Borut Sluban
Impact in
- Human-Computer Interaction top 1%
- Digital Communication and Language
- Artificial Intelligence top 5%
- Sentiment Analysis and Opinion Mining
- Advanced Text Analysis Techniques
- Machine Learning and Data Classification
- Imbalanced Data Classification Techniques
- Topic Modeling
Papers in
-
- Complex Network Analysis Techniques 4
- Opinion Dynamics and Social Influence 4
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- Social Media and Politics 2
- Co-authors
- Igor MozetičJasmina SmailovićPetra Kralj NovakNada LavračDragan GambergerStefano BattistonMiha GrćarSenja Pollak
- Journals
- PLoS ONE (2 papers)Data Mining and Knowledge Discovery (1 paper)BMC Bioinformatics (1 paper)Language Resources and Evaluation (1 paper)Applied Network Science (1 paper)
- Partner nations
- SloveniaSwitzerlandCroatia
In The Last Decade
Borut Sluban
12 papers receiving 624 citations
Hit Papers
Peers
Comparison fields: 5 of 92
- Human-Computer Interaction 266
- Artificial Intelligence 379
- Communication 68
- Experimental and Cognitive Psychology 61
- Literature and Literary Theory 46
Countries citing papers authored by Borut Sluban
This map shows the geographic impact of Borut Sluban'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 Borut Sluban with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Borut Sluban more than expected).
Fields of papers citing papers by Borut Sluban
This network shows the impact of papers produced by Borut Sluban. 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 Borut Sluban. The network helps show where Borut Sluban may publish in the future.
Co-authors
The 14 scholars most cited alongside Borut Sluban, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2018 | 3 | |
| 2 | Toward a better understanding of emotional dynamics on facebook | 2018 | 1 |
| 3 | 2017 | 1 | |
| 4 | Sentiment of Emojis Hit paper breakdown → | 2015 | 488 |
| 5 | 2015 | 32 | |
| 6 | 2015 | 47 | |
| 7 | 2014 | 3 | |
| 8 | 2014 | 5 | |
| 9 | 2013 | 60 | |
| 10 | 2013 | 7 | |
| 11 | Irregularity Detection in Categorized Document Corpora | 2012 | 1 |
| 12 | 2010 | 1 |
About Borut Sluban
Borut Sluban is a scholar working on Statistical and Nonlinear Physics, Communication, Artificial Intelligence, Human-Computer Interaction and Statistics, Probability and Uncertainty, having authored 12 papers that have together received 649 indexed citations. Recurring topics across this work include Complex Network Analysis Techniques (4 papers), Opinion Dynamics and Social Influence (4 papers), Machine Learning and Data Classification (3 papers), Social Media and Politics (2 papers), Advanced Text Analysis Techniques (2 papers), Electoral Systems and Political Participation (1 paper), Distributed Sensor Networks and Detection Algorithms (1 paper) and Misinformation and Its Impacts (1 paper). The work is most often cited by research in Human-Computer Interaction (266 citations), Artificial Intelligence (379 citations), Communication (68 citations), Experimental and Cognitive Psychology (61 citations) and Literature and Literary Theory (46 citations). Borut Sluban has collaborated with scholars based in Slovenia, Switzerland and Croatia. Frequent co-authors include Igor Mozetič, Jasmina Smailović, Petra Kralj Novak, Nada Lavrač, Dragan Gamberger, Stefano Battiston, Miha Grćar, Senja Pollak, Marko Popović and Michelangelo Puliga. Their work appears in journals such as PLoS ONE, Data Mining and Knowledge Discovery, BMC Bioinformatics, Language Resources and Evaluation and Applied Network Science.
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.