Esin Durmus
- Artificial Intelligence top 5%
- Computer Vision and Pattern Recognition
- Sociology and Political Science
- Safety Research top 10%
- Information Systems
- Co-authors
- Faisal LadhakTatsunori HashimotoKathleen McKeownDan JurafskyClaire CardieMyra ChengTianyi ZhangPercy Liang
- Topics
- Topic Modeling (9 papers)Natural Language Processing Techniques (6 papers)Social Media and Politics (4 papers)
- Journals
- Proceedings of the National Academy of SciencesACM Computing SurveysTransactions of the Association for Computational Linguistics
- Partner nations
- United StatesHong KongItaly
In The Last Decade
Esin Durmus
16 papers receiving 414 citations
Hit Papers
Peers
Comparison fields: 5 of 77
- Artificial Intelligence 280
- Computer Vision and Pattern Recognition 51
- Sociology and Political Science 45
- Safety Research 40
- Information Systems 32
Countries citing papers authored by Esin Durmus
This map shows the geographic impact of Esin Durmus'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 Esin Durmus with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Esin Durmus more than expected).
Fields of papers citing papers by Esin Durmus
This network shows the impact of papers produced by Esin Durmus. 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 Esin Durmus. The network helps show where Esin Durmus may publish in the future.
Co-authorship network of co-authors of Esin Durmus
This figure shows the co-authorship network connecting the top 25 collaborators of Esin Durmus. A scholar is included among the top collaborators of Esin Durmus 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 Esin Durmus. Esin Durmus 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 | 5 | |
| 3 | 2 | |
| 4 | Benchmarking Large Language Models for News Summarizationbreakdown → | 124 |
| 5 | 39 | |
| 6 | Easily Accessible Text-to-Image Generation Amplifies Demographic Stereotypes at Large Scalebreakdown → | 118 |
| 7 | 15 | |
| 8 | 0 | |
| 9 | 3 | |
| 10 | 38 | |
| 11 | 11 | |
| 12 | 1 | |
| 13 | 21 | |
| 14 | Proceedings of the Third Workshop on Computational Modeling of People's Opinions, Personality, and Emotion's in Social Media | 25 |
| 15 | 12 | |
| 16 | 12 | |
| 17 | 3 | |
| 18 | Cornell Belief and Sentiment System at TAC 2016. | 3 |
About Esin Durmus
Esin Durmus is a scholar working on Communication, Artificial Intelligence and General Social Sciences, having authored 18 papers that have together received 432 indexed citations. Recurring topics across this work include Topic Modeling (9 papers), Natural Language Processing Techniques (6 papers) and Social Media and Politics (4 papers). The work is most often cited by research in Health Informatics (27 citations), Artificial Intelligence (280 citations) and General Social Sciences (22 citations). Esin Durmus has collaborated with scholars based in United States, Hong Kong and Italy. Frequent co-authors include Faisal Ladhak, Tatsunori Hashimoto, Kathleen McKeown, Dan Jurafsky, Claire Cardie, Myra Cheng, Tianyi Zhang, Percy Liang, Aylin Caliskan and Debora Nozza. Their work appears in journals such as Proceedings of the National Academy of Sciences, ACM Computing Surveys and Transactions of the Association for Computational Linguistics.
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.