Isabelle Augenstein
- Artificial Intelligence top 1%
- Sociology and Political Science top 5%
- Information Systems top 2%
- Computer Vision and Pattern Recognition top 10%
- Statistical and Nonlinear Physics top 10%
- Co-authors
- Anders SøgaardKalina BontchevaChristina LiomaJakob Grue SimonsenPepa AtanasovaElena KochkinaMaria LiakataSebastian Ruder
- Topics
- Topic Modeling (53 papers)Natural Language Processing Techniques (25 papers)Hate Speech and Cyberbullying Detection (12 papers)
- Partner nations
- DenmarkUnited KingdomUnited States
In The Last Decade
Isabelle Augenstein
77 papers receiving 1.6k citations
Peers
Comparison fields: 5 of 105
- Artificial Intelligence 1.3k
- Sociology and Political Science 386
- Information Systems 308
- Computer Vision and Pattern Recognition 138
- Statistical and Nonlinear Physics 87
Countries citing papers authored by Isabelle Augenstein
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
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
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 0 | |
| 3 | 0 | |
| 4 | 0 | |
| 5 | 1 | |
| 6 | 1 | |
| 7 | 4 | |
| 8 | 4 | |
| 9 | 21 | |
| 10 | 10 | |
| 11 | 145 | |
| 12 | 21 | |
| 13 | 71 | |
| 14 | 39 | |
| 15 | Turing at SemEval-2017 task 8 : sequential approach to rumour stance
\nclassification with branch-LSTM
\n | 81 |
| 16 | Learning what to share between loosely related tasks | 25 |
| 17 | Monolingual Social Media Datasets for Detecting Contradiction and Entailment | 7 |
| 18 | 166 | |
| 19 | Statistical knowledge patterns for characterising linked data | 4 |
| 20 | Mining Equivalent Relations from Linked Data | 8 |
About Isabelle Augenstein
Isabelle Augenstein is a scholar working on Artificial Intelligence, Health Informatics and General Social Sciences, having authored 88 papers that have together received 1.6k indexed citations. Recurring topics across this work include Topic Modeling (53 papers), Natural Language Processing Techniques (25 papers) and Hate Speech and Cyberbullying Detection (12 papers). The work is most often cited by research in Health Informatics (59 citations), Artificial Intelligence (1.3k citations) and Information Systems (308 citations). Isabelle Augenstein has collaborated with scholars based in Denmark, United Kingdom and United States. Frequent 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. Their work appears in journals such as PLoS ONE, ACM Computing Surveys and Information Fusion.
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.