ABSTRACT
The proliferation of social media has intensified the necessity for automated misinformation detection. Existing methods often struggle with early detection, as key information is not readily available during the initial dissemination stages. In this paper, we introduce a novel model for early misinformation detection on social media by classifying information propagation paths and leveraging linguistic patterns. Our model incorporates a causal user attribute inference model to label users as potential misinformation propagators or believers. Designed for early detection, the model includes two auxiliary tasks: forecasting the scope of misinformation dissemination and clustering similar nodes (users) based on their attributes outperforming the current state-of-the-art benchmarks.
- H. Allcott and M. Gentzkow. Social media and fake news in the 2016 election. Journal of economic perspectives, 31(2):211--236, 2017.Google ScholarCross Ref
- Y. Asim, A. K. Malik, B. Raza, and A. R. Shahid. A trust model for analysis of trust, influence and their relationship in social network communities. Telematics and Informatics, 36:94--116, 2019.Google ScholarCross Ref
- E. Chen and E. Ferrara. Tweets in time of conflict: A public dataset tracking the twitter discourse on the war between ukraine and russia. arXiv preprint arXiv:2203.07488, 2022.Google Scholar
- L. Derczynski and K. Bontcheva. Pheme: Veracity in digital social networks. In UMAP workshops, 2014.Google Scholar
- U. Ezeakunne, S. M. Ho, and X. Liu. Sentiment and retweet analysis of user response for early fake news detection. In The International Conference on Social Computing, Behavioral-Cultural Modeling, & Prediction and Behavior Representation in Modeling and Simulation (SBP-BRiMS'20), pages 1--10, 2020.Google Scholar
- K. Hayawi, S. Shahriar, M. A. Serhani, I. Taleb, and S. S. Mathew. Anti-vax: a novel twitter dataset for covid-19 vaccine misinformation detection. Public health, 203:23--30, 2022.Google ScholarCross Ref
- M. Z. Hossain, M. A. Rahman, M. S. Islam, and S. Kar. Banfakenews: A dataset for detecting fake news in bangla. arXiv preprint arXiv:2004.08789, 2020.Google Scholar
- S. Jiang and C. Wilson. Linguistic signals under misinformation and fact-checking: Evidence from user comments on social media. Proceedings of the ACM on Human-Computer Interaction, 2(CSCW):1--23, 2018.Google ScholarDigital Library
- Z. Jin, A. Lalwani, T. Vaidhya, X. Shen, Y. Ding, Z. Lyu, M. Sachan, R. Mihalcea, and B. Schölkopf. Logical fallacy detection. arXiv preprint arXiv:2202.13758, 2022.Google Scholar
- Y. Liu and Y.-F. B. Wu. Fned: a deep network for fake news early detection on social media. ACM Transactions on Information Systems (TOIS), 38(3):1--33, 2020.Google Scholar
- P. A. Lofgren, S. Banerjee, A. Goel, and C. Seshadhri. Fast-ppr: Scaling personalized pagerank estimation for large graphs. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 1436--1445, 2014.Google ScholarDigital Library
- J. Ma, W. Gao, P. Mitra, S. Kwon, B. J. Jansen, K.-F. Wong, and M. Cha. Detecting rumors from microblogs with recurrent neural networks. 2016.Google Scholar
- F. Monti, F. Frasca, D. Eynard, D. Mannion, and M. M. Bronstein. Fake news detection on social media using geometric deep learning. arXiv preprint arXiv:1902.06673, 2019.Google Scholar
- D. Q. Nguyen, T. Vu, and A. T. Nguyen. BERTweet: A pre-trained language model for English Tweets. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 9--14, 2020.Google ScholarCross Ref
- P. Patwa, S. Sharma, S. Pykl, V. Guptha, G. Kumari, M. S. Akhtar, A. Ekbal, A. Das, and T. Chakraborty. Fighting an infodemic: Covid-19 fake news dataset. In Combating Online Hostile Posts in Regional Languages during Emergency Situation: First International Workshop, CONSTRAINT 2021, Collocated with AAAI 2021, Virtual Event, February 8, 2021, Revised Selected Papers 1, pages 21--29. Springer, 2021.Google Scholar
