Today’s societies have become more dependent on social networks in terms of communications and interactions. These networks contain most of the people?s activities, which can be public or even personal events. In the last decade, social networks have turned into more prominent platforms in managing and organizing public events. The Egyptian revolution in 2011 and the Ukrainian revolution in 2014 are good reflections of such events. However, it is not known how much the privacy issue of users is revealed in the reality as a consequence of their online interactions. In this work, we investigate the privacy issue in online social networks and its reflection on real life. Our dataset was extracted from the Facebook groups/pages that were involved in the 2019 Iraqi October revolution. Our approach generates a static network using the collected dataset. Then, we investigate the generated static network in terms of detecting potential anomalies. After that, we project the static network (including its characteristics) into a dynamic environment and generate a dynamic network for investigating the privacy issue in the real life. The contribution of this work lies in projecting a real-world static network into a dynamic environment aiming at investigating users? privacy in the real world. Finally, this kind of approach has not been given enough attention in the literature and yet to be deeply investigated in this article.