H-Index & Metrics Best Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science D-index 30 Citations 3,914 88 World Ranking 8868 National Ranking 520

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • The Internet
  • Machine learning

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.

His most cited work include:

  • Analysing how people orient to and spread rumours in social media by looking at conversational threads (302 citations)
  • Detection and Resolution of Rumours in Social Media: A Survey (284 citations)
  • SemEval-2017 Task 8: RumourEval: Determining rumour veracity and support for rumours (135 citations)

What are the main themes of his work throughout his whole career to date?

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.

He most often published in these fields:

  • Social media (43.97%)
  • Artificial intelligence (36.17%)
  • Information retrieval (29.08%)

What were the highlights of his more recent work (between 2019-2021)?

  • Artificial intelligence (36.17%)
  • Natural language processing (22.70%)
  • Machine learning (12.77%)

In recent papers he was focusing on the following fields of study:

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.

Between 2019 and 2021, his most popular works were:

  • QMUL-SDS at CheckThat! 2020: Determining COVID-19 Tweet Check-Worthiness Using an Enhanced CT-BERT with Numeric Expressions (6 citations)
  • NUAA-QMUL at SemEval-2020 Task 8: Utilizing BERT and DenseNet for Internet Meme Emotion Analysis (2 citations)
  • Birds of a Feather Check Together: Leveraging Homophily for Sequential Rumour Detection (2 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • The Internet
  • Machine learning

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.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

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)

401 Citations

Detection and Resolution of Rumours in Social Media: A Survey

Arkaitz Zubiaga;Ahmet Aker;Kalina Bontcheva;Maria Liakata.
ACM Computing Surveys (2018)

368 Citations

Real-time classification of Twitter trends

Arkaitz Zubiaga;Damiano Spina;Raquel Martínez;Víctor Fresno.
association for information science and technology (2015)

169 Citations

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)

164 Citations

All-in-one: Multi-task Learning for Rumour Verification

Elena Kochkina;Maria Liakata;Arkaitz Zubiaga.
international conference on computational linguistics (2018)

123 Citations

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)

119 Citations

Exploiting Context for Rumour Detection in Social Media

Arkaitz Zubiaga;Maria Liakata;Maria Liakata;Rob Procter;Rob Procter.
social informatics (2017)

109 Citations

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)

107 Citations

Towards real-time summarization of scheduled events from twitter streams

Arkaitz Zubiaga;Damiano Spina;Enrique Amigó;Julio Gonzalo.
acm conference on hypertext (2012)

102 Citations

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)

102 Citations

If you think any of the details on this page are incorrect, let us know.

Contact us

Best Scientists Citing Arkaitz Zubiaga

Preslav Nakov

Preslav Nakov

Qatar Computing Research Institute

Publications: 78

Alberto Barrón-Cedeño

Alberto Barrón-Cedeño

University of Bologna

Publications: 24

Kalina Bontcheva

Kalina Bontcheva

University of Sheffield

Publications: 20

Heng Ji

Heng Ji

University of Illinois at Urbana-Champaign

Publications: 19

Maria Liakata

Maria Liakata

Turing Institute

Publications: 17

Marcos Zampieri

Marcos Zampieri

Rochester Institute of Technology

Publications: 15

Niloy Ganguly

Niloy Ganguly

Indian Institute of Technology Kharagpur

Publications: 12

Wei Gao

Wei Gao

UNSW Sydney

Publications: 11

Lluís Màrquez

Lluís Màrquez

Amazon (United States)

Publications: 10

Iryna Gurevych

Iryna Gurevych

University of Paderborn

Publications: 10

James Glass

James Glass

MIT

Publications: 9

Asif Ekbal

Asif Ekbal

Indian Institute of Technology Patna

Publications: 9

Kam-Fai Wong

Kam-Fai Wong

Chinese University of Hong Kong

Publications: 9

Paolo Rosso

Paolo Rosso

Universitat Politècnica de València

Publications: 9

Lutz Bornmann

Lutz Bornmann

Max Planck Society

Publications: 8

Kathleen M. Carley

Kathleen M. Carley

Carnegie Mellon University

Publications: 8

Something went wrong. Please try again later.