip?dl=0#.
References
H. Allcott, M. Gentzkow, Social Media and Fake News in the 2016 Election, Technical report (NBER, 2017)
R. Baly, G. Karadzhov, D. Alexandrov, J. Glass, P. Nakov, Predicting factuality of reporting and bias of news media sources (2018). arXiv:1810.01765
S. Ben-David, J. Blitzer, K. Crammer, A. Kulesza, F. Pereira, J.W. Vaughan, A theory of learning from different domains. Mach. Learn. 79(1–2), 151–175 (2010)
J. Blitzer, M. Dredze, F. Pereira, Biographies, bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification, in Proceedings of the 45th Annual Meeting of the ACL (2017), pp. 440–447
K. Bousmalis, G. Trigeorgis, N. Silberman, D. Krishnan, D. Erhan, Domain separation networks, in NeurIPS (2016)
V. Carrieri, L. Madio, F. Principe, Vaccine hesitancy and (fake) news: Quasi-experimental evidence from italy. Health Econ. 28(11), 1377–1382 (2019)
C. Castillo, M. Mendoza, B. Poblete, Information credibility on twitter, in WWW (2011)
N.K. Conroy, V.L. Rubin, Y. Chen, Automatic deception detection: Methods for finding fake news. Proc. Assoc. Inf. Sci. Technol. 52(1), 1–4 (2015)
J. Devlin, M.W. Chang, K. Lee, K. Toutanova, Bert: Pre-training of deep bidirectional transformers for language understanding (2018). arXiv:1810.04805
C. Gong, J. Yu, R. Xia, Unified feature and instance based domain adaptation for end-to-end aspect-based sentiment analysis, in EMNLP (2020)
C. Guo, J. Cao, X. Zhang, K. Shu, H. Liu, Dean: Learning dual emotion for fake news detection on social media (2019). arXiv:1903.01728
H. Guo, J. Cao, Y. Zhang, J. Guo, J. Li, Rumor detection with hierarchical social attention network, in CIKM (2018)
S. Hosseinimotlagh, E.F. Papalexakis, Unsupervised content-based identification of fake news articles with tensor decomposition ensembles, in MIS2 (2018)
Z. Jin, J. Cao, Y. Zhang, J. Luo, News verification by exploiting conflicting social viewpoints in microblogs, in AAAI (2016)
Z. Jin, J. Cao, Y. Zhang, J. Zhou, Q. Tian, Novel visual and statistical image features for microblogs news verification. IEEE Trans. Multimedia 19(3), 598–608 (2016)
H. Karimi, J. Tang, Learning hierarchical discourse-level structure for fake news detection (2019). arXiv:1903.07389
Y. Kim, Convolutional neural networks for sentence classification (2014). arXiv:1408.5882
D.P. Kingma, J. Ba, Adam: A method for stochastic optimization (2014). arXiv:1412.6980
W.M. Kouw, L.J. Van Der Maaten, J.H. Krijthe, M. Loog, Feature-level domain adaptation. J. Mach. Learn. Res. (2016)
Q. Le, T. Mikolov, Distributed representations of sentences and documents, in ICML (2014)
M.T. Luong, H. Pham, C.D. Manning, Effective approaches to attention-based neural machine translation (2015). arXiv:1508.04025
S. McKay, C. Tenove, Disinformation as a threat to deliberative democracy. Polit. Res. Q. 1065912920938143 (2020)
S.J. Pan, Q. Yang, A survey on transfer learning. IEEE Trans. Knowl. Data Eng 22(10), 1345–1359 (2009)
M. Potthast, J. Kiesel, K. Reinartz, J. Bevendorff, B. Stein, A stylometric inquiry into hyperpartisan and fake news (2017). arXiv:1702.05638
F. Qian, C. Gong, K. Sharma, Y. Liu, Neural user response generator: fake news detection with collective user intelligence, in IJCAI (2018)
H. Rashkin, E. Choi, J.Y. Jang, S. Volkova, Y. Choi, Truth of varying shades: analyzing language in fake news and political fact-checking, in EMNLP (2017), pp. 2931–2937
N. Ruchansky, S. Seo, Y. Liu, Csi: a hybrid deep model for fake news detection, in CIKM (2017)
V. Sanh, L. Debut, J. Chaumond, T. Wolf, Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter (2019). arXiv:1910.01108
