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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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Stance Classification and Rumor Analysis: Using New Dialog-Act Features and Augmenting Input Tweets
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Microblog Rumor Prediction Model and Analysis Based on Decision Tree
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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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
