World's Best Scientists 2026 revealed!
Isabelle Augenstein

Isabelle Augenstein

Award Badge
Rising Stars
2025

D-Index & Metrics

Rising Stars

D-Index
40
Citations
6217
World Ranking
660
National Ranking
2

Computer Science

D-Index
34
Citations
5114
World Ranking
12156
National Ranking
59

Research.com Recognitions

  • 2025 - Research.com Rising Stars Award

Overview

Isabelle Augenstein is affiliated with the University of Copenhagen in Denmark. Their research primarily spans the field of Computer Science, with a focus on subfields such as Artificial Intelligence, Sociology and Political Science, Information Systems, Gender Studies, and Computer Vision and Pattern Recognition.

The scientist's work covers a range of topics, including:

  • Topic Modeling
  • Natural Language Processing Techniques
  • Hate Speech and Cyberbullying Detection
  • Misinformation and Its Impacts
  • Explainable Artificial Intelligence (XAI)
  • Sentiment Analysis and Opinion Mining
  • Ethics and Social Impacts of AI

Isabelle Augenstein has contributed to numerous scholarly venues, frequently publishing in:

  • arXiv (Cornell University)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • ACM Computing Surveys
  • Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
  • PLoS ONE

Their recent papers highlight diverse research interests and include:

  • Factuality challenges in the era of large language models and opportunities for fact-checking, 2024, Nature Machine Intelligence
  • Information fusion as an integrative cross-cutting enabler to achieve robust, explainable, and trustworthy medical artificial intelligence, 2021, Information Fusion
  • A Survey on Stance Detection for Mis- and Disinformation Identification, 2022, Findings of the Association for Computational Linguistics: NAACL 2022
  • A Survey on Gender Bias in Natural Language Processing, 2021, arXiv (Cornell University)
  • Detecting Harmful Content on Online Platforms: What Platforms Need vs. Where Research Efforts Go, 2023, ACM Computing Surveys

Frequent collaborators with whom Augenstein has co-authored multiple publications include:

  • Arnav Arora
  • Pepa Atanasova
  • Preslav Nakov
  • Christina Lioma
  • Karolina Stańczak

Best Publications

  • Stance detection with bidirectional conditional encoding

    Isabelle Augenstein;Tim Rocktäschel;Andreas Vlachos;Kalina Bontcheva

  • SemEval 2017 Task 10: ScienceIE - Extracting Keyphrases and Relations from Scientific Publications

    Isabelle Augenstein;Mrinal Das;Sebastian Riedel;Lakshmi Vikraman

  • emoji2vec: Learning Emoji Representations from their Description

    Ben Eisner;Tim Rocktäschel;Isabelle Augenstein;Matko Bosnjak

  • Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

    Unknown

  • Latent Multi-Task Architecture Learning

    Sebastian Ruder;Joachim Bingel;Isabelle Augenstein;Anders Søgaard

  • A simple but tough-to-beat baseline for the Fake News Challenge stance detection task

    Benjamin Riedel;Isabelle Augenstein;Georgios P. Spithourakis;Sebastian Riedel

  • MultiFC: A Real-World Multi-Domain Dataset for Evidence-Based Fact Checking of Claims

    Isabelle Augenstein;Christina Lioma;Dongsheng Wang;Lucas Chaves Lima

  • Information fusion as an integrative cross-cutting enabler to achieve robust, explainable, and trustworthy medical artificial intelligence

    Andreas Holzinger;Andreas Holzinger;Matthias Dehmer;Frank Emmert-Streib;Rita Cucchiara

  • A Diagnostic Study of Explainability Techniques for Text Classification.

    Pepa Atanasova;Jakob Grue Simonsen;Christina Lioma;Isabelle Augenstein

  • Sluice networks: Learning what to share between loosely related tasks.

    Sebastian Ruder;Joachim Bingel;Isabelle Augenstein;Anders Søgaard

  • 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

  • Learning what to share between loosely related tasks

    Sebastian Ruder;Joachim Bingel;Isabelle Augenstein;Anders Søgaard

  • Generating Fact Checking Explanations

    Pepa Atanasova;Jakob Grue Simonsen;Christina Lioma;Isabelle Augenstein

  • LODifier: generating linked data from unstructured text

    Isabelle Augenstein;Sebastian Padó;Sebastian Rudolph

  • Generalisation in named entity recognition

    Isabelle Augenstein;Leon Derczynski;Kalina Bontcheva

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

    Elena Kochkina;Maria Liakata;Isabelle Augenstein

  • A Survey on Stance Detection for Mis- and Disinformation Identification

    Momchil Hardalov;Arnav Arora;Preslav Nakov;Isabelle Augenstein

  • Zero-Shot Cross-Lingual Transfer with Meta Learning

    Farhad Nooralahzadeh;Giannis Bekoulis;Johannes Bjerva;Isabelle Augenstein

  • A Supervised Approach to Extractive Summarisation of Scientific Papers

    Ed Collins;Isabelle Augenstein;Sebastian Riedel

  • Factuality challenges in the era of large language models and opportunities for fact-checking

    Unknown

  • Multi-Task Learning of Pairwise Sequence Classification Tasks over Disparate Label Spaces

    Isabelle Augenstein;Sebastian Ruder;Anders Søgaard

  • Neighborhood Contrastive Learning for Scientific Document Representations with Citation Embeddings

    Unknown

  • emoji2vec: Learning Emoji Representations from their Description

    Ben Eisner;Tim Rocktäschel;Isabelle Augenstein;Matko Bošnjak

  • Multi-Task Learning of Keyphrase Boundary Classification

    Isabelle Augenstein;Anders Søgaard

  • Generalisation in Named Entity Recognition: A Quantitative Analysis

    Isabelle Augenstein;Leon Derczynski;Kalina Bontcheva

Frequent Co-Authors

Anders Søgaard
Anders Søgaard University of Copenhagen
Ryan Cotterell
Ryan Cotterell ETH Zurich
Sebastian Riedel
Sebastian Riedel University College London
Kalina Bontcheva
Kalina Bontcheva University of Sheffield
Fabio Ciravegna
Fabio Ciravegna University of Turin
Preslav Nakov
Preslav Nakov Mohamed bin Zayed University of Artificial Intelligence
Tim Rocktäschel
Tim Rocktäschel University College London
Sebastian Ruder
Sebastian Ruder Google (United States)
Maria Liakata
Maria Liakata Queen Mary University of London
Hanna Wallach
Hanna Wallach Microsoft (United States)

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