The Online Behaviour of the Algerian Abusers in Social Media Networks

TL;DR

This study analyzes the online behavior of Algerian Facebook users involved in cyberbullying and abusive content, providing data to improve automatic abuse detection systems amid linguistic diversity.

Contribution

It offers a statistical analysis of Algerian online abusive behavior and highlights the need for tailored detection systems considering dialectal language variations.

Findings

Identified patterns of abusive behavior among Algerian Facebook users

Highlighted linguistic challenges in abuse detection due to dialect diversity

Provided data to enhance automatic abuse detection algorithms

Abstract

Connecting to social media networks becomes a daily task for the majority of people around the world, and the amount of shared information is growing exponentially. Thus, controlling the way in which people communicate is necessary, in order to protect them from disorientation, conflicts, aggressions, etc. In this paper, we conduct a statistical study on the cyber-bullying and the abusive content in social media (i.e. Facebook), where we try to spot the online behaviour of the abusers in the Algerian community. More specifically, we have involved 200 Facebook users from different regions among 600 to carry out this study. The aim of this investigation is to aid automatic systems of abuse detection to take decision by incorporating the online activity. Abuse detection systems require a large amount of data to perform better on such kind of texts (i.e. unstructured and informal texts), and this is due to the lack of standard orthography, where there are various Algerian dialects and languages spoken.