Libby Hemphill

65 papers receiving 890 citations

Peers

Libby Hemphill
Comparison fields: 5 of 111
  • Communication 365
  • Health Informatics 56
  • Human-Computer Interaction 69
  • General Social Sciences 36
  • Artificial Intelligence 308
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Alexandra Olteanu United States
Björn Roß Germany
Meredith Broussard United States
Andrea L. Guzman United States
Danielle Keats Citron United States
Engin Bozdag Netherlands
Amy X. Zhang United States
P. M. Krafft United States
Reuben Binns United Kingdom
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Citations per field
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Citations per year

Countries citing papers authored by Libby Hemphill

Since Specialization
Citations

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

Fields of papers citing papers by Libby Hemphill

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 69 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201383
2 202468
3 202367
4 201661
5 200754
6 202443
7 201341
8 201832
9 202032
10 201431
11 201630
12 201227
13 200525
14 202322
15 201620
16 201319
17 202218
18 202018
19 202416
20 201216

About Libby Hemphill

Libby Hemphill is a scholar working on Communication, Sociology and Political Science, Artificial Intelligence, Information Systems and Political Science and International Relations, having authored 69 papers that have together received 945 indexed citations. Recurring topics across this work include Social Media and Politics (28 papers), Hate Speech and Cyberbullying Detection (9 papers), Data Quality and Management (9 papers), Research Data Management Practices (9 papers), Electoral Systems and Political Participation (8 papers), Scientific Computing and Data Management (7 papers), Media Studies and Communication (7 papers) and Media Influence and Politics (5 papers). The work is most often cited by research in Communication (365 citations), Health Informatics (56 citations), Human-Computer Interaction (69 citations), General Social Sciences (36 citations) and Artificial Intelligence (308 citations). Libby Hemphill has collaborated with scholars based in United States, Ireland and Netherlands. Frequent co-authors include Jahna Otterbacher, Lingyao Li, Matthew A. Shapiro, Aron Culotta, Huizi Yu, Zihui Ma, Joshua Guberman, Lizhou Fan, Sanggyu Lee and Shubham Atreja. Their work appears in journals such as Proceedings of the ACM on Human-Computer Interaction, Journal of the Association for Information Science and Technology, Policy & Internet, Journal of Information Technology & Politics and Scientific Data.

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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