Tun Lu

2.3k citations
147 papers · 1.5k · 1 hit paper · h-index 22

Impact in

Papers in

Tun Lu

133 papers receiving 1.5k citations

Tun Lu's Hit Papers

Cross-modal Ambiguity Learning for Multimodal Fake News Detection 2022 · 194 citations
1940+1+2Years since publication50100150

Peers

Tun Lu
Comparison fields: 5 of 146
  • Information Systems 697
  • Human-Computer Interaction 144
  • Computer Science Applications 108
  • Artificial Intelligence 566
  • Communication 90
Replace Ning Gu with:
Ning Gu China
Deirdre K. Mulligan United States
Eelco Herder Germany
Denis Parra Chile
Michaël Friedewald Germany
Longqi Yang United States
Nithya Sambasivan United States
Derek McAuley United Kingdom
Yoram Bachrach United Kingdom
Costas Vassilakis Greece
Tun Lu relative to Ning Gu China Ning Gu's profile →
Citations per field
00.5×1.7×
Ning Gu · 1×
Citations per year

Countries citing papers authored by Tun Lu

Since Specialization
Citations

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

Fields of papers citing papers by Tun Lu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 147 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Cross-modal Ambiguity Learning for Multimodal Fake News Detection
Hit paper breakdown →
2022194
2 201490
3 201672
4 201553
5 201649
6 201741
7 201141
8 201539
9 201437
10 201635
11 201127
12 202127
13 201627
14 202026
15 201425
16 201124
17 201724
18 202122
19 202222
20 202121

About Tun Lu

Tun Lu is a scholar working on Information Systems, Artificial Intelligence, Computer Networks and Communications, Sociology and Political Science and Communication, having authored 147 papers that have together received 1.5k indexed citations. Recurring topics across this work include Recommender Systems and Techniques (39 papers), Topic Modeling (15 papers), Service-Oriented Architecture and Web Services (14 papers), Advanced Graph Neural Networks (10 papers), Caching and Content Delivery (10 papers), Scientific Computing and Data Management (9 papers), Complex Network Analysis Techniques (9 papers) and Social Media and Politics (9 papers). The work is most often cited by research in Information Systems (697 citations), Human-Computer Interaction (144 citations), Computer Science Applications (108 citations), Artificial Intelligence (566 citations) and Communication (90 citations). Tun Lu has collaborated with scholars based in China, United States and United Kingdom. Frequent co-authors include Ning Gu, Peng Zhang, Dongsheng Li, Li Shang, Qin Lv, Hansu Gu, Xianghua Ding, Dongsheng Li, Yixuan Chen and Jie Sui. Their work appears in journals such as Proceedings of the ACM on Human-Computer Interaction, Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies, ACM Transactions on the Web, Knowledge-Based Systems and IEEE Transactions on Computational Social Systems.

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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