Christopher Rytting
Impact in
- General Social Sciences top 0.5%
- Computational and Text Analysis Methods
- Health Informatics top 10%
Papers in
-
- Social Media and Politics 2
-
- Machine Learning and Algorithms 1
- Adversarial Robustness in Machine Learning 1
- Anomaly Detection Techniques and Applications 1
- Hate Speech and Cyberbullying Detection 1
- Co-authors
- Lisa P. Argyle (2 shared papers)David Wingate (2 shared papers)Joshua R. Gubler (2 shared papers)Nancy Fulda (2 shared papers)Ethan C. Busby (2 shared papers)Christopher A. Bail (1 shared paper)D. L. Wingate (1 shared paper)Joshua A. Robinson (1 shared paper)
- Journals
- Proceedings of the National Academy of Sciences (1 paper)Political Analysis (1 paper)Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (1 paper)
- Partner nations
- United States
In The Last Decade
Christopher Rytting
4 papers receiving 373 citations
Christopher Rytting's Hit Papers
Peers
Comparison fields: 5 of 64
- General Social Sciences 65
- Health Informatics 19
- Artificial Intelligence 187
- Safety Research 45
- General Decision Sciences 9
Countries citing papers authored by Christopher Rytting
This map shows the geographic impact of Christopher Rytting'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 Christopher Rytting with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Christopher Rytting more than expected).
Fields of papers citing papers by Christopher Rytting
This network shows the impact of papers produced by Christopher Rytting. 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 Christopher Rytting. The network helps show where Christopher Rytting may publish in the future.
Co-authors
The 15 scholars most cited alongside Christopher Rytting, 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 | Out of One, Many: Using Language Models to Simulate Human Samples Hit paper breakdown → | 2023 | 250 |
| 2 | 2022 | 67 | |
| 3 | 2023 | 60 | |
| 4 | 2024 | 5 |
About Christopher Rytting
Christopher Rytting is a scholar working on Communication, Artificial Intelligence, Political Science and International Relations, Sociology and Political Science and General Social Sciences, having authored 4 papers that have together received 382 indexed citations. Recurring topics across this work include Social Media and Politics (2 papers), Machine Learning and Algorithms (1 paper), Electoral Systems and Political Participation (1 paper), Misinformation and Its Impacts (1 paper), Computational and Text Analysis Methods (1 paper), Adversarial Robustness in Machine Learning (1 paper), Anomaly Detection Techniques and Applications (1 paper) and Hate Speech and Cyberbullying Detection (1 paper). The work is most often cited by research in General Social Sciences (65 citations), Health Informatics (19 citations), Artificial Intelligence (187 citations), Safety Research (45 citations) and General Decision Sciences (9 citations). Christopher Rytting has collaborated with scholars based in United States. Frequent co-authors include Lisa P. Argyle, David Wingate, Joshua R. Gubler, Nancy Fulda, Ethan C. Busby, Christopher A. Bail, D. L. Wingate, Joshua A. Robinson, Alex Shaw and Mahmoud I. Khalil. Their work appears in journals such as Proceedings of the National Academy of Sciences, Political Analysis and Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).
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.