Name
Abstract | ||
|---|---|---|
Most existing techniques for spam detection on Twitter aim to identify and block users who post spam tweets. In this paper, we propose a semi-supervised spam detection (S3D) framework for spam detection at tweet-level. The proposed framework consists of two main modules: spam detection module operating in real-time mode and model update module operating in batch mode. The spam detection module con... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TCSS.2017.2773581 | IEEE Transactions on Computational Social Systems |
Keywords | DocType | Volume |
Detectors,Twitter,Unsolicited electronic mail,Feature extraction,Real-time systems | Journal | 5 |
Issue | ISSN | Citations |
1 | 2329-924X | 12 |
PageRank | References | Authors |
0.60 | 13 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Surendra Sedhai | 1 | 54 | 2.83 |
| Aixin Sun | 2 | 3071 | 156.89 |