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IKM at SemEval-2017 Task 8: Convolutional Neural Networks for stance detection and rumor verification
Open accessAbstract
This paper describes our approach for SemEval-2017 Task 8. We aim at detecting the stance of tweets and determining the veracity of the given rumor. We utilize a convolutional neural network for short text categorization using multiple filter sizes. Our approach beats the baseline classifiers on different event data with good F1 scores. The best of our submitted runs achieves rank 1st among all scores on subtask B.
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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 Chen et al. 2017
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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
“The systems with the least elaborate features were [Chen et al. 2017] and [Garcı́a Lozano et al. 2017] for CNNs (word embeddings), [Srivastava et al. 2017] (sparse word vectors as input to logistic regression) and [Kochkina et al. 2017] (average word vectors, punctuation, similarity between word…”
MethodA 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
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
Metadata
- Authors
- Chen, Y.-C., Liu, Z.-Y., & Kao, H.-Y.
- Affiliations
- National Cheng Kung University (TW)
- Corresponding author
- Kao, Hung-Yu
- ORCID
- Kao, Hung-Yu
- Venue
- Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
- Published
- 2017
- Pages
- 465-469
- OpenAlex
- W2753727548
- Cited by
- 107
- References
- 0
- Citations by year
- 2025: 5 · 2024: 12 · 2023: 5 · 2022: 13 · 2021: 17 · 2020: 25 · 2019: 18 · 2018: 8 · 2017: 4
- FWCI
- 44.03
- 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 › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- stance detection, rumor verification, convolutional neural networks, tweets, event data
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