Dan Liebling
Impact in
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- Complex Network Analysis Techniques
- Information Systems top 2%
- Web Data Mining and Analysis
- Information Retrieval and Search Behavior
- Recommender Systems and Techniques
- Expert finding and Q&A systems
Papers in
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- Expert finding and Q&A systems 3
- Web Data Mining and Analysis 2
- Spam and Phishing Detection 1
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- Mobile Crowdsensing and Crowdsourcing 5
- Co-authors
- Susan Dumais (5 shared papers)Daniel Ramage (1 shared paper)Doug Downey (1 shared paper)Eric Horvitz (1 shared paper)Fernando Díaz (1 shared paper)Georg Buscher (1 shared paper)Ryen W. White (1 shared paper)Qingyao Ai (2 shared papers)
- Journals
- Proceedings of the International AAAI Conference on Web and Social Media (4 papers)
- Partner nations
- United StatesUnited KingdomSouth Korea
In The Last Decade
Dan Liebling
9 papers receiving 630 citations
Dan Liebling's Hit Papers
Peers
Comparison fields: 5 of 55
- Statistical and Nonlinear Physics 213
- Information Systems 349
- Artificial Intelligence 354
- General Social Sciences 31
- Computer Science Applications 51
Countries citing papers authored by Dan Liebling
This map shows the geographic impact of Dan Liebling'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 Dan Liebling with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dan Liebling more than expected).
Fields of papers citing papers by Dan Liebling
This network shows the impact of papers produced by Dan Liebling. 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 Dan Liebling. The network helps show where Dan Liebling may publish in the future.
Co-authors
The 15 scholars most cited alongside Dan Liebling, 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 | Characterizing Microblogs with Topic Models Hit paper breakdown → | 2010 | 489 |
| 2 | 2008 | 101 | |
| 3 | 2013 | 37 | |
| 4 | 2018 | 20 | |
| 5 | 2017 | 18 | |
| 6 | 2017 | 13 | |
| 7 | 2021 | 3 | |
| 8 | 2021 | 2 | |
| 9 | 2021 | 1 |
About Dan Liebling
Dan Liebling is a scholar working on Information Systems, Computer Science Applications, Information Systems and Management, Artificial Intelligence and Human-Computer Interaction, having authored 9 papers that have together received 684 indexed citations. Recurring topics across this work include Mobile Crowdsensing and Crowdsourcing (5 papers), Personal Information Management and User Behavior (3 papers), Usability and User Interface Design (3 papers), Expert finding and Q&A systems (3 papers), Web Data Mining and Analysis (2 papers), Complex Network Analysis Techniques (2 papers), Topic Modeling (2 papers) and Spam and Phishing Detection (1 paper). The work is most often cited by research in Statistical and Nonlinear Physics (213 citations), Information Systems (349 citations), Artificial Intelligence (354 citations), General Social Sciences (31 citations) and Computer Science Applications (51 citations). Dan Liebling has collaborated with scholars based in United States, United Kingdom and South Korea. Frequent co-authors include Susan Dumais, Daniel Ramage, Doug Downey, Eric Horvitz, Fernando Díaz, Georg Buscher, Ryen W. White, Qingyao Ai, Nick Craswell and Jaime Teevan. Their work appears in journals such as Proceedings of the International AAAI Conference on Web and Social Media.
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.