Muh‐Chyun Tang

602 citations
33 papers · 419 indexed · h-index 12

Muh‐Chyun Tang

31 papers receiving 384 citations

Peers

Muh‐Chyun Tang
Comparison fields: 5 of 94
  • Information Systems 191
  • Information Systems and Management 54
  • Statistics, Probability and Uncertainty 50
  • Computer Science Applications 29
  • Communication 37
Replace Margaret E. I. Kipp with:
Margaret E. I. Kipp United States
Julian Warner United Kingdom
Ann O’Brien United Kingdom
Ke Zhou United Kingdom
Martha E. Williams United States
Koraljka Golub Sweden
Laurent Romary France
Hussein Suleman South Africa
Clare Beghtol Canada
Sean L. Humpherys United States
Muh‐Chyun Tang relative to Margaret E. I. Kipp United States Margaret E. I. Kipp's profile →
Citations per field
00.5×7.5×
Margaret E. I. Kipp · 1×
Citations per year

Countries citing papers authored by Muh‐Chyun Tang

Since Specialization
Citations

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

Fields of papers citing papers by Muh‐Chyun Tang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Muh‐Chyun Tang. 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 Muh‐Chyun Tang. The network helps show where Muh‐Chyun Tang may publish in the future.

Co-authorship network

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

All Works

20 of 20 papers shown
#Work
1 20255
2 20226
3 20225
4 202111
5 20198
6 201747
7 20173
8
A cross-language co-word network comparison of Buddhist literature in Digital Library and Museum of Buddhist study
20171
9 20163
10 201431
11 201310
12 201310
13 20111
14 20104
15 20052
16
Rutgers' HARD and Web Interactive Track Experiments at TREC 2003.
200312
17 200399
18
Rutgers’ TREC 2001 Interactive Track Experience
199834
19 198921
20 198815

About Muh‐Chyun Tang

Muh‐Chyun Tang is a scholar working on Library and Information Sciences, Statistics, Probability and Uncertainty and Computer Science Applications, having authored 33 papers that have together received 419 indexed citations. Recurring topics across this work include Information Retrieval and Search Behavior (7 papers), scientometrics and bibliometrics research (6 papers), Digital Marketing and Social Media (6 papers), Complex Network Analysis Techniques (5 papers), Mobile Crowdsensing and Crowdsourcing (4 papers), Media Influence and Health (3 papers), Advanced Text Analysis Techniques (3 papers) and Technology Adoption and User Behaviour (3 papers). The work is most often cited by research in Information Systems (191 citations), Information Systems and Management (54 citations) and Statistics, Probability and Uncertainty (50 citations). Muh‐Chyun Tang has collaborated with scholars based in Taiwan, United States and Australia. Frequent co-authors include John L. Falk, Xiaojun Yuan, Nicholas J. Belkin, Diane Kelly, Colleen Cool, Gheorghe Mureşan, Giyeong Kim, Kuang‐Hua Chen, Chunmei Wang and Jieh Hsiang. Their work appears in journals such as SHILAP Revista de lepidopterología, Journal of Pharmacology and Experimental Therapeutics and Pharmacology Biochemistry and Behavior.

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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