Open Access iconOpen Access

ARTICLE

Predicting Violence-Induced Stress in an Arabic Social Media Forum

Abeer Abdulaziz AlArfaj1, Nada Ali Hakami2,*, 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: email

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

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.

Keywords


Cite This Article

APA Style
AlArfaj, A.A., Hakami, N.A., Mahmoud, H.A.H. (2023). Predicting violence-induced stress in an arabic social media forum. Intelligent Automation & Soft Computing, 35(2), 1423-1439. https://doi.org/10.32604/iasc.2023.028067
Vancouver Style
AlArfaj AA, Hakami NA, Mahmoud HAH. Predicting violence-induced stress in an arabic social media forum. Intell Automat Soft Comput . 2023;35(2):1423-1439 https://doi.org/10.32604/iasc.2023.028067
IEEE Style
A.A. AlArfaj, N.A. Hakami, and H.A.H. Mahmoud "Predicting Violence-Induced Stress in an Arabic Social Media Forum," Intell. Automat. Soft Comput. , vol. 35, no. 2, pp. 1423-1439. 2023. https://doi.org/10.32604/iasc.2023.028067



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
  • 1025

    View

  • 591

    Download

  • 0

    Like

Share Link