Peter Potash

475 citations
18 papers · 232 · h-index 8

Impact in

Papers in

    • Topic Modeling 10
    • Natural Language Processing Techniques 4
    • Advanced Text Analysis Techniques 3
    • Sentiment Analysis and Opinion Mining 2
    • Hate Speech and Cyberbullying Detection 2
    • Software Engineering Research 2

Peter Potash

17 papers receiving 208 citations

Peers

Peter Potash
Comparison fields: 5 of 41
  • Artificial Intelligence 179
  • Computer Vision and Pattern Recognition 57
  • Social Psychology 49
  • Signal Processing 25
  • Information Systems 46
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Citations per year

Countries citing papers authored by Peter Potash

Since Specialization
Citations

This map shows the geographic impact of Peter Potash'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 Peter Potash with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Peter Potash more than expected).

Fields of papers citing papers by Peter Potash

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Peter Potash. 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 Peter Potash. The network helps show where Peter Potash may publish in the future.

Co-authors

The 12 scholars most cited alongside Peter Potash, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Peter Potash Line = papers co-authored together Peter Potash links everyone, so they are left out of the graph.

All Works

18 of 18 papers shown
#Work
1 201566
2 201752
3 201744
4 201717
5 201710
6
Length, Interchangeability, and External Knowledge: Observations from Predicting Argument Convincingness
20177
7 20197
8 20157
9 20234
10 20164
11
Recommender System Incorporating User Personality Profile through Analysis of Written Reviews.
20164
12
Using Topic Modeling and Text Embeddings to Predict Deleted Tweets
20164
13
Here's My Point: Argumentation Mining with Pointer Networks
20172
14 20031
15 19921
16 19941
17 20171
18
Litigious Vermonters : court records to 1825
19790

About Peter Potash

Peter Potash is a scholar working on Artificial Intelligence, Information Systems, Sociology and Political Science, Communication and Statistical and Nonlinear Physics, having authored 18 papers that have together received 232 indexed citations. Recurring topics across this work include Topic Modeling (10 papers), Natural Language Processing Techniques (4 papers), Advanced Text Analysis Techniques (3 papers), Sentiment Analysis and Opinion Mining (2 papers), Misinformation and Its Impacts (2 papers), Hate Speech and Cyberbullying Detection (2 papers), Software Engineering Research (2 papers) and Social Media and Politics (2 papers). The work is most often cited by research in Artificial Intelligence (179 citations), Computer Vision and Pattern Recognition (57 citations), Social Psychology (49 citations), Signal Processing (25 citations) and Information Systems (46 citations). Peter Potash has collaborated with scholars based in United States, United Kingdom and China. Frequent co-authors include Anna Rumshisky, Alexey Romanov, William Boag, Timothy J. Hazen, Vasili Ramanishka, Eric B. Bell, Keming Lu, Junwei Lu, Xihui Lin and Yuwen Sun. Their work appears in journals such as Journal of the Early Republic, Historical Methods A Journal of Quantitative and Interdisciplinary History, Der Nervenarzt, Lecture notes in computer science and OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information).

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.

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