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20162024
most citedLearning Reporting Dynamics during Breaking News for Rumour Detection in Social Media

153 citations · 180 across the 9 of their papers we have counts for

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8 papers · 1 filter

cs.CL2024

Generating Zero-shot Abstractive Explanations for Rumour Verification

Iman Munire Bilal, Preslav Nakov, Rob Procter +1

The task of rumour verification in social media concerns assessing the veracity of a claim on the basis of conversation threads that result from it. While previous work has focused…

cs.CL20233 cited

A User-Centered, Interactive, Human-in-the-Loop Topic Modelling System

Zheng Fang, Lama Alqazlan, Du Liu +2

Human-in-the-loop topic modelling incorporates users' knowledge into the modelling process, enabling them to refine the model iteratively. Recent research has demonstrated the valu…

cs.CL2023

PANACEA: An Automated Misinformation Detection System on COVID-19

Runcong Zhao, Miguel Arana-Catania, Lixing Zhu +6

In this demo, we introduce a web-based misinformation detection system PANACEA on COVID-19 related claims, which has two modules, fact-checking and rumour detection. Our fact-check…

cs.CL20221 cited

Template-based Abstractive Microblog Opinion Summarisation

Iman Munire Bilal, Bo Wang, Adam Tsakalidis +3

We introduce the task of microblog opinion summarisation (MOS) and share a dataset of 3100 gold-standard opinion summaries to facilitate research in this domain. The dataset contai…

cs.CL2022

Supporting peace negotiations in the Yemen war through machine learning

M. Arana-Catania, F. A. Van Lier, Rob Procter

Today's conflicts are becoming increasingly complex, fluid and fragmented, often involving a host of national and international actors with multiple and often divergent interests.…

cs.CL2016153 cited

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

Arkaitz Zubiaga, Maria Liakata, Rob Procter

Breaking news leads to situations of fast-paced reporting in social media, producing all kinds of updates related to news stories, albeit with the caveat that some of those early u…