TY  - EJOU
AU  - AlArfaj, Abeer Abdulaziz 
AU  - Hakami, Nada Ali 
AU  - Mahmoud, Hanan Ahmed Hosni 

TI  - Predicting Violence-Induced Stress in an Arabic Social Media Forum
T2  - Intelligent Automation \& Soft Computing

PY  - 2023
VL  - 35
IS  - 2
SN  - 2326-005X

AB  - 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.
KW  - Arabic language analysis; violence-induced stress detection; hybrid model; deep learning

DO  - 10.32604/iasc.2023.028067