Social networking sites (SNSs) offer users a platform to build and maintain social connections. Understanding when people feel comfortable sharing information about themselves on SNSs is critical to a good user experience, because self-disclosure helps maintain friendships and increase relationship closeness. This observational research develops a machine learning model to measure selfdisclosure in SNSs and uses it to understand the contexts where it is higher or lower. Features include emotional valence, social distance between the poster and people mentioned in the post, the language similarity between the post and the community and post topic. To validate the model and advance our understanding about online selfdisclosure, we applied it to de-identified, aggregated status updates from Facebook users. Results show that women self-disclose more than men. People with a stronger desire to manage impressions self-disclose less. Network size is negatively associated with self-disclosure, while tie strength and network density are positively associated.
For instance, REF developed a model to measure self-disclosure in social networking sites by deploying emotional valence, social distance between the poster and other people and linguistic features such as those identified by the Linguistic Inquiry and Word Count program (LIWC) etc.
Modeling Self-Disclosure in Social Networking Sites
CSCW '16
Psychology Computer Science