Abstract
Social Media such as Facebook plays a substantial role in virtual com-munities 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 chil-dren. 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 tex-tual, 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 vio-lence-induced stress detection. Experiments depict that our proposed hybrid mod-el 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.