Rumour Detection
22 papers with code • 1 benchmarks • 3 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
CIDII Dataset
Most implemented papers
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.
No Rumours Please! A Multi-Indic-Lingual Approach for COVID Fake-Tweet Detection
Towards this, we propose an approach to detect fake news about COVID-19 early on from social media, such as tweets, for multiple Indic-Languages besides English.
PADA: Example-based Prompt Learning for on-the-fly Adaptation to Unseen Domains
eyalbd2/PADA
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We address a challenging and underexplored version of this domain adaptation problem, where an algorithm is trained on several source domains, and then applied to examples from unseen domains that are unknown at training time.
A War Beyond Deepfake: Benchmarking Facial Counterfeits and Countermeasures
In recent years, visual forgery has reached a level of sophistication that humans cannot identify fraud, which poses a significant threat to information security.
MTLTS: A Multi-Task Framework To Obtain Trustworthy Summaries From Crisis-Related Microblogs
rajdeep345/mtlts
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Occurrences of catastrophes such as natural or man-made disasters trigger the spread of rumours over social media at a rapid pace.
Public Wisdom Matters! Discourse-Aware Hyperbolic Fourier Co-Attention for Social-Text Classification
The surface-level signals expressed by a social-text itself may not be adequate for such tasks; therefore, recent methods attempted to incorporate other intrinsic signals such as user behavior and the underlying graph structure.
CMA-R:Causal Mediation Analysis for Explaining Rumour Detection
ltian678/cma-r
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We apply causal mediation analysis to explain the decision-making process of neural models for rumour detection on Twitter.
Rumour Evaluation with Very Large Language Models
To the end, we employ two prompting-based LLM variants (GPT-3. 5-turbo and GPT-4) to extend the two RumourEval subtasks: (1) veracity prediction, and (2) stance classification.
