Organization, Bot, or Human: Towards an Efficient Twitter User Classification

Authors

  • Kheir Eddine Daouadi MIRACL-FSEGS, Sfax University, Sfax
  • Rim Zghal Rebaï MIRACL-ISIMS, Sfax University, Sfax
  • Ikram Amous MIRACL-ISIMS, Sfax University, Sfax

DOI:

https://doi.org/10.13053/cys-23-2-3192

Keywords:

Social network analysis, twitter user classification, human vs. bot vs. organization, statistical-based approach, content-based approach, hybrid-based approach

Abstract

Today, through Twitter, researchers propose approaches for classifying user accounts. However, they have to face confidence challenges owing to the diversity of the types of data propagated throughout Twitter. In addition, the messages from Twitter are imprecise, very short and even written in many dialects and languages. Moreover, the majority of the related works focus on the overall user’s activity, which makes them not suitable at the post-level classification. This paper presents an alternative approach for classifying user accounts as being accounts of bots, humans or organizations. The suggested approach consists in accurately classifying user accounts from one single post by leveraging a minimal number of language-independent features. We performed several experiments over a Twitter datasets and supervised learn-based algorithms. Our results demonstrated that simply using a minimal number of language-independent features extracted from one single post is sufficient to classify user accounts accurately and quickly. Our proposed approach yielded high F1-measure (>95%) and high AUC (>99%) using Random Forest.

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Published

2019-06-28

Issue

Vol. 23 No. 2 (2019): Topic Trends in Computing Research (Guest Editors: A. Aguilar Meléndez, E. U. Moya Sánchez)

Section

Articles of the Thematic Issue

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