Detecting Social Bots on Twitter: A Literature Review

Eiman Alothali, Nazar Zaki, Elfadil A. Mohamed, Hany Alashwal

Research output: Chapter in Book/Report/Conference proceedingConference contribution

39 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationProceedings of the 2018 13th International Conference on Innovations in Information Technology, IIT 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages175-180
Number of pages6
ISBN (Electronic)9781538666739
DOIs
Publication statusPublished - Jan 8 2019
Event13th International Conference on Innovations in Information Technology, IIT 2018 - Al Ain, United Arab Emirates
Duration: Nov 18 2018Nov 19 2018

Publication series

NameProceedings of the 2018 13th International Conference on Innovations in Information Technology, IIT 2018

Conference

Conference13th International Conference on Innovations in Information Technology, IIT 2018
Country/TerritoryUnited Arab Emirates
CityAl Ain
Period11/18/1811/19/18

Keywords

  • Detection
  • Social Bots
  • Sybil
  • Twitter

ASJC Scopus subject areas

  • Hardware and Architecture
  • Information Systems and Management
  • Information Systems

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