Jakub Šimko

452 citations
31 papers · 221 · h-index 9

Impact in

Papers in

Jakub Šimko

30 papers receiving 209 citations

Peers

Jakub Šimko
Comparison fields: 5 of 46
  • Computer Science Applications 40
  • Human-Computer Interaction 25
  • Health Informatics 5
  • Artificial Intelligence 89
  • Communication 18
Replace Róbert Móro with:
Róbert Móro Slovakia
Jean Garcia-Gathright United States
Joseph Corneli United Kingdom
Jonathan Stray United States
Rachael Tatman United States
Kristen Greene United States
Erik Harpstead United States
Mina Lee United States
Gonzalo Gabriel Méndez Ecuador
Mauri Kaipainen Finland
Jakub Šimko relative to Róbert Móro Slovakia Róbert Móro's profile →
Citations per field
00.5×3.1×
Róbert Móro · 1×
Citations per year

Countries citing papers authored by Jakub Šimko

Since Specialization
Citations

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

Fields of papers citing papers by Jakub Šimko

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 11 scholars most cited alongside Jakub Šimko, 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 Jakub Šimko Line = papers co-authored together Jakub Šimko links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 31 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201631
2 202225
3 202314
4 202314
5 201813
6 201912
7
Unravelling the basic concepts and intents of misbehavior in post-truth society
20199
8 20119
9 20218
10 20128
11 20227
12 20177
13 20196
14 20136
15 20146
16 20105
17 20234
18 20114
19 20124
20 20174

About Jakub Šimko

Jakub Šimko is a scholar working on Artificial Intelligence, Sociology and Political Science, Computer Vision and Pattern Recognition, Computer Science Applications and Information Systems, having authored 31 papers that have together received 221 indexed citations. Recurring topics across this work include Mobile Crowdsensing and Crowdsourcing (7 papers), Misinformation and Its Impacts (7 papers), Gaze Tracking and Assistive Technology (5 papers), Topic Modeling (5 papers), Spam and Phishing Detection (4 papers), Semantic Web and Ontologies (4 papers), Advanced Image and Video Retrieval Techniques (4 papers) and Advanced Text Analysis Techniques (3 papers). The work is most often cited by research in Computer Science Applications (40 citations), Human-Computer Interaction (25 citations), Health Informatics (5 citations), Artificial Intelligence (89 citations) and Communication (18 citations). Jakub Šimko has collaborated with scholars based in Slovakia, Czechia and United States. Frequent co-authors include Mária Bieliková, Róbert Móro, Ivan Srba, Peter Brusilovsky, Michal Kompan, Dongwon Lee, Cesare Pautasso, Pavol Návrat, Thái Hoàng Lê and Dereck Toker. Their work appears in journals such as International Journal on Semantic Web and Information Systems, Multimedia Tools and Applications, International Journal of Human-Computer Studies, Journal of Eye Movement Research and Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence.

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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