SOTAVerified

Rumour Detection

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.

Papers

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Showing 21–30 of 98 papers

TitleStatusHype
Rumour detection using graph neural network and oversampling in benchmark Twitter dataset—0
An Emotion-Aware Multi-Task Approach to Fake News and Rumour Detection using Transfer Learning—0
Domain Generalization for Text Classification with Memory-Based Supervised Contrastive LearningCode0
Model-Agnostic and Diverse Explanations for Streaming Rumour Graphs—0
Detecting Rumours with Latency Guarantees using Massive Streaming Data—0
Evaluating BERT-based Pre-training Language Models for Detecting Misinformation—0
Vital Node Identification in Complex Networks Using a Machine Learning-Based Approach—0
DUCK: Rumour Detection on Social Media by Modelling User and Comment Propagation Networks—0
Bi-Directional Recurrent Neural Ordinary Differential Equations for Social Media Text Classification—0
A Survey on Stance Detection for Mis- and Disinformation Identification—0
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All datasetsSepehr_RumTel011

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ParsBERT+PCapsNet +SA+Title+ AuxiliaryF-Measure0.95—Unverified
2BERT-SAWSF-Measure0.93—Unverified
3Jahanbakhsh-Nagadeh et al.F-Measure0.83—Unverified
4Common context features + Four SA classesF-Measure0.79—Unverified
#ModelMetricClaimedVerifiedStatus
1CMA_R0..5sec1—Unverified