Peter Potash

468 citations
17 papers · 193 · h-index 6

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

16 papers receiving 172 citations

Peers

Peter Potash
Comparison fields: 5 of 37
  • Artificial Intelligence 148
  • Computer Vision and Pattern Recognition 48
  • Social Psychology 45
  • Signal Processing 22
  • Computational Mathematics 1
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Citations per field
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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

17 of 17 papers shown
#Work
1 201555
2 201747
3 201738
4 201715
5
Length, Interchangeability, and External Knowledge: Observations from Predicting Argument Convincingness
20176
6 20196
7 20155
8 20164
9 20234
10
Using Topic Modeling and Text Embeddings to Predict Deleted Tweets
20164
11
Recommender System Incorporating User Personality Profile through Analysis of Written Reviews.
20163
12
Here's My Point: Argumentation Mining with Pointer Networks
20172
13 20031
14 19941
15 19921
16 20171
17
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 Social Psychology, having authored 17 papers that have together received 193 indexed citations. Recurring topics across this work include Topic Modeling (10 papers), Natural Language Processing Techniques (4 papers), Advanced Text Analysis Techniques (3 papers), Misinformation and Its Impacts (2 papers), Sentiment Analysis and Opinion Mining (2 papers), Hate Speech and Cyberbullying Detection (2 papers), Software Engineering Research (2 papers) and Social Media and Politics (1 paper). The work is most often cited by research in Artificial Intelligence (148 citations), Computer Vision and Pattern Recognition (48 citations), Social Psychology (45 citations), Signal Processing (22 citations) and Computational Mathematics (1 citation). Peter Potash has collaborated with scholars based in United States, United Kingdom and Cayman Islands. Frequent co-authors include Anna Rumshisky, Alexey Romanov, William Boag, Timothy J. Hazen, Vasili Ramanishka, Eric B. Bell, Yuan Zheng, Tristan Naumann, Xihui Lin and Yuwen Sun. Their work appears in journals such as Historical Methods A Journal of Quantitative and Interdisciplinary History, Journal of the Early Republic, Der Nervenarzt, Conference on Recommender Systems and arXiv (Cornell University).

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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