Using Self-labeling and Co-Training to Enhance Bots Labeling in Twitter

Eiman Alothali, Kadhim Hayawi, Hany Alashwal

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

Abstract

The rapid evolution in social bots have required efficient solutions to detect them in real-time. In fact, obtaining labeled stream datasets that contains variety of bots is essential for this classification task. Despite that, it is one of the challenging issues for this problem. Accordingly, finding appropriate techniques to label unlabeled data is vital to enhance bot detection. In this paper, we investigate two labeling techniques for semi-supervised learning to evaluate different performances for bot detection. We examine self-training and co-training. Our results show that self-training with maximum confidence outperformed by achieving a score of 0.856 for F1 measure and 0.84 for AUC. Random Forest classifier in both techniques performed better compared to other classifiers. In co-training, using single view approach with random forest classifier using less features achieved slightly better compared to single view with more features. Using multi-view of features in co-training in general achieved similar results for different splits.

Original languageEnglish
Title of host publication2022 IEEE/ACS 19th International Conference on Computer Systems and Applications, AICCSA 2022 - Proceedings
PublisherIEEE Computer Society
ISBN (Electronic)9798350310085
DOIs
Publication statusPublished - 2022
Event19th IEEE/ACS International Conference on Computer Systems and Applications, AICCSA 2022 - Abu Dhabi, United Arab Emirates
Duration: Dec 5 2022Dec 7 2022

Publication series

NameProceedings of IEEE/ACS International Conference on Computer Systems and Applications, AICCSA
Volume2022-December
ISSN (Print)2161-5322
ISSN (Electronic)2161-5330

Conference

Conference19th IEEE/ACS International Conference on Computer Systems and Applications, AICCSA 2022
Country/TerritoryUnited Arab Emirates
CityAbu Dhabi
Period12/5/2212/7/22

Keywords

  • co-training
  • self-labeling
  • Semi-supervised

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications
  • Hardware and Architecture
  • Signal Processing
  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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