World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
39
Citations
8883
World Ranking
9589
National Ranking
598

Maria Liakata publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Maria Liakata sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 140 publications — 23rd percentile

23% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Maria Liakata D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Maria Liakata sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 39 D-Index — 33rd percentile

33% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Overview

Maria Liakata is affiliated with Queen Mary University of London in the United Kingdom. Their research is primarily situated within the field of Computer Science, with a notable focus on subfields such as Artificial Intelligence, Sociology and Political Science, Statistical and Nonlinear Physics, Applied Psychology, and Experimental and Cognitive Psychology.

Their work spans a range of topics, including:

  • Topic Modeling
  • Natural Language Processing Techniques
  • Advanced Text Analysis Techniques
  • Misinformation and Its Impacts
  • Digital Mental Health Interventions
  • Complex Network Analysis Techniques
  • Mental Health via Writing

Maria Liakata has contributed to several recent publications, including:

  • Natural Language Processing markers in first episode psychosis and people at clinical high-risk, 2021, Translational Psychiatry
  • Development and validation of open-source deep neural networks for comprehensive chest x-ray reading: a retrospective, multicentre study, 2023, The Lancet Digital Health
  • Identifying Moments of Change from Longitudinal User Text, 2022, Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  • Evaluating the generalisability of neural rumour verification models, 2022, Information Processing & Management
  • Birds of a feather check together: Leveraging homophily for sequential rumour detection, 2020, Online Social Networks and Media

Their frequent co-authors include:

  • Adam Tsakalidis
  • Arkaitz Zubiaga
  • Rob Procter
  • Elena Kochkina
  • Yulan He

Maria Liakata's publications are often found in venues such as:

  • arXiv (Cornell University)
  • Zenodo (CERN European Organization for Nuclear Research)
  • Translational Psychiatry
  • The Lancet Digital Health
  • Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Best Publications

  • Detection and Resolution of Rumours in Social Media: A Survey

    Arkaitz Zubiaga;Ahmet Aker;Kalina Bontcheva;Maria Liakata

  • The Automation of Science

    Ross Donald King;Jeremy John Rowland;Jeremy John Rowland;Stephen G. Oliver;Stephen G. Oliver;Michael Young

  • 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

  • Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC-2016)

    Daniel Duma;Maria Liakata;James Ravenscroft;Amanda Clare

  • SemEval-2017 Task 8: RumourEval: Determining rumour veracity and support for rumours

    Leon Derczynski;Kalina Bontcheva;Maria Liakata;Rob Procter

  • Exploiting Context for Rumour Detection in Social Media

    Arkaitz Zubiaga;Maria Liakata;Maria Liakata;Rob Procter;Rob Procter

  • Characterisation of mental health conditions in social media using Informed Deep Learning

    George Gkotsis;Anika Oellrich;Sumithra Velupillai;Sumithra Velupillai;Maria Liakata

  • Using clinical Natural Language Processing for health outcomes research: Overview and actionable suggestions for future advances

    Sumithra Velupillai;Sumithra Velupillai;Hanna Suominen;Hanna Suominen;Maria Liakata;Angus Roberts

  • Measuring scientific impact beyond academia: an assessment of existing impact metrics and proposed improvements

    James Edward Ravenscroft;Maria Liakata;Amanda Clare;Daniel Duma

  • SemEval-2019 Task 7: RumourEval, Determining Rumour Veracity and Support for Rumours

    Genevieve Gorrell;Elena Kochkina;Maria Liakata;Ahmet Aker

  • Automatic recognition of conceptualization zones in scientific articles and two life science applications

    Maria Liakata;Shyamasree Saha;Simon Dobnik;Colin Batchelor

  • Towards Robot Scientists for autonomous scientific discovery

    Andrew Charles Sparkes;Wayne Aubrey;Emma Louise Byrne;Amanda Janet Clare

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

    Arkaitz Zubiaga;Maria Liakata;Rob Procter

  • Discourse-aware rumour stance classification in social media using sequential classifiers

    Arkaitz Zubiaga;Elena Kochkina;Elena Kochkina;Maria Liakata;Maria Liakata;Rob Procter;Rob Procter

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

    Elena Kochkina;Maria Liakata;Arkaitz Zubiaga

  • Corpora for the Conceptualisation and Zoning of Scientific Papers

    Maria Liakata;Simone Teufel;Advaith Siddharthan;Colin R. Batchelor

  • tBERT: Topic Models and BERT Joining Forces for Semantic Similarity Detection

    Nicole Peinelt;Dong Nguyen;Maria Liakata

  • How We Do Things With Words: Analyzing Text as Social and Cultural Data.

    Dong Nguyen;Maria Liakata;Maria Liakata;Maria Liakata;Simon DeDeo;Jacob Eisenstein

  • Turing at SemEval-2017 Task 8: Sequential Approach to Rumour Stance Classification with Branch-LSTM

    Elena Kochkina;Maria Liakata;Isabelle Augenstein

  • The language of mental health problems in social media

    George Gkotsis;Anika Oellrich;Tim J. P. Hubbard;Richard J. B. Dobson

  • Towards detecting rumours in social media

    Arkaitz Zubiaga;Maria Liakata;Rob Procter;Kalina Bontcheva

Frequent Co-Authors

Arkaitz Zubiaga
Arkaitz Zubiaga Queen Mary University of London
Rob Procter
Rob Procter University of Warwick
Kalina Bontcheva
Kalina Bontcheva University of Sheffield
Ross D. King
Ross D. King University of Manchester
Dietrich Rebholz-Schuhmann
Dietrich Rebholz-Schuhmann University of Cologne
Terry Lyons
Terry Lyons University of Oxford
Stephen Pulman
Stephen Pulman University of Oxford
Stephen G. Oliver
Stephen G. Oliver University of Cambridge
Mark Rouncefield
Mark Rouncefield Lancaster University
Tim Hubbard
Tim Hubbard King's College London

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