Sungyong Seo
- Information Systems top 2%
- Spam and Phishing Detection 2
- Artificial Intelligence top 5%
- Sentiment Analysis and Opinion Mining 2
- Topic Modeling 2
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- Model Reduction and Neural Networks 2
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- Misinformation and Its Impacts 4
- Crime Patterns and Interventions 1
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- Hydrological Forecasting Using AI 1
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- Stock Market Forecasting Methods 1
- Co-authors
- Yan LiuJing HuangHao YangChuizheng MengKarishma SharmaSirisha RambhatlaNatali RuchanskyHau Chan
- Journals
- arXiv (Cornell University) (3 papers)International Conference on Learning Representations (2 papers)Proceedings of the International AAAI Conference on Web and Social Media (1 paper)
- Partner nations
- United StatesChina
In The Last Decade
Sungyong Seo
10 papers receiving 395 citations
Hit Papers
Peers
Comparison fields: 5 of 74
- Information Systems 273
- Artificial Intelligence 295
- Computer Vision and Pattern Recognition 63
- Health Informatics 4
- Statistical and Nonlinear Physics 26
Countries citing papers authored by Sungyong Seo
This map shows the geographic impact of Sungyong Seo'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 Sungyong Seo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sungyong Seo more than expected).
Fields of papers citing papers by Sungyong Seo
This network shows the impact of papers produced by Sungyong Seo. 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 Sungyong Seo. The network helps show where Sungyong Seo may publish in the future.
Co-authorship network
The 23 scholars most cited alongside Sungyong Seo, 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 | 2021 | 3 | |
| 2 | 2021 | 9 | |
| 3 | Coronavirus on Social Media: Analyzing Misinformation in Twitter Conversations. | 2020 | 37 |
| 4 | Physics-aware Difference Graph Networks for Sparsely-Observed Dynamics | 2020 | 19 |
| 5 | 2019 | 3 | |
| 6 | Automatically Inferring Data Quality for Spatiotemporal Forecasting | 2018 | 3 |
| 7 | 2018 | 11 | |
| 8 | 2018 | 11 | |
| 9 | CSI: A Hybrid Deep Model for Fake News. | 2017 | 16 |
| 10 | Interpretable Convolutional Neural Networks with Dual Local and Global Attention for Review Rating Predictionbreakdown → | 2017 | 295 |
About Sungyong Seo
Sungyong Seo is a scholar working on Statistical and Nonlinear Physics, Applied Psychology and Artificial Intelligence, having authored 10 papers that have together received 407 indexed citations. Recurring topics across this work include Misinformation and Its Impacts (4 papers), Spam and Phishing Detection (2 papers), Sentiment Analysis and Opinion Mining (2 papers), Topic Modeling (2 papers), Model Reduction and Neural Networks (2 papers), Hydrological Forecasting Using AI (1 paper), Crime Patterns and Interventions (1 paper) and Stock Market Forecasting Methods (1 paper). The work is most often cited by research in Information Systems (273 citations), Artificial Intelligence (295 citations) and Computer Vision and Pattern Recognition (63 citations). Sungyong Seo has collaborated with scholars based in United States and China. Frequent co-authors include Yan Liu, Jing Huang, Hao Yang, Chuizheng Meng, Karishma Sharma, Sirisha Rambhatla, Yan Liu, Natali Ruchansky, Hau Chan and P. Jeffrey Brantingham. Their work appears in journals such as arXiv (Cornell University), International Conference on Learning Representations and 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.