Michael Yeomans

22 papers receiving 805 citations

Michael Yeomans's Hit Papers

Making sense of recommendations 2019 · 259 citations
2590+2+4Years since publication50100150200250

Peers

Michael Yeomans
Comparison fields: 5 of 81
  • Computer Science Applications 135
  • General Decision Sciences 39
  • Safety Research 157
  • Health Informatics 25
  • Communication 61
Replace Kristen Vaccaro with:
Kristen Vaccaro United States
Aimee Rickman United States
Jennifer M. Logg United States
Ronald E. Robertson United States
Lingyun Qiu China
Daniel M. Bartels United States
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Leyla Dogruel Germany
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Citations per year

Countries citing papers authored by Michael Yeomans

Since Specialization
Citations

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

Fields of papers citing papers by Michael Yeomans

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Michael Yeomans, 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 Michael Yeomans Line = papers co-authored together Michael Yeomans links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 26 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Making sense of recommendations
Hit paper breakdown →
2019259
2 2017124
3 2020100
4 202076
5 201661
6 201938
7 202137
8 201737
9 201935
10 202024
11 201711
12 202310
13 20189
14 19967
15 20176
16 20203
17 20241
18 20171
19
Topic Preference Detection: A Novel Approach to Understand Perspective Taking in Conversation
20201
20
It Doesn't Hurt to Ask
20171

About Michael Yeomans

Michael Yeomans is a scholar working on Sociology and Political Science, Experimental and Cognitive Psychology, Artificial Intelligence, Literature and Literary Theory and Safety Research, having authored 26 papers that have together received 845 indexed citations. Recurring topics across this work include Online Learning and Analytics (4 papers), Social Media and Politics (3 papers), Experimental Behavioral Economics Studies (3 papers), Language, Metaphor, and Cognition (3 papers), Language, Discourse, Communication Strategies (3 papers), Computational and Text Analysis Methods (3 papers), Social and Intergroup Psychology (3 papers) and Discourse Analysis in Language Studies (3 papers). The work is most often cited by research in Computer Science Applications (135 citations), General Decision Sciences (39 citations), Safety Research (157 citations), Health Informatics (25 citations) and Communication (61 citations). Michael Yeomans has collaborated with scholars based in United States, United Kingdom and Canada. Frequent co-authors include Jon Kleinberg, Sendhil Mullainathan, Anuj Shah, Francesca Gino, Julia A. Minson, Justin Reich, Alison Wood Brooks, Karen Huang, Dustin Tingley and Hanne K. Collins. Their work appears in journals such as Management Science, The R Journal, Organizational Behavior and Human Decision Processes, International Journal of Artificial Intelligence in Education and Journal of Personality and Social Psychology.

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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