TY - GEN
T1 - Detecting Social Bots on Twitter
T2 - 13th International Conference on Innovations in Information Technology, IIT 2018
AU - Alothali, Eiman
AU - Zaki, Nazar
AU - Mohamed, Elfadil A.
AU - Alashwal, Hany
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/2
Y1 - 2018/7/2
N2 - 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.
AB - 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.
KW - Detection
KW - Social Bots
KW - Sybil
KW - Twitter
UR - http://www.scopus.com/inward/record.url?scp=85062399827&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85062399827&partnerID=8YFLogxK
U2 - 10.1109/INNOVATIONS.2018.8605995
DO - 10.1109/INNOVATIONS.2018.8605995
M3 - Conference contribution
AN - SCOPUS:85062399827
T3 - Proceedings of the 2018 13th International Conference on Innovations in Information Technology, IIT 2018
SP - 175
EP - 180
BT - Proceedings of the 2018 13th International Conference on Innovations in Information Technology, IIT 2018
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 18 November 2018 through 19 November 2018
ER -