Daniel Zeng

12.2k citations
438 papers · 7.6k · 1 hit paper · h-index 42

Impact in

Papers in

    • Topic Modeling 63
    • Sentiment Analysis and Opinion Mining 46
    • Advanced Text Analysis Techniques 45
    • Advanced Graph Neural Networks 37
    • Recommender Systems and Techniques 32

Daniel Zeng

407 papers receiving 7.2k citations

Daniel Zeng's Hit Papers

Social Media Analytics and Intelligence 2010 · 370 citations
3700+5+10Years since publication100200300

Peers

Daniel Zeng
Comparison fields: 5 of 193
  • Information Systems 2.3k
  • Statistical and Nonlinear Physics 1.1k
  • Artificial Intelligence 2.5k
  • Communication 386
  • Marketing 487
Replace Jon Crowcroft with:
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Daniel Zeng relative to Jon Crowcroft United Kingdom Jon Crowcroft's profile →
Citations per field
00.5×3.2×
Jon Crowcroft · 1×
Citations per year

Countries citing papers authored by Daniel Zeng

Since Specialization
Citations

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

Fields of papers citing papers by Daniel Zeng

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 438 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2004439
2 2007370
3
Social Media Analytics and Intelligence
Hit paper breakdown →
2010370
4 2007169
5 2018158
6 2007136
7 2003135
8 2009128
9 2006109
10 2007107
11 2013107
12 200299
13 201999
14 202292
15 201483
16 201581
17 201879
18 201475
19 200870
20 201569

About Daniel Zeng

Daniel Zeng is a scholar working on Artificial Intelligence, Information Systems, Statistical and Nonlinear Physics, Management Science and Operations Research and Sociology and Political Science, having authored 438 papers that have together received 7.6k indexed citations. Recurring topics across this work include Complex Network Analysis Techniques (82 papers), Topic Modeling (63 papers), Sentiment Analysis and Opinion Mining (46 papers), Advanced Text Analysis Techniques (45 papers), Opinion Dynamics and Social Influence (38 papers), Advanced Graph Neural Networks (37 papers), Recommender Systems and Techniques (32 papers) and Consumer Market Behavior and Pricing (29 papers). The work is most often cited by research in Information Systems (2.3k citations), Statistical and Nonlinear Physics (1.1k citations), Artificial Intelligence (2.5k citations), Communication (386 citations) and Marketing (487 citations). Daniel Zeng has collaborated with scholars based in China, United States and Hong Kong. Frequent co-authors include Hsinchun Chen, Zan Huang, Fei–Yue Wang, Xiaolong Zheng, Wenji Mao, Hsinchun Chen, Qiudan Li, Linjing Li, Ahmed Abbasi and Robert F. Lusch. Their work appears in journals such as IEEE Intelligent Systems, Journal of the Association for Information Systems, Decision Support Systems, INFORMS journal on computing and Journal of Medical Internet Research.

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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