Caleb Ziems
Impact in
- General Social Sciences top 1%
- Computational and Text Analysis Methods
- Health Informatics top 10%
Papers in
-
- Topic Modeling 9
- Natural Language Processing Techniques 7
- Hate Speech and Cyberbullying Detection 2
- Speech Recognition and Synthesis 2
- Speech and dialogue systems 2
- Sentiment Analysis and Opinion Mining 1
-
- Swearing, Euphemism, Multilingualism 1
- Co-authors
- Diyi Yang (14 shared papers)William A. Held (4 shared papers)Omar Ahmed Shaikh (1 shared paper)Mai ElSherief (1 shared paper)David Muchlinski (2 shared papers)Munmun De Choudhury (1 shared paper)Naren Ramakrishnan (1 shared paper)Bing He (1 shared paper)
- Journals
- Computational Linguistics (1 paper)Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (3 papers)Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (1 paper)
- Partner nations
- United StatesSingaporeFrance
In The Last Decade
Caleb Ziems
13 papers receiving 510 citations
Caleb Ziems's Hit Papers
Peers
Comparison fields: 5 of 75
- General Social Sciences 51
- Health Informatics 17
- Artificial Intelligence 337
- Communication 56
- Safety Research 24
Countries citing papers authored by Caleb Ziems
This map shows the geographic impact of Caleb Ziems'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 Caleb Ziems with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Caleb Ziems more than expected).
Fields of papers citing papers by Caleb Ziems
This network shows the impact of papers produced by Caleb Ziems. 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 Caleb Ziems. The network helps show where Caleb Ziems may publish in the future.
Co-authors
The 25 scholars most cited alongside Caleb Ziems, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Can Large Language Models Transform Computational Social Science? Hit paper breakdown → | 2023 | 233 |
| 2 | 2021 | 94 | |
| 3 | 2021 | 69 | |
| 4 | 2022 | 38 | |
| 5 | 2022 | 15 | |
| 6 | 2022 | 15 | |
| 7 | 2023 | 13 | |
| 8 | 2023 | 11 | |
| 9 | 2024 | 8 | |
| 10 | 2023 | 7 | |
| 11 | 2023 | 5 | |
| 12 | 2023 | 4 | |
| 13 | 2023 | 4 | |
| 14 | 2024 | 0 |
About Caleb Ziems
Caleb Ziems is a scholar working on Artificial Intelligence, Communication, Political Science and International Relations, General Social Sciences and Computer Vision and Pattern Recognition, having authored 14 papers that have together received 516 indexed citations. Recurring topics across this work include Topic Modeling (9 papers), Natural Language Processing Techniques (7 papers), Computational and Text Analysis Methods (2 papers), Hate Speech and Cyberbullying Detection (2 papers), Speech Recognition and Synthesis (2 papers), Speech and dialogue systems (2 papers), Swearing, Euphemism, Multilingualism (1 paper) and Sentiment Analysis and Opinion Mining (1 paper). The work is most often cited by research in General Social Sciences (51 citations), Health Informatics (17 citations), Artificial Intelligence (337 citations), Communication (56 citations) and Safety Research (24 citations). Caleb Ziems has collaborated with scholars based in United States, Singapore and France. Frequent co-authors include Diyi Yang, William A. Held, Omar Ahmed Shaikh, Mai ElSherief, David Muchlinski, Munmun De Choudhury, Naren Ramakrishnan, Bing He, Sandeep Soni and Srijan Kumar. Their work appears in journals such as Computational Linguistics, Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) 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.