Joe Barrow
Impact in
- Health Informatics top 10%
- Artificial Intelligence in Healthcare and Education
- Artificial Intelligence top 10%
- Topic Modeling
- Natural Language Processing Techniques
- Advanced Text Analysis Techniques
- Explainable Artificial Intelligence (XAI)
- Hate Speech and Cyberbullying Detection
Papers in
-
- Natural Language Processing Techniques 6
- Topic Modeling 6
- Machine Learning and Data Classification 1
- Sentiment Analysis and Opinion Mining 1
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- Handwritten Text Recognition Techniques 1
- Multimodal Machine Learning Applications 1
- Co-authors
- Franck Dernoncourt (4 shared papers)Md Mehrab Tanjim (1 shared paper)Isabel O. Gallegos (2 shared papers)Nesreen K. Ahmed (1 shared paper)Tong Yu (1 shared paper)Ryan A. Rossi (3 shared papers)Sungchul Kim (2 shared papers)Ruiyi Zhang (2 shared papers)
- Journals
- Computational Linguistics (1 paper)
- Partner nations
- United StatesChinaGermany
In The Last Decade
Joe Barrow
7 papers receiving 192 citations
Joe Barrow's Hit Papers
Peers
Comparison fields: 5 of 62
- Health Informatics 21
- Artificial Intelligence 134
- General Social Sciences 10
- Safety Research 17
- Computer Science Applications 8
Countries citing papers authored by Joe Barrow
This map shows the geographic impact of Joe Barrow'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 Joe Barrow with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Joe Barrow more than expected).
Fields of papers citing papers by Joe Barrow
This network shows the impact of papers produced by Joe Barrow. 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 Joe Barrow. The network helps show where Joe Barrow may publish in the future.
Co-authors
The 25 scholars most cited alongside Joe Barrow, 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 | Bias and Fairness in Large Language Models: A Survey Hit paper breakdown → | 2024 | 141 |
| 2 | 2021 | 27 | |
| 3 | 2020 | 21 | |
| 4 | 2020 | 11 | |
| 5 | 2017 | 5 | |
| 6 | 2024 | 3 | |
| 7 | 2024 | 2 | |
| 8 | 2025 | 0 | |
| 9 | 2021 | 0 |
About Joe Barrow
Joe Barrow is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Sociology and Political Science, Information Systems and Statistical and Nonlinear Physics, having authored 9 papers that have together received 210 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (6 papers), Topic Modeling (6 papers), Machine Learning and Data Classification (1 paper), Cybercrime and Law Enforcement Studies (1 paper), Handwritten Text Recognition Techniques (1 paper), Multimodal Machine Learning Applications (1 paper), Sentiment Analysis and Opinion Mining (1 paper) and Misinformation and Its Impacts (1 paper). The work is most often cited by research in Health Informatics (21 citations), Artificial Intelligence (134 citations), General Social Sciences (10 citations), Safety Research (17 citations) and Computer Science Applications (8 citations). Joe Barrow has collaborated with scholars based in United States, China and Germany. Frequent co-authors include Franck Dernoncourt, Md Mehrab Tanjim, Isabel O. Gallegos, Nesreen K. Ahmed, Tong Yu, Ryan A. Rossi, Sungchul Kim, Ruiyi Zhang, Jordan Boyd‐Graber and Pedro Rodríguez. Their work appears in journals such as 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.