Byung-Won On

1.2k citations
58 papers · 797 · 1 hit paper · h-index 13

Impact in

Papers in

    • Web Data Mining and Analysis 10
    • Technology and Data Analysis 7
    • Spam and Phishing Detection 7
    • Topic Modeling 8
    • Text and Document Classification Technologies 7
    • Sentiment Analysis and Opinion Mining 6
    • Advanced Text Analysis Techniques 6

Byung-Won On

47 papers receiving 731 citations

Byung-Won On's Hit Papers

Fake News Stance Detection Using Deep Learning Architecture (CNN-LSTM) 2020 · 204 citations
2040+2+4Years since publication50100150200

Peers

Byung-Won On
Comparison fields: 5 of 111
  • Signal Processing 155
  • Information Systems 312
  • Artificial Intelligence 400
  • Sociology and Political Science 198
  • Health Information Management 18
Replace Fahim Sufi with:
Fahim Sufi Australia
Azreen Azman Malaysia
Justin Zhan United States
Ahmed Alsayat Saudi Arabia
João Francisco Valiati Brazil
Ou Jin China
B. Annappa India
Jason Wei United States
Wenting Tu China
Praphula Kumar Jain India
Byung-Won On relative to Fahim Sufi Australia Fahim Sufi's profile →
Citations per field
00.5×1.5×2.4×
Fahim Sufi · 1×
Citations per year

Countries citing papers authored by Byung-Won On

Since Specialization
Citations

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

Fields of papers citing papers by Byung-Won On

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Byung-Won On. 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 Byung-Won On. The network helps show where Byung-Won On may publish in the future.

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 58 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Fake News Stance Detection Using Deep Learning Architecture (CNN-LSTM)
Hit paper breakdown →
2020204
2 2019122
3 202099
4 200551
5 201835
6 202133
7 201531
8 201824
9 202123
10 201822
11 200913
12 202112
13 201012
14 20148
15 20137
16 20177
17 20107
18 20196
19 20166
20 20106

About Byung-Won On

Byung-Won On is a scholar working on Information Systems, Artificial Intelligence, Computer Networks and Communications, Computer Vision and Pattern Recognition and Signal Processing, having authored 58 papers that have together received 797 indexed citations. Recurring topics across this work include Web Data Mining and Analysis (10 papers), Data Quality and Management (8 papers), Topic Modeling (8 papers), Technology and Data Analysis (7 papers), Spam and Phishing Detection (7 papers), Text and Document Classification Technologies (7 papers), Sentiment Analysis and Opinion Mining (6 papers) and Advanced Text Analysis Techniques (6 papers). The work is most often cited by research in Signal Processing (155 citations), Information Systems (312 citations), Artificial Intelligence (400 citations), Sociology and Political Science (198 citations) and Health Information Management (18 citations). Byung-Won On has collaborated with scholars based in South Korea, United States and Singapore. Frequent co-authors include Gyu Sang Choi, Arif Mehmood, Saleem Ullah, Maqsood Ahmad, Muhammad Umer, Zainab Imtiaz, Saima Sadiq, Dongwon Lee, Seog‐Chan Oh and Dongwon Jeong. Their work appears in journals such as IEEE Access, Applied Sciences, Information Sciences, Symmetry and Sensors.

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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