G. Sheares, R. Miklencicova, M. Grupac, The viral power of fake news: subjective social insecurity, covid-19 damaging misinformation, and baseless conspiracy theories. Linguist. Philos. Investig. 19, 121–127 (2020)
K. Shu, H.R. Bernard, H. Liu, Studying fake news via network analysis: detection and mitigation, in Emerging Research Challenges and Opportunities in Computational Social Network Analysis and Mining (Springer, Berlin, 2019), pp. 43–65
K. Shu, L. Cui, S. Wang, D. Lee, H. Liu, Defend: explainable fake news detection, in KDD (2019)
K. Shu, D. Mahudeswaran, S. Wang, D. Lee, H. Liu, Fakenewsnet: a data repository with news content, social context and dynamic information for studying fake news on social media (2018). arXiv:1809.01286
K. Shu, A. Sliva, S. Wang, J. Tang, H. Liu, Fake news detection on social media: a data mining perspective. ACM SIGKDD Explor. (2017)
K. Shu, S. Wang, H. Liu, Beyond news contents: the role of social context for fake news detection, in Proceedings of the twelfth ACM international conference on web search and data mining (2019), pp. 312–320
C. Sun, L. Huang, X. Qiu, Utilizing bert for aspect-based sentiment analysis via constructing auxiliary sentence (2019). arXiv:1903.09588
C. Sun, X. Qiu, Y. Xu, X. Huang, How to fine-tune bert for text classification? in China National Conference on Chinese Computational Linguistics (Springer, Berlin, 2019), pp. 194–206
E. Tacchini, G. Ballarin, M.L. Della Vedova, S. Moret, L. de Alfaro, Some like it hoax: Automated fake news detection in social networks (2017). arXiv:1704.07506
E. Tzeng, J. Hoffman, K. Saenko, T. Darrell, Adversarial discriminative domain adaptation, in CVPR (2017)
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, I. Polosukhin, Attention is all you need, in NeurIPS (2017)
G.A. Vlad, M.A. Tanase, C. Onose, D.C. Cercel, Sentence-level propaganda detection in news articles with transfer learning and bert-bilstm-capsule model, in Proceedings of the Second Workshop on NLP for Internet Freedom (2019). pp. 148–154
Y. Wang, F. Ma, Z. Jin, Y. Yuan, G. Xun, K. Jha, L. Su, J. Gao, Eann: event adversarial neural networks for multi-modal fake news detection, in KDD (2018)
H. Xu, B. Liu, L. Shu, P.S. Yu, Bert post-training for review reading comprehension and aspect-based sentiment analysis (2019). arXiv:1904.02232
S. Yang, K. Shu, S. Wang, R. Gu, F. Wu, H. Liu, Unsupervised fake news detection on social media: a generative approach, in AAAI (2019)
P. Zhou, Z. Qi, S. Zheng, J. Xu, H. Bao, B. Xu, Text classification improved by integrating bidirectional lstm with two-dimensional max pooling (2016). arXiv:1611.06639
X. Zhou, R. Zafarani, Fake news: a survey of research, detection methods, and opportunities (2018) arXiv:1812.00315
F. Zhuang, X. Cheng, P. Luo, S.J. Pan, Q. He, Supervised representation learning: transfer learning with deep autoencoders, in AAAI (2015)
F. Zhuang, P. Luo, H. Xiong, Y. Xiong, Q. He, Z. Shi, Cross-domain learning from multiple sources: a consensus regularization perspective. IEEE Trans. KDE (2009)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Shu, K., Mosallanezhad, A., Liu, H. (2022). Cross-Domain Fake News Detection on Social Media: A Context-Aware Adversarial Approach. In: Khosravy, M., Echizen, I., Babaguchi, N. (eds) Frontiers in Fake Media Generation and Detection. Studies in Autonomic, Data-driven and Industrial Computing. Springer, Singapore. https://doi.org/10.1007/978-981-19-1524-6_9
Download citation
DOI: https://doi.org/10.1007/978-981-19-1524-6_9
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-1523-9
Online ISBN: 978-981-19-1524-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)