Name
Abstract | ||
|---|---|---|
Due to the exponential growth in the popularity of online social networks (OSNs), such as Twitter and Facebook, the number of machine accounts that are designed to mimic human users has increased. Social bots accounts (Sybils) have become more sophisticated and deceptive in their efforts to replicate the behaviors of normal accounts. As such, there is a distinct need for the research community to develop technologies that can detect social bots. This paper presents a review of the recent techniques that have emerged that are designed to differentiate between social bot account and human accounts. We limit the analysis to the detection of social bots on the Twitter social media platform. We review the various detection schemes that are currently in use and examine common aspects such as the classifier, datasets, and selected features employed. We also compare the evaluation techniques that are employed to validate the classifiers. Finally, we highlight the challenges that remain in the domain of social bot detection and consider future directions for research efforts that are designed to address this problem. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/INNOVATIONS.2018.8605995 | 2018 International Conference on Innovations in Information Technology (IIT) |
Keywords | Field | DocType |
Social Bots,Twitter,Detection,Sybil | Data science,Data mining,Social media,Social network,Computer science,Crowdsourcing,Popularity,Feature extraction,Classifier (linguistics),Replicate | Conference |
ISSN | ISBN | Citations |
2325-5498 | 978-1-5386-6674-6 | 0 |
PageRank | References | Authors |
0.34 | 20 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Eiman Alothali | 1 | 0 | 0.34 |
| Nazar Zaki | 2 | 139 | 14.31 |
| Elfadil A. Mohamed | 3 | 0 | 0.34 |
| Hany Al Ashwal | 4 | 0 | 0.68 |