Effects of Ego Networks and Communities on Self-Disclosure in an Online Social Network

Understanding how much users disclose personal information in Online Social Networks (OSN) has served various scenarios such as maintaining social relationships and customer segmentation. Prior studies on self-disclosure have relied on surveys or users' direct social networks. These approaches, however, cannot represent the whole population nor consider user dynamics at the community level. In this paper, we conduct a quantitative study at different granularities of networks (ego networks and user communities) to understand users' self-disclosing behaviors better. As our first contribution, we characterize users into three types (open, closed, and moderate) based on the Communication Privacy Management theory and extend the analysis of the self-disclosure of users to a large-scale OSN dataset which could represent the entire network structure. As our second contribution, we show that our proposed features of ego networks and positional and structural properties of communities significantly affect self-disclosing behavior. Based on these insights, we present the possible relation between the propensity of the self-disclosure of users and the sociological theory of structural holes, i.e., users at a bridge position can leverage advantages among distinct groups. To the best of our knowledge, our study provides the first attempt to shed light on the self-disclosure of users using the whole network structure, which paves the way to a better understanding of users' self-disclosing behaviors and their relations with overall network structures.