Isabelle Augenstein

4.6k total citations
88 papers, 1.6k citations indexed

About

Isabelle Augenstein is a scholar working on Artificial Intelligence, Information Systems and Sociology and Political Science. According to data from OpenAlex, Isabelle Augenstein has authored 88 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 77 papers in Artificial Intelligence, 17 papers in Information Systems and 13 papers in Sociology and Political Science. Recurrent topics in Isabelle Augenstein's work include Topic Modeling (53 papers), Natural Language Processing Techniques (25 papers) and Hate Speech and Cyberbullying Detection (12 papers). Isabelle Augenstein is often cited by papers focused on Topic Modeling (53 papers), Natural Language Processing Techniques (25 papers) and Hate Speech and Cyberbullying Detection (12 papers). Isabelle Augenstein collaborates with scholars based in Denmark, United Kingdom and United States. Isabelle Augenstein's co-authors include Anders Søgaard, Kalina Bontcheva, Christina Lioma, Jakob Grue Simonsen, Pepa Atanasova, Elena Kochkina, Maria Liakata, Sebastian Ruder, Matko Bošnjak and Ben Eisner and has published in prestigious journals such as PLoS ONE, ACM Computing Surveys and Information Fusion.

In The Last Decade

Isabelle Augenstein

77 papers receiving 1.6k citations

Peers

Isabelle Augenstein
María Teresa Martín Valdivia Spain
Luís Alfonso Ureña López Spain
Pawan Goyal India
Alice Oh South Korea
Martin Potthast Germany
Barbara Plank Denmark
Matthew Lease United States
Noah Constant United States
Veselin Stoyanov United States
Kentaro Inui Japan
María Teresa Martín Valdivia Spain
Isabelle Augenstein
Citations per year, relative to Isabelle Augenstein Isabelle Augenstein (= 1×) peers María Teresa Martín Valdivia

Countries citing papers authored by Isabelle Augenstein

Since Specialization
Citations

This map shows the geographic impact of Isabelle Augenstein's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Isabelle Augenstein with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Isabelle Augenstein more than expected).

Fields of papers citing papers by Isabelle Augenstein

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Isabelle Augenstein. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Isabelle Augenstein. The network helps show where Isabelle Augenstein may publish in the future.

Co-authorship network of co-authors of Isabelle Augenstein

This figure shows the co-authorship network connecting the top 25 collaborators of Isabelle Augenstein. A scholar is included among the top collaborators of Isabelle Augenstein based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Isabelle Augenstein. Isabelle Augenstein is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Sun, Jingyi, et al.. (2025). Graph-Guided Textual Explanation Generation Framework. Research at the University of Copenhagen (University of Copenhagen). 29350–29374.
2.
Roitero, Kevin, et al.. (2025). Efficiency and Effectiveness of LLM-Based Summarization of Evidence in Crowdsourced Fact-Checking. VBN Forskningsportal (Aalborg Universitet). 457–467.
3.
Atanasova, Pepa, et al.. (2024). Revealing the Parametric Knowledge of Language Models: A Unified Framework for Attribution Methods. Research at the University of Copenhagen (University of Copenhagen). 8173–8186.
4.
Atanasova, Pepa, et al.. (2024). DYNAMICQA: Tracing Internal Knowledge Conflicts in Language Models. Research at the University of Copenhagen (University of Copenhagen). 14346–14360.
5.
Augenstein, Isabelle, et al.. (2023). Measuring Gender Bias in West Slavic Language Models. Research at the University of Copenhagen (University of Copenhagen). 146–154. 1 indexed citations
6.
Williams, Adina, et al.. (2023). A Latent-Variable Model for Intrinsic Probing. Proceedings of the AAAI Conference on Artificial Intelligence. 37(11). 13591–13599. 1 indexed citations
7.
Augenstein, Isabelle, et al.. (2023). Topic-Guided Sampling For Data-Efficient Multi-Domain Stance Detection. 13448–13464. 4 indexed citations
8.
Assenmacher, Dennis, et al.. (2023). People Make Better Edits: Measuring the Efficacy of LLM-Generated Counterfactually Augmented Data for Harmful Language Detection. IRIS Research product catalog (Sapienza University of Rome). 10480–10504. 4 indexed citations
9.
Augenstein, Isabelle, et al.. (2022). Neighborhood Contrastive Learning for Scientific Document Representations with Citation Embeddings. GoeScholar The Publication Server of the Georg-August-Universität Göttingen (Georg-August-Universität Göttingen). 11670–11688. 21 indexed citations
10.
Ponti, Edoardo Maria, et al.. (2022). Same Neurons, Different Languages: Probing Morphosyntax in Multilingual Pre-trained Models. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 1589–1598. 10 indexed citations
11.
Holzinger, Andreas, Matthias Dehmer, Frank Emmert‐Streib, et al.. (2021). Information fusion as an integrative cross-cutting enabler to achieve robust, explainable, and trustworthy medical artificial intelligence. Information Fusion. 79. 263–278. 145 indexed citations
12.
Atanasova, Pepa, et al.. (2020). Generating Label Cohesive and Well-Formed Adversarial Claims. VBN Forskningsportal (Aalborg Universitet). 3168–3177. 21 indexed citations
13.
Atanasova, Pepa, Jakob Grue Simonsen, Christina Lioma, & Isabelle Augenstein. (2020). Generating Fact Checking Explanations. Research at the University of Copenhagen (University of Copenhagen). 7352–7364. 71 indexed citations
14.
Augenstein, Isabelle, Sebastian Ruder, & Anders Søgaard. (2018). . arXiv (Cornell University). 39 indexed citations
15.
Kochkina, Elena, Maria Liakata, & Isabelle Augenstein. (2017). Turing at SemEval-2017 task 8 : sequential approach to rumour stance \nclassification with branch-LSTM \n. Warwick Research Archive Portal (University of Warwick). 81 indexed citations
16.
Ruder, Sebastian, Joachim Bingel, Isabelle Augenstein, & Anders Søgaard. (2017). Learning what to share between loosely related tasks. Research at the University of Copenhagen (University of Copenhagen). 25 indexed citations
17.
Augenstein, Isabelle, et al.. (2016). Monolingual Social Media Datasets for Detecting Contradiction and Entailment. Language Resources and Evaluation. 4602–4605. 7 indexed citations
18.
Eisner, Ben, Tim Rocktäschel, Isabelle Augenstein, Matko Bošnjak, & Sebastian Riedel. (2016). emoji2vec: Learning Emoji Representations from their Description. 48–54. 166 indexed citations
19.
Blomqvist, Eva, Ziqi Zhang, Anna Lisa Gentile, Isabelle Augenstein, & Fabio Ciravegna. (2013). Statistical knowledge patterns for characterising linked data. UCL Discovery (University College London). 1188. 1–13. 4 indexed citations
20.
Zhang, Ziqi, Anna Lisa Gentile, Isabelle Augenstein, Eva Blomqvist, & Fabio Ciravegna. (2013). Mining Equivalent Relations from Linked Data. UCL Discovery (University College London). 289–293. 8 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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