A
Arkaitz Zubiaga
Researcher at Queen Mary University of London
Publications - 189
Citations - 5738
Arkaitz Zubiaga is an academic researcher from Queen Mary University of London. The author has contributed to research in topics: Social media & Computer science. The author has an hindex of 37, co-authored 162 publications receiving 4345 citations. Previous affiliations of Arkaitz Zubiaga include National University of Distance Education & University of Warwick.
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Journal ArticleDOI
Analysing how people orient to and spread rumours in social media by looking at conversational threads
TL;DR: The study shows that rumours that are ultimately proven true tend to be resolved faster than those that turn out to be false, and reinforces the need for developing robust machine learning techniques that can provide assistance in real time for assessing the veracity of rumours.
Journal ArticleDOI
Detection and Resolution of Rumours in Social Media: A Survey
TL;DR: The authors provide an overview of research into social media rumours with the ultimate goal of developing a rumour classification system that consists of four components: rumour detection, rumor tracking, rumour stance classification, and rumour veracity classification.
Proceedings ArticleDOI
SemEval-2017 Task 8: RumourEval: Determining rumour veracity and support for rumours
Leon Derczynski,Kalina Bontcheva,Maria Liakata,Rob Procter,Geraldine Wong Sak Hoi,Arkaitz Zubiaga +5 more
TL;DR: An annotation scheme is presented, a large dataset covering multiple topics – each having their own families of claims and replies – and these are used to pose two concrete challenges as well as the results achieved by participants on these challenges.
Journal ArticleDOI
Detection and Resolution of Rumours in Social Media: A Survey
TL;DR: This article introduces and discusses two types of rumours that circulate on social media: long-standing rumours that circulating for long periods of time, and newly emerging rumours spawned during fast-paced events such as breaking news, where reports are released piecemeal and often with an unverified status in their early stages.
Book ChapterDOI
Exploiting Context for Rumour Detection in Social Media
TL;DR: A novel approach using Conditional Random Fields that learns from the sequential dynamics of social media posts with the current state-of-the-art rumour detection system, as well as other baselines, and results provide evidence for the generalisability of the classifier.