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
Natural language processing with Python
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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
Research works printed but not linked: 2
- Galitsky, B., 2015, March. Detecting Rumor and Disinformation by Web Mining. In 2015 AAAI Spring Symposium Series.
- O'Reilly Media Inc. NLTK Tweet Tokenizer (2015). Retrieved April 1, 2017, from http://www.nltk.org/api/nltk.tokenize.html Scikit-learn Chi-Squared Feature Selection, 2010. Retrieved April 1, 2017, from http://scikit- learn.org/stable/modules/generated/sklearn.feature_sel ection.chi2.html Scikit-learn Variance Threshold Feature Selection, 2010. Retrieved April 1, 2017, from http://scikit- learn.org/stable/modules/generated/sklearn.feature_sel ection.VarianceThreshold.html Scikit-learn Truncated SVD, 2010. Retrieved April 1, 2017, from http://scikit- learn.org/stable/modules/generated/sklearn.decomposi tion.TruncatedSVD.html NLTK NPS Chat Corpus Reader, 2015. Retrieved April 1, 2017, from http://www.nltk.org/_modules/nltk/corpus/reader/nps_ chat.html NLTK Opinion Lexicon Corpus Reader, 2015. Re- trieved April 1, 2017, from http://www.nltk.org/_modules/nltk/corpus/reader/opin ion_lexicon.html NLTK Categorized Sentences Corpus Reader. Re- trieved April 1, 2017, from http://www.nltk.org/_modules/nltk/corpus/reader/cate gorized_sents.html
Cited by
Items 101–106 of 106
An advanced ensemble approach for detecting fake news
Hansrajh, A. (2021) · DUT Open Scholar (Durban University of Technology) · doi:10.51415/10321/4093
Few Labels with Active Learning: From Weak to Strong Labels for Misinformation Detection
Alalawi, A., Alsuhaibani, A., Naseem, U., Suleiman, B., Jameel, S., & Razzak, I. (2025) · International World Wide Web Conference (WWW) · doi:10.1145/3701716.3718376
Salah, I., Berradia, T., Jouini, K., Cabani, A., & Korbaa, O. (2025) · Research Square · doi:10.21203/rs.3.rs-7482447/v1
Automated fact-checking for supporting sub-editing
Prieto-Chavana, N. (2023) · Sussex Research Online (University of Sussex)
Викторовна, И. С., & Шарифулловна, Х. Г. (n.d.) · CyberLeninK (CyberLeninka)
Socially Responsible and Explainable Automated Fact-Checking and Hate Speech Detection
Vargas, F., Pardo, T. A. S., & Benevenuto, F. (2026) · doi:10.5753/ctdiac.2026.33597
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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