LAUSR

Drawing on digital identity theories, social software engineering theory (SSE), and the Privacy Safeguard (PriS) methodology, we examined the way that personal information uploaded on social media (SM) imposes privacy issues. Throughout a review on users’ self-representation on SM, we examined the impact of self-determination and self-disclosure on users’ privacy, and we identified the social attributes (SA) that cause privacy implications. This paper specifies 18 SA that users employ to achieve their optimal level of representation while summarizing possible ways that these attributes provoke users’ identification. In particular, our research has shown that SM users represent their personas by unveiling SA to construct popular, representative, and conversational profiles. As disclosing SA increases privacy implications, we intend to help users build profiles that respect their privacy. Examining users’ SA deepens our understanding of disclosing personal information on SM while leading to a better quantification of identity attributes; furthermore, users’ top five most revealing attributes were summarized. Considering that SSE addresses users’ privacy implications from an early stage of systems designing, our research, identifying the SA, will be helpful in addressing privacy from a socio-technical aspect, aiming at bridging the socio-technical gap by drawing designers’ attention to users’ social aspects.