Kai Shu
- Information Systems top 0.05%
- Spam and Phishing Detection 36
- Recommender Systems and Techniques 6
- Sociology and Political Science top 0.1%
- Misinformation and Its Impacts 49
- Signal Processing top 0.5%
- Artificial Intelligence top 0.2%
- Topic Modeling 24
- Sentiment Analysis and Opinion Mining 7
- Privacy-Preserving Technologies in Data 7
- Hate Speech and Cyberbullying Detection 6
- Communication top 1%
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- Complex Network Analysis Techniques 11
- Journals
- Information Processing & Management (4 papers)Scientific Reports (1 paper)Chemical Communications (1 paper)
- Partner nations
- United StatesChinaAustralia
In The Last Decade
Kai Shu
96 papers receiving 5.7k citations
Hit Papers
Peers
Comparison fields: 5 of 122
- Information Systems 3.7k
- Sociology and Political Science 4.7k
- Signal Processing 1.1k
- Artificial Intelligence 3.1k
- Communication 485
Countries citing papers authored by Kai Shu
This map shows the geographic impact of Kai Shu'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 Kai Shu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kai Shu more than expected).
Fields of papers citing papers by Kai Shu
This network shows the impact of papers produced by Kai Shu. 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 Kai Shu. The network helps show where Kai Shu may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Kai Shu, 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 | 2025 | 2 | |
| 2 | 2025 | 1 | |
| 3 | 2025 | 1 | |
| 4 | 2025 | 0 | |
| 5 | 2024 | 6 | |
| 6 | Combating misinformation in the age of LLMs: Opportunities and challengesbreakdown → | 2024 | 40 |
| 7 | 2024 | 3 | |
| 8 | 2024 | 0 | |
| 9 | 2024 | 2 | |
| 10 | 2023 | 1 | |
| 11 | 2023 | 8 | |
| 12 | 2023 | 1 | |
| 13 | 2023 | 9 | |
| 14 | 2021 | 3 | |
| 15 | 2020 | 18 | |
| 16 | Incorporating User-Comment Graph for Fake News Detection. | 2020 | 3 |
| 17 | Unsupervised Fake News Detection on Social Media: A Generative Approachbreakdown → | 2019 | 197 |
| 18 | 2018 | 6 | |
| 19 | Exploiting Tri-Relationship for Fake News Detection. | 2017 | 68 |
| 20 | Multi-label informed feature selection | 2016 | 85 |
About Kai Shu
Kai Shu is a scholar working on Information Systems, Artificial Intelligence, Sociology and Political Science, Statistical and Nonlinear Physics and Signal Processing, having authored 105 papers that have together received 6.0k indexed citations. Recurring topics across this work include Misinformation and Its Impacts (49 papers), Spam and Phishing Detection (36 papers), Topic Modeling (24 papers), Complex Network Analysis Techniques (11 papers), Sentiment Analysis and Opinion Mining (7 papers), Privacy-Preserving Technologies in Data (7 papers), Hate Speech and Cyberbullying Detection (6 papers) and Recommender Systems and Techniques (6 papers). The work is most often cited by research in Information Systems (3.7k citations), Sociology and Political Science (4.7k citations), Signal Processing (1.1k citations), Artificial Intelligence (3.1k citations) and Communication (485 citations). Kai Shu has collaborated with scholars based in United States, China and Australia. Frequent co-authors include Huan Liu, Suhang Wang, Jiliang Tang, Amy Sliva, Dongwon Lee, Reza Zafarani, Limeng Cui, Xinyi Zhou, Huan Liu and Jundong Li. Their work appears in journals such as Information Processing & Management, Scientific Reports, Chemical Communications, Dalton Transactions and Big Data.
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.