Joe Barrow
Impact in
- Health Informatics top 5%
- Artificial Intelligence in Healthcare and Education
- Artificial Intelligence top 10%
- Topic Modeling
- Natural Language Processing Techniques
- Hate Speech and Cyberbullying Detection
- Advanced Text Analysis Techniques
- Explainable Artificial Intelligence (XAI)
Papers in
-
- Topic Modeling 7
- Natural Language Processing Techniques 7
- Machine Learning and Data Classification 1
- Algorithms and Data Compression 1
- Hate Speech and Cyberbullying Detection 1
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- Handwritten Text Recognition Techniques 1
- Co-authors
- Ryan A. Rossi (3 shared papers)Franck Dernoncourt (4 shared papers)Ruiyi Zhang (2 shared papers)Tong Yu (2 shared papers)Isabel Gallegos (2 shared papers)Md Mehrab Tanjim (1 shared paper)Sungchul Kim (2 shared papers)Nesreen K. Ahmed (1 shared paper)
- Partner nations
- United StatesGermanyChina
In The Last Decade
Joe Barrow
11 papers receiving 365 citations
Joe Barrow's Hit Papers
Peers
Comparison fields: 5 of 68
- Health Informatics 30
- Artificial Intelligence 187
- General Social Sciences 15
- Safety Research 26
- Computer Science Applications 10
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 | 287 |
| 2 | 2021 | 33 | |
| 3 | 2020 | 32 | |
| 4 | 2020 | 15 | |
| 5 | 2017 | 6 | |
| 6 | 2024 | 6 | |
| 7 | 2025 | 4 | |
| 8 | 2024 | 4 | |
| 9 | 2025 | 3 | |
| 10 | 2019 | 2 | |
| 11 | 2025 | 2 | |
| 12 | 2021 | 0 | |
| 13 | 2025 | 0 |
About Joe Barrow
Joe Barrow is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Computer Science Applications, Communication and Statistical and Nonlinear Physics, having authored 13 papers that have together received 394 indexed citations. Recurring topics across this work include Topic Modeling (7 papers), Natural Language Processing Techniques (7 papers), Machine Learning and Data Classification (1 paper), Algorithms and Data Compression (1 paper), Handwritten Text Recognition Techniques (1 paper), Wikis in Education and Collaboration (1 paper), Hate Speech and Cyberbullying Detection (1 paper) and Open Education and E-Learning (1 paper). The work is most often cited by research in Health Informatics (30 citations), Artificial Intelligence (187 citations), General Social Sciences (15 citations), Safety Research (26 citations) and Computer Science Applications (10 citations). Joe Barrow has collaborated with scholars based in United States, Germany and China. Frequent co-authors include Ryan A. Rossi, Franck Dernoncourt, Ruiyi Zhang, Tong Yu, Isabel Gallegos, Md Mehrab Tanjim, Sungchul Kim, Nesreen K. Ahmed, Jordan Lee Boyd-Graber and Douglas W. Oard. Their work appears in journals such as Computational Linguistics and Lecture notes in computer science.
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.