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Rumor Identification and Belief Investigation on Twitter
Open accessAbstract
Social media users spend several hours a day to read, post and search for news on microblogging platforms.Social media is becoming a key means for discovering news.However, verifying the trustworthiness of this information is becoming even more challenging.In this study, we attempt to address the problem of rumor detection and belief investigation on Twitter.Our definition of rumor is an unverifiable statement, which spreads misinformation or disinformation.We adopt a supervised rumors classification task using the standard dataset.By employing the Tweet Latent Vector (TLV) feature, which creates a 100-d vector representative of each tweet, we increased the rumor retrieval task precision up to 0.972.We also introduce the belief score and study the belief change among the rumor posters between 2010 and 2016.
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“The authors compared the TLV approach to their own earlier system as well as to original features of Qazvinian et al. (2011) and showed that the TLV approach outperforms both baselines.”
From Detection and Resolution of Rumours in Social Media: A Survey (2019) · cites this paper as Hamidian and Diab (2016)
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Research works printed but not linked: 9
- Weiwei Guo and Mona Diab. 2012. Modeling sentences in the latent space. In Proceedings of the 50th Annual Meeting of the Association for Computational Linguis- tics: Long Papers-Volume 1, pages 864-872. Associa- tion for Computational Linguistics.
- Weiwei Guo, Wei Liu, and Mona T Diab. 2014. Fast tweet retrieval with compact binary codes. In COL- ING, pages 486-496.
- Mark Hall, Eibe Frank, Geoffrey Holmes, Bernhard Pfahringer, Peter Reutemann, and Ian H Witten. 2009. The weka data mining software: an update. ACM SIGKDD explorations newsletter, 11(1):10-18.
- Sardar Hamidiain and Mona Diab. 2015. Rumor detec- tion and classification for twitter data. The Fifth In- ternational Conference on Social Media Technologies, Communication, and Informatics, SOTICS, IARIA, pages 71-77.
- Alessandro Moschitti. 2004. A study on convolution ker- nels for shallow semantic parsing. In Proceedings of the 42nd Annual Meeting on Association for Compu- tational Linguistics, page 335. Association for Com- putational Linguistics.
- Vahed Qazvinian, Emily Rosengren, Dragomir R Radev, and Qiaozhu Mei. 2011. Rumor has it: Identify- ing misinformation in microblogs. In Proceedings of the Conference on Empirical Methods in Natural Lan- guage Processing, pages 1589-1599. Association for Computational Linguistics.
- Tatsuro Takahashi and Nobuyuki Igata. 2012. Rumor detection on twitter. In Soft Computing and Intelligent Systems (SCIS) and 13th International Symposium on Advanced Intelligent Systems (ISIS), 2012 Joint 6th In- ternational Conference on, pages 452-457. IEEE.
- Soroush Vosoughi. 2015. Automatic detection and ver- ification of rumors on Twitter. Ph.D. thesis, Mas- sachusetts Institute of Technology.
- Fan Yang, Yang Liu, Xiaohui Yu, and Min Yang. 2012. Automatic detection of rumor on sina weibo. In Pro- ceedings of the ACM SIGKDD Workshop on Mining Data Semantics, page 13. ACM.
Cited by
Sorted by citations
Detection and Resolution of Rumours in Social Media: A Survey
Zubiaga, A., Aker, A., Bontcheva, K., Liakata, M., & Procter, R. (2019) · ACM Computing Surveys · doi:10.1145/3161603 · Cited by 862
“In their recent paper, [Hamidian and Diab 2016] introduce the Tweet Latent Vector (TLV) approach that is obtained by applying the Semantic Textual Similarity model proposed by [Guo and Diab 2012].”
BackgroundMethodThe Web of False Information: Rumors, Fake News, Hoaxes, Clickbait, and Various Other Shenanigans
Zannettou, S., Sirivianos, M., Blackburn, J., & Kourtellis, N. (2019) · Journal of Data and Information Quality · doi:10.1145/3309699 · Cited by 86
Metadata
- Authors
- Hamidian, S., & Diab, M.
- Affiliations
- George Washington University (US)
- Venue
- Proceedings of the 7th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
- Published
- 2016
- Pages
- 3-8
- OpenAlex
- W2508989498
- Cited by
- 73
- References
- 0
- Citations by year
- 2025: 1 · 2024: 4 · 2023: 3 · 2022: 6 · 2021: 9 · 2020: 13 · 2019: 18 · 2018: 9 · 2017: 8 · 2016: 1
- FWCI
- 36.96
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (WASSA) (publishedVersion, cc-by)
- Funders
- Defense Advanced Research Projects Agency
- Topics
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Keywords
- rumor detection, Twitter, misinformation, disinformation, rumor verification, belief revision, microblogging
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