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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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Stance Classification in Rumours as a Sequential Task Exploiting the Tree Structure of Social Media Conversations
TL;DR: This work is the first to model Twitter conversations as a tree structure in this manner, introducing a novel way of tackling NLP tasks on Twitter conversations.
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All-in-one: Multi-task Learning for Rumour Verification
TL;DR: The authors propose a multi-task learning approach that allows joint training of the main and auxiliary tasks, improving the performance of rumour verification, and examine the connection between the dataset properties and the outcomes of the multitask learning models used.
Proceedings ArticleDOI
Towards real-time summarization of scheduled events from twitter streams
TL;DR: In this article, the authors deal with shrinking the stream of tweets for scheduled events in real-time, following two steps: (i) sub-event detection, which determines if something new has occurred, and (ii) tweet selection, which picks a tweet to describe each sub event.
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Tweet, but Verify: Epistemic Study of Information Verification on Twitter
Arkaitz Zubiaga,Heng Ji +1 more
TL;DR: In this article, the authors survey users on credibility perceptions regarding witness pictures posted on Twitter related to Hurricane Sandy and find that while author details not readily available on Twitter feeds should be emphasized in order to facilitate verification of tweets, showing multiple tweets corroborating a fact misleads users to trusting what actually is a hoax.
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Making the Most of Tweet-Inherent Features for Social Spam Detection on Twitter
TL;DR: In this paper, the authors focus on the detection of spam tweets, which optimises the amount of data that needs to be gathered by relying only on tweet-inherent features.