Lucy Vasserman
Impact in
- Artificial Intelligence top 2%
- Hate Speech and Cyberbullying Detection
- Topic Modeling
- Natural Language Processing Techniques
- Adversarial Robustness in Machine Learning
- Explainable Artificial Intelligence (XAI)
- Safety Research top 5%
- Ethics and Social Impacts of AI
Papers in
-
- Hate Speech and Cyberbullying Detection 5
- Natural Language Processing Techniques 3
- Topic Modeling 3
- Speech Recognition and Synthesis 2
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- Social Media and Politics 1
- Co-authors
- Jeffrey Sorensen (4 shared papers)Nithum Thain (2 shared papers)Lucas Dixon (2 shared papers)John Li (1 shared paper)Nitesh Goyal (3 shared papers)Yi Tay (1 shared paper)Jai Prakash Gupta (1 shared paper)Alyssa Lees (2 shared papers)
- Journals
- Proceedings of the ACM on Human-Computer Interaction (1 paper)CHI Conference on Human Factors in Computing Systems (1 paper)Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (1 paper)
- Partner nations
- United States
In The Last Decade
Lucy Vasserman
7 papers receiving 599 citations
Lucy Vasserman's Hit Papers
Peers
Comparison fields: 5 of 68
- Artificial Intelligence 529
- Safety Research 83
- Health Informatics 13
- Communication 66
- General Social Sciences 15
Countries citing papers authored by Lucy Vasserman
This map shows the geographic impact of Lucy Vasserman'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 Lucy Vasserman with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Lucy Vasserman more than expected).
Fields of papers citing papers by Lucy Vasserman
This network shows the impact of papers produced by Lucy Vasserman. 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 Lucy Vasserman. The network helps show where Lucy Vasserman may publish in the future.
Co-authors
The 14 scholars most cited alongside Lucy Vasserman, 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 | Measuring and Mitigating Unintended Bias in Text Classification Hit paper breakdown → | 2018 | 338 |
| 2 | 2019 | 149 | |
| 3 | 2022 | 67 | |
| 4 | 2022 | 37 | |
| 5 | 2022 | 21 | |
| 6 | 2016 | 12 | |
| 7 | 2015 | 11 | |
| 8 | 2022 | 0 |
About Lucy Vasserman
Lucy Vasserman is a scholar working on Artificial Intelligence, Communication, Sociology and Political Science, Signal Processing and Safety Research, having authored 8 papers that have together received 635 indexed citations. Recurring topics across this work include Hate Speech and Cyberbullying Detection (5 papers), Natural Language Processing Techniques (3 papers), Topic Modeling (3 papers), Speech Recognition and Synthesis (2 papers), Gender, Feminism, and Media (1 paper), Social Media and Politics (1 paper), Advanced Malware Detection Techniques (1 paper) and Misinformation and Its Impacts (1 paper). The work is most often cited by research in Artificial Intelligence (529 citations), Safety Research (83 citations), Health Informatics (13 citations), Communication (66 citations) and General Social Sciences (15 citations). Lucy Vasserman has collaborated with scholars based in United States. Frequent co-authors include Jeffrey Sorensen, Nithum Thain, Lucas Dixon, John Li, Nitesh Goyal, Yi Tay, Jai Prakash Gupta, Alyssa Lees, Vinh Q. Tran and Donald Metzler. Their work appears in journals such as Proceedings of the ACM on Human-Computer Interaction, CHI Conference on Human Factors in Computing Systems and Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining.
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.