Joseph Seering

1.7k citations
37 papers · 1.3k · 1 hit paper · h-index 17

Impact in

Papers in

Joseph Seering

32 papers receiving 1.2k citations

Joseph Seering's Hit Papers

Moderator engagement and community development in the age of algorithms 2019 · 244 citations
2440+2+4Years since publication50100150200

Peers

Joseph Seering
Comparison fields: 5 of 79
  • Communication 421
  • Human-Computer Interaction 156
  • Artificial Intelligence 550
  • Computer Science Applications 81
  • Sociology and Political Science 607
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Shagun Jhaver United States
Andrew McStay United Kingdom
Kelley Cotter United States
Kristen Vaccaro United States
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Brahim Zarouali Belgium
Alyson L. Young United States
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Citations per field
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Citations per year

Countries citing papers authored by Joseph Seering

Since Specialization
Citations

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

Fields of papers citing papers by Joseph Seering

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 37 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Moderator engagement and community development in the age of algorithms
Hit paper breakdown →
2019244
2 2017194
3 2020106
4 201987
5 201966
6 201859
7 202158
8 201757
9 202052
10 201951
11 201850
12 202041
13 201837
14 201928
15 202323
16 201921
17 202018
18 201914
19 202211
20 201910

About Joseph Seering

Joseph Seering is a scholar working on Sociology and Political Science, Artificial Intelligence, Communication, Information Systems and Human-Computer Interaction, having authored 37 papers that have together received 1.3k indexed citations. Recurring topics across this work include Hate Speech and Cyberbullying Detection (14 papers), Social Media and Politics (11 papers), Digital Games and Media (11 papers), Innovative Human-Technology Interaction (5 papers), Wikis in Education and Collaboration (4 papers), Impact of Technology on Adolescents (4 papers), ICT in Developing Communities (3 papers) and AI in Service Interactions (3 papers). The work is most often cited by research in Communication (421 citations), Human-Computer Interaction (156 citations), Artificial Intelligence (550 citations), Computer Science Applications (81 citations) and Sociology and Political Science (607 citations). Joseph Seering has collaborated with scholars based in United States, South Korea and United Kingdom. Frequent co-authors include Geoff Kaufman, Robert E. Kraut, Jessica Hammer, Laura Dabbish, Jina Yoon, Tony Wang, Zheng Yao, Michal Luria, Saiph Savage and Diyi Yang. Their work appears in journals such as Proceedings of the ACM on Human-Computer Interaction, New Media & Society, Computers in Human Behavior Reports, Proceedings on Privacy Enhancing Technologies and Pure (University of Bath).

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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