Social Media User Behavior Analysis in E-Government Context

Abstract

Social media provides platforms to communicate with large populations and creates a favorable environment exploit the