- H. Rashkin, E. Choi, J. Y. Jang, S. Volkova, and Y. Choi. Truth of varying shades: Analyzing language in fake news and political fact-checking. In Proceedings of the 2017 conference on empirical methods in natural language processing, pages 2931--2937, 2017.Google ScholarCross Ref
- N. Ruchansky, S. Seo, and Y. Liu. Csi: A hybrid deep model for fake news detection. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pages 797--806, 2017.Google ScholarDigital Library
- N. Seddari, A. Derhab, M. Belaoued, W. Halboob, J. Al-Muhtadi, and A. Bouras. A hybrid linguistic and knowledge-based analysis approach for fake news detection on social media. IEEE Access, 10:62097--62109, 2022.Google ScholarCross Ref
- K. Shu, D. Mahudeswaran, S. Wang, D. Lee, and H. Liu. Fakenewsnet: A data repository with news content, social context, and spatiotemporal information for studying fake news on social media. Big data, 8(3):171--188, 2020.Google ScholarCross Ref
- K. Shu, A. Sliva, S. Wang, J. Tang, and H. Liu. Fake news detection on social media: A data mining perspective. ACM SIGKDD explorations newsletter, 19(1):22--36, 2017.Google Scholar
- J. A. Tucker, A. Guess, P. Barberá, C. Vaccari, A. Siegel, S. Sanovich, D. Stukal, and B. Nyhan. Social media, political polarization, and political disinformation: A review of the scientific literature. Political polarization, and political disinformation: a review of the scientific literature (March 19, 2018), 2018.Google Scholar
- S. Volkova, K. Shaffer, J. Y. Jang, and N. Hodas. Separating facts from fiction: Linguistic models to classify suspicious and trusted news posts on twitter. In Proceedings of the 55th annual meeting of the association for computational linguistics (volume 2: Short papers), pages 647--653, 2017.Google ScholarCross Ref
- Y. Wang, M. McKee, A. Torbica, and D. Stuckler. Systematic literature review on the spread of health-related misinformation on social media. Social science & medicine, 240:112552, 2019.Google Scholar
- L. Wu and H. Liu. Tracing fake-news footprints: Characterizing social media messages by how they propagate. In Proceedings of the eleventh ACM international conference on Web Search and Data Mining, pages 637--645, 2018.Google ScholarDigital Library
- Y. Yang, L. Zheng, J. Zhang, Q. Cui, Z. Li, and P. S. Yu. Ti-cnn: Convolutional neural networks for fake news detection. arXiv preprint arXiv:1806.00749, 2018.Google Scholar
- F. Yu, Q. Liu, S. Wu, L. Wang, T. Tan, et al. A convolutional approach for misinformation identification. In IJCAI, pages 3901--3907, 2017.Google ScholarCross Ref
- Z. Yue, H. Zeng, Z. Kou, L. Shang, and D. Wang. Contrastive domain adaptation for early misinformation detection: A case study on covid-19. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management, pages 2423--2433, 2022.Google ScholarDigital Library
- H. Zhang, S. Qian, Q. Fang, and C. Xu. Multimodal disentangled domain adaption for social media event rumor detection. IEEE Transactions on Multimedia, 23:4441--4454, 2020.Google ScholarCross Ref
- X. Zhang, Y. Malkov, O. Florez, S. Park, B. McWilliams, J. Han, and A. El-Kishky. Twhin-bert: a socially-enriched pre-trained language model for multilingual tweet representations. arXiv preprint arXiv:2209.07562, 2022.Google Scholar
- C. Zhou, K. Li, and Y. Lu. Linguistic characteristics and the dissemination of misinformation in social media: The moderating effect of information richness. Information Processing& Management, 58(6):102679, 2021.Google ScholarDigital Library
Index Terms
- Catching Lies in the Act: A Framework for Early Misinformation Detection on Social Media
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