Open Access

ARTICLE

Predicting Violence-Induced Stress in an Arabic Social Media Forum

by Abeer Abdulaziz AlArfaj¹, Nada Ali Hakami^2,*, Hanan Ahmed Hosni Mahmoud¹

1 Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, 11671, Saudi Arabia
2 Department of Computer Science, College of Computer Science and Information Technology, Jazan University, Jazan, Saudi Arabia

* Corresponding Author: Nada Ali Hakami. Email:

Intelligent Automation & Soft Computing 2023, 35(2), 1423-1439. https://doi.org/10.32604/iasc.2023.028067

Received 01 February 2022; Accepted 17 March 2022; Issue published 19 July 2022

Abstract

Social Media such as Facebook plays a substantial role in virtual communities by sharing ideas and ideologies among different populations over time. Social interaction analysis aids in defining people’s emotions and aids in assessing public attitudes, towards different issues such as violence against women and children. In this paper, we proposed an Arabic language prediction model to identify the issue of Violence-Induced Stress in social media. We searched for Arabic posts of many countries through Facebook application programming interface (API). We discovered that the stress state of a battered woman is usually related to her friend’s stress states on Facebook. We applied a large real database from Facebook platforms to analytically investigate the correlation of violence-induced stress states and the victim interactions on social media. We extracted a set of textual, spatial, and interaction attributes from various features. Therefore, we are proposing a hybrid model–an interaction graph model incorporated in a deep learning neural model to leverage post content and interaction data for violence-induced stress detection. Experiments depict that our proposed hybrid model can enhance the prediction performance by 10% in F1-measure. Also, considering the user interaction information can learn an interesting phenomenon, where, the sparse social interactions of violence-induced stress stressed victims is higher by around 15% percent non-battered users, signifying that the structure of the friends of such victims is less connected than non-stressed users.

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Keywords

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