Rumour Detection

21 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.

Most implemented papers

Learning Reporting Dynamics during Breaking News for Rumour Detection in Social Media

yunzhusong/aard 24 Oct 2016

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.

The Surprising Performance of Simple Baselines for Misinformation Detection

ComplexData-MILA/misinfo-baselines 14 Apr 2021

As social media becomes increasingly prominent in our day to day lives, it is increasingly important to detect informative content and prevent the spread of disinformation and unverified rumours.

Turing at SemEval-2017 Task 8: Sequential Approach to Rumour Stance Classification with Branch-LSTM

seongjinpark-88/RumorEval2019 SEMEVAL 2017

This paper describes team Turing's submission to SemEval 2017 RumourEval: Determining rumour veracity and support for rumours (SemEval 2017 Task 8, Subtask A).

Stance Classification for Rumour Analysis in Twitter: Exploiting Affective Information and Conversation Structure

dadangewp/SemEval2017-RumourEval 7 Jan 2019

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 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).

Danish Stance Classification and Rumour Resolution

danish-stance-detectors/RumourResolution 2 Jul 2019

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

wmkouw/ssa-nlp 8 Sep 2019

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 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.