Arkaitz Zubiaga spends much of his time researching Social media, Artificial intelligence, Internet privacy, Natural language processing and World Wide Web. His study connects Openness to experience and Social media. He focuses mostly in the field of Artificial intelligence, narrowing it down to topics relating to Machine learning and, in certain cases, Multi-task learning.
His work on Fake news is typically connected to Focus, Misinformation and Scientific literature as part of general Internet privacy study, connecting several disciplines of science. His Natural language processing research is multidisciplinary, relying on both Annotation, Word and False accusation. His research integrates issues of Categorization, Automatic summarization and Set in his study of World Wide Web.
Arkaitz Zubiaga mainly investigates Social media, Artificial intelligence, Information retrieval, Natural language processing and World Wide Web. His Social media study incorporates themes from Context, Internet privacy, Journalism, The Internet and Data science. His Artificial intelligence research includes themes of Identification, Machine learning and Set.
His Information retrieval research is multidisciplinary, incorporating perspectives in Classifier, Web page, Metadata and Cluster analysis. His Natural language processing study combines topics from a wide range of disciplines, such as Annotation and Word, SemEval. His work on Bookmarking as part of general World Wide Web study is frequently connected to Work, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
His primary areas of study are Artificial intelligence, Natural language processing, Machine learning, Social media and Word. His Artificial intelligence research includes elements of Rumor and Selection. His studies in Natural language processing integrate themes in fields like Lexical semantics, SemEval, Internet meme and Residual neural network.
His Machine learning research includes elements of Democracy, Stance detection, Digital citizen and Knowledge graph. He combines subjects such as Hindi and The Internet with his study of Social media. His Word research incorporates themes from Class and Knowledge base.
His primary scientific interests are in Artificial intelligence, Information retrieval, Social media, Selection and Natural language processing. Artificial intelligence connects with themes related to The Internet in his study. His research in Information retrieval intersects with topics in Classifier and Fake news.
Arkaitz Zubiaga connects Social media with Event in his study. His study in Selection is interdisciplinary in nature, drawing from both Machine learning, Digital citizen, Democracy and Information overload. The study incorporates disciplines such as SemEval, Internet meme and Residual neural network in addition to Natural language processing.
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Analysing how people orient to and spread rumours in social media by looking at conversational threads
Arkaitz Zubiaga;Maria Liakata;Rob Procter;Geraldine Wong Sak Hoi.
PLOS ONE (2016)
Detection and Resolution of Rumours in Social Media: A Survey
Arkaitz Zubiaga;Ahmet Aker;Kalina Bontcheva;Maria Liakata.
ACM Computing Surveys (2018)
Real-time classification of Twitter trends
Arkaitz Zubiaga;Damiano Spina;Raquel Martínez;Víctor Fresno.
association for information science and technology (2015)
SemEval-2017 Task 8: RumourEval: Determining rumour veracity and support for rumours
Leon Derczynski;Kalina Bontcheva;Maria Liakata;Rob Procter.
meeting of the association for computational linguistics (2017)
All-in-one: Multi-task Learning for Rumour Verification
Elena Kochkina;Maria Liakata;Arkaitz Zubiaga.
international conference on computational linguistics (2018)
Classifying trending topics: a typology of conversation triggers on Twitter
Arkaitz Zubiaga;Damiano Spina;Víctor Fresno;Raquel Martínez.
conference on information and knowledge management (2011)
Exploiting Context for Rumour Detection in Social Media
Arkaitz Zubiaga;Maria Liakata;Maria Liakata;Rob Procter;Rob Procter.
social informatics (2017)
Hawkes processes for continuous time sequence classification : an application to rumour stance classification in Twitter
Michal Lukasik;P. K. Srijith;Duy Vu;Kalina Bontcheva.
meeting of the association for computational linguistics (2016)
Towards real-time summarization of scheduled events from twitter streams
Arkaitz Zubiaga;Damiano Spina;Enrique Amigó;Julio Gonzalo.
acm conference on hypertext (2012)
SemEval-2019 Task 7: RumourEval, Determining Rumour Veracity and Support for Rumours
Genevieve Gorrell;Elena Kochkina;Maria Liakata;Ahmet Aker.
north american chapter of the association for computational linguistics (2019)
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