Sunipa Dev

3.0k citations
22 papers · 361 · h-index 8

Impact in

Papers in

Journals
ACM Transactions on Interactive Intelligent Systems (1 paper)Studies in computational intelligence (1 paper)Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (1 paper)arXiv (Cornell University) (1 paper)Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (2 papers)

In The Last Decade

Sunipa Dev

18 papers receiving 346 citations

Peers

Sunipa Dev
Comparison fields: 5 of 55
  • Health Informatics 25
  • Safety Research 73
  • Artificial Intelligence 241
  • General Social Sciences 22
  • Computer Science Applications 15
Replace Kellie Webster with:
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Citations per field
00.5×1.5×2.3×
Kellie Webster · 1×
Citations per year

Countries citing papers authored by Sunipa Dev

Since Specialization
Citations

This map shows the geographic impact of Sunipa Dev'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 Sunipa Dev with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sunipa Dev more than expected).

Fields of papers citing papers by Sunipa Dev

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Sunipa Dev. 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 Sunipa Dev. The network helps show where Sunipa Dev may publish in the future.

Co-authors

The 25 scholars most cited alongside Sunipa Dev, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Sunipa Dev Line = papers co-authored together Sunipa Dev links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 22 papers — load more, or switch the sort, to bring in the rest.

#Work
1 202388
2 202183
3 202071
4 202231
5 202123
6 202211
7 202410
8 20228
9 20236
10 20236
11 20235
12 20234
13 20234
14 20234
15 20242
16 20242
17 20212
18 20221
19 20160
20 20250

About Sunipa Dev

Sunipa Dev is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems, Health Informatics and General Health Professions, having authored 22 papers that have together received 361 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (8 papers), Topic Modeling (7 papers), Hate Speech and Cyberbullying Detection (4 papers), Text Readability and Simplification (4 papers), Artificial Intelligence in Healthcare and Education (2 papers), Software Engineering Research (2 papers), Disability Rights and Representation (1 paper) and Hermeneutics and Narrative Identity (1 paper). The work is most often cited by research in Health Informatics (25 citations), Safety Research (73 citations), Artificial Intelligence (241 citations), General Social Sciences (22 citations) and Computer Science Applications (15 citations). Sunipa Dev has collaborated with scholars based in United States, India and United Kingdom. Frequent co-authors include Jeff M. Phillips, Vivek Srikumar, Kai-Wei Chang, Tao Li, Anaelia Ovalle, Arjun Subramonian, Emily Denton, Alex Taylor, Robin Brewer and Shaun K. Kane. Their work appears in journals such as ACM Transactions on Interactive Intelligent Systems, Studies in computational intelligence, Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, arXiv (Cornell University) and Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing.

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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