Semi-supervised Bot Detection
Semi-Supervised Bot Detection is a machine learning approach to identifying automated social media accounts that leverages a small set of labeled bot and human examples alongside a larger body of unlabeled data to improve detection accuracy by exploiting underlying data distribution patterns.
12
Publications
597
Citations
46
Authors
27
Institutions
Publications per year
2017–2026
11
Parents
12
Detecting Bots on Russian Political Twitter
Sergey Sanovich, Richard Bonneau, Joshua A. Tucker · Big Data · 2017 · 147 citations
Social Sciences, Political Communication, Political Science +14
Social Bots Detection via Fusing BERT and Graph Convolutional Networks
Qinglang Guo, Haiyong Xie, Yangyang Li et al. · Symmetry · 2021 · 61 citations · Full text
1–5 of 12
46
The University of Texas at San Antonio, United States
1 publications · 72 citations
1
h-index
1
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1
h-index
1
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1
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1–5 of 46
27
Shandong Yingcai University
Jinan, China
1 h-index
1 publications
33 citations
The University of Texas at San Antonio
San Antonio, United States
1 h-index
1 publications
72 citations
Xinjiang University
Ürümqi, China
1 h-index
1 publications
61 citations
National Engineering Research Center for Information Technology in Agriculture
Beijing, China
1 h-index
3 publications
183 citations

Tarbiat Modares University
Tehran, Iran
1 h-index
2 publications
86 citations
United Arab Emirates University
Al Ain, United Arab Emirates
1 h-index
2 publications
40 citations
Jaypee Institute of Information Technology
Noida, India
1 h-index
1 publications
14 citations
Zayed University
Abu Dhabi, United Arab Emirates
1 h-index
1 publications
20 citations
1–8 of 27
2 publications · 53 citations
2
h-index
1 publications · 147 citations
1
h-index
1 publications · 61 citations
1
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1 publications · 104 citations
1
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JCS · 1 publications · 17 citations
1
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1–5