Jonathan Schler

3.4k citations
36 papers · 2.0k · h-index 15

Impact in

    • Authorship Attribution and Profiling
    • Topic Modeling
    • Natural Language Processing Techniques
    • Hate Speech and Cyberbullying Detection
    • Sentiment Analysis and Opinion Mining
    • Advanced Text Analysis Techniques
    • Spam and Phishing Detection

Papers in

    • Authorship Attribution and Profiling 16
    • Topic Modeling 13
    • Natural Language Processing Techniques 12
    • Advanced Text Analysis Techniques 7
    • Hate Speech and Cyberbullying Detection 6
    • Algorithms and Data Compression 3
    • Spam and Phishing Detection 5
    • Web Data Mining and Analysis 3

Jonathan Schler

33 papers receiving 1.8k citations

Peers

Jonathan Schler
Comparison fields: 5 of 75
  • Artificial Intelligence 1.8k
  • Information Systems 535
  • Human-Computer Interaction 106
  • Communication 86
  • Sociology and Political Science 319
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Citations per field
00.5×1.5×2.2×
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Citations per year

Countries citing papers authored by Jonathan Schler

Since Specialization
Citations

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

Fields of papers citing papers by Jonathan Schler

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 16 scholars most cited alongside Jonathan Schler, 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 Jonathan Schler Line = papers co-authored together Jonathan Schler links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 36 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Effects of Age and Gender on Blogging
2005345
2 2008313
3 2009243
4 2010162
5 2004154
6 2007129
7 2006129
8
Measuring Differentiability: Unmasking Pseudonymous Authors
2007108
9 2005103
10 200397
11 200652
12 201232
13 201320
14 200618
15 201916
16 201314
17 200111
18
Authorship Attribution: What's Easy and What's Hard?
201310
19
Using neutral examples for learning polarity
200510
20 200410

About Jonathan Schler

Jonathan Schler is a scholar working on Artificial Intelligence, Information Systems, Sociology and Political Science, Human-Computer Interaction and Communication, having authored 36 papers that have together received 2.0k indexed citations. Recurring topics across this work include Authorship Attribution and Profiling (16 papers), Topic Modeling (13 papers), Natural Language Processing Techniques (12 papers), Advanced Text Analysis Techniques (7 papers), Hate Speech and Cyberbullying Detection (6 papers), Spam and Phishing Detection (5 papers), Algorithms and Data Compression (3 papers) and Web Data Mining and Analysis (3 papers). The work is most often cited by research in Artificial Intelligence (1.8k citations), Information Systems (535 citations), Human-Computer Interaction (106 citations), Communication (86 citations) and Sociology and Political Science (319 citations). Jonathan Schler has collaborated with scholars based in Israel and United States. Frequent co-authors include Moshe Koppel, Shlomo Argamon, James W. Pennebaker, Ronen Feldman, Yonatan Aumann, Benjamin Rosenfeld, Ido Dagan, David Sarne, Moshe Fresko and Kate Loveys. Their work appears in journals such as Journal on Computing and Cultural Heritage, Language Resources and Evaluation, Communications of the ACM, PLoS ONE and Journal of Machine Learning Research.

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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