Dawn Nafus

33 papers receiving 1.4k citations

Peers

Dawn Nafus
Comparison fields: 5 of 119
  • Human-Computer Interaction 387
  • Communication 249
  • Computer Science Applications 138
  • Safety Research 180
  • Applied Psychology 87
Replace Irina Shklovski with:
Irina Shklovski Denmark
Melissa Mazmanian United States
Yubo Kou United States
Minna Ruckenstein Finland
Catherine D’Ignazio United States
Geoff Kaufman United States
Sheena Erete United States
Leopoldina Fortunati Italy
Michael Zimmer United States
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Citations per field
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Irina Shklovski · 1×
Citations per year

Countries citing papers authored by Dawn Nafus

Since Specialization
Citations

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

Fields of papers citing papers by Dawn Nafus

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 35 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2002400
2 2016239
3
This One Does Not Go Up to 11: The Quantified Self Movement as an Alternative Big Data Practice
2014126
4 2011117
5
Self-Tracking
201674
6 202359
7 201659
8 200954
9 201451
10
Big Data, Big Questions| This One Does Not Go Up To 11: The Quantified Self Movement as an Alternative Big Data Practice
201448
11 201846
12 200843
13 200635
14 202229
15 200922
16
Consumption, Technology, and Development: The “Poor” as “Consumer”
201220
17 200918
18 201612
19 202111
20 20089

About Dawn Nafus

Dawn Nafus is a scholar working on Sociology and Political Science, Safety Research, Computer Science Applications, Human-Computer Interaction and Anthropology, having authored 35 papers that have together received 1.5k indexed citations. Recurring topics across this work include Ethics and Social Impacts of AI (7 papers), Innovative Human-Technology Interaction (5 papers), Digital Economy and Work Transformation (4 papers), Information Systems Theories and Implementation (4 papers), Open Source Software Innovations (4 papers), Green IT and Sustainability (3 papers), Mobile Crowdsensing and Crowdsourcing (3 papers) and Race, Genetics, and Society (3 papers). The work is most often cited by research in Human-Computer Interaction (387 citations), Communication (249 citations), Computer Science Applications (138 citations), Safety Research (180 citations) and Applied Psychology (87 citations). Dawn Nafus has collaborated with scholars based in United States, United Kingdom and New Zealand. Frequent co-authors include Gina Neff, Jamie Sherman, Ken Anderson, David Gray Widder, Tye Rattenbury, Leopoldina Fortunati, Kenneth J. Gergen, Eija-Liisa Kasesniemi, G Ström and Amit M. Schejter. Their work appears in journals such as Big Data & Society, Journal of the Royal Anthropological Institute, New Media & Society, International journal of communication and Journal of Visual Culture.

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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