The DOI matches its Crossref record; 13 of the 14 references it cites were found in a registry or an index.
NileTMRG at SemEval-2017 Task 8: Determining Rumour and Veracity Support for Rumours on Twitter.
Open accessAbstract
This paper presents the results and conclusions of our participation in SemEval-2017 task 8: Determining rumour veracity and support for rumours.We have participated in 2 subtasks: SDQC (Subtask A) which deals with tracking how tweets orient to the accuracy of a rumourous story, and Veracity Prediction (Subtask B) which deals with the goal of predicting the veracity of a given rumour.Our participation was in the closed task variant, in which the prediction is made solely from the tweet itself.For subtask A, linear support vector classification was applied to a model of bag of words, and the help of a naïve Bayes classifier was used for semantic feature extraction.For subtask B, a similar approach was used.Many features were used during the experimentation process but only a few proved to be useful with the data set provided.Our system achieved 71% accuracy and ranked 5th among 8 systems for subtask A and achieved 53% accuracy with the lowest RMSE value of 0.672 ranking at the first place among 5 systems for subtask B.
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“As discussed above, the approach reported by Enayet and El-Beltagy (2017), which aggregates the stance of individual tweets to determine the veracity of rumours as a three-way classification problem, is currently the best performing system.”
From Detection and Resolution of Rumours in Social Media: A Survey (2019) · cites this paper as Enayet and El-Beltagy (2017)
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“the best performing system in this task (Enayet and El-Beltagy 2017) considered the percentage of reply tweets classified as either support, deny, or query”
From Detection and Resolution of Rumours in Social Media: A Survey (2019) · cites this paper as Enayet and El-Beltagy 2017
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“Participants viewed the task either as a three-way (Enayet and El-Beltagy 2017;Wang et al. 2017;Singh et al. 2017) or two-way (Chen et al. 2017;Srivastava et al. 2017) single tweet classification task.”
From Detection and Resolution of Rumours in Social Media: A Survey (2019) · cites this paper as Enayet and El-Beltagy 2017
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Cites
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Enquiring Minds: Early Detection of Rumors in Social Media from Enquiry Posts
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Real-time Rumor Debunking on Twitter
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SemEval-2017 Task 8: RumourEval: Determining rumour veracity and support for rumours
Derczynski, L., Bontcheva, K., Liakata, M., Procter, R., Wong Sak Hoi, G., & Zubiaga, A. (2017) · Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017) · doi:10.18653/v1/s17-2006 · Cited by 323
SemEval-2019 Task 7: RumourEval, Determining Rumour Veracity and Support for Rumours
Gorrell, G., Kochkina, E., Liakata, M., Aker, A., Zubiaga, A., Bontcheva, K., & Derczynski, L. (2019) · International Workshop on Semantic Evaluation (SemEval) · doi:10.18653/v1/s19-2147 · Cited by 239
Rumor Detection and Classification for Twitter DataPreprint
Hamidian, S., & Diab, M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.08926 · Cited by 119
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Cited by
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
“…8), although some added features more specific to the distribution of stance labels in the tweets replying to the source tweet (for example, the best performing system in this task [Enayet and El-Beltagy 2017] considered the percentage of reply tweets classified as either support, deny or query).”
BackgroundMethodInfluentialA survey on fake news and rumour detection techniques
Bondielli, A., & Marcelloni, F. (2019) · Information Sciences · doi:10.1016/j.ins.2019.05.035 · Cited by 658
A Survey on Automated Fact-Checking
Guo, Z., Schlichtkrull, M., & Vlachos, A. (2022) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00454 · Cited by 500
DeClarE: Debunking Fake News and False Claims using Evidence-Aware Deep Learning
Popat, K., Mukherjee, S., Yates, A., & Weikum, G. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1003 · Cited by 414
Mining Dual Emotion for Fake News Detection
Zhang, X., Cao, J., Li, X., Sheng, Q., Zhong, L., & Shu, K. (2021) · International World Wide Web Conference (WWW) · doi:10.1145/3442381.3450004 · Cited by 366
Metadata
- Authors
- Enayet, O., & El-Beltagy, S. R.
- Affiliations
- Nile University (EG)
- Corresponding author
- Enayet, Omar
- Venue
- Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
- Published
- 2017
- OpenAlex
- W2752362131
- Cited by
- 100
- References
- 14
- Citations by year
- 2025: 6 · 2024: 11 · 2023: 7 · 2022: 10 · 2021: 12 · 2020: 14 · 2019: 25 · 2018: 11 · 2017: 3
- FWCI
- 45.30
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- International Workshop on Semantic Evaluation (SemEval) (publishedVersion, cc-by)
- Topics
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
- Keywords
- rumor detection, Twitter, bag-of-words features
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- Enayet, O., & El-Beltagy, S. R. (2017). NileTMRG at SemEval-2017 Task 8: Determining Rumour and Veracity Support for Rumours on Twitter. Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017). https://doi.org/10.18653/v1/s17-2082
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