Profiling social media users with selective self-disclosure behavior

• Main Author: GONG, Wei
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2016
• Subjects: Adoption dynamics; graphical models; brand factors; social network; diffusion; recommendation systems; Databases and Information Systems; Social Media
• Online Access: https://ink.library.smu.edu.sg/etd_coll_all/1; https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=1006&context=etd_coll_all
• Institution: Singapore Management University

Social media has become a popular platform for millions of users to share activities and thoughts. Many applications are now tapping on social media to disseminate information (e.g., news), to promote products (e.g., advertisements), to manage customer relationship (e.g., customer feedback), and to source for investment (e.g., crowdfunding). Many of these applications require user profile knowledge to select the target social media users or to personalize messages to users. Social media user profiling is a task of constructing user profiles such as demographical labels, interests, and opinions, etc., using social media data. Among the social media user profiling research works, many focus on analyzing posted content. These works could run into the danger of non-representative findings as users often withhold some information when posting content on social media. This behavior is called selective self-disclosure. The challenge of profiling users with selective self-disclosure behavior motivates this dissertation, which consists of three pieces of research works.