Rumour Detection
20 papers with code • 1 benchmarks • 2 datasets
Rumor detection is the task of identifying rumors, i.e. statements whose veracity is not quickly or ever confirmed, in utterances on social media platforms.
Benchmarks
These leaderboards are used to track progress in Rumour Detection
| Trend | Dataset | Best Model | Paper | Code | Compare |
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Sepehr_RumTel01
Most implemented papers
Learning Reporting Dynamics during Breaking News for Rumour Detection in Social Media
yunzhusong/aard
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In this paper we introduce a novel approach to rumour detection that learns from the sequential dynamics of reporting during breaking news in social media to detect rumours in new stories.
Turing at SemEval-2017 Task 8: Sequential Approach to Rumour Stance Classification with Branch-LSTM
This paper describes team Turing's submission to SemEval 2017 RumourEval: Determining rumour veracity and support for rumours (SemEval 2017 Task 8, Subtask A).
Rumor Detection on Twitter with Tree-structured Recursive Neural Networks
Automatic rumor detection is technically very challenging.
Stance Classification for Rumour Analysis in Twitter: Exploiting Affective Information and Conversation Structure
On this line, a new shared task has been proposed at SemEval-2017 (Task 8, SubTask A), which is focused on rumour stance classification in English tweets.
BUT-FIT at SemEval-2019 Task 7: Determining the Rumour Stance with Pre-Trained Deep Bidirectional Transformers
MFajcik/RumourEval2019
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SEMEVAL 2019
This paper describes our system submitted to SemEval 2019 Task 7: RumourEval 2019: Determining Rumour Veracity and Support for Rumours, Subtask A (Gorrell et al., 2019).
CLEARumor at SemEval-2019 Task 7: ConvoLving ELMo Against Rumors
lschmelzeisen/clearumor
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SEMEVAL 2019
The goal of subtask B is to predict the veracity of a given rumor.
Danish Stance Classification and Rumour Resolution
Furthermore, experiments show that stance labels can be used across languages and platforms with a HMM to predict the veracity of rumours, achieving an accuracy of 0. 82 and F1 score of 0. 67.
Back to the Future -- Sequential Alignment of Text Representations
In particular, language evolution causes data drift between time-steps in sequential decision-making tasks.
Claim Check-Worthiness Detection as Positive Unlabelled Learning
copenlu/check-worthiness-pu-learning
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Findings of the Association for Computational Linguistics 2020
In applying this, we out-perform the state of the art in two of the three tasks studied for claim check-worthiness detection in English.
Estimating predictive uncertainty for rumour verification models
kochkinaelena/Uncertainty4VerificationModels
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ACL 2020
The inability to correctly resolve rumours circulating online can have harmful real-world consequences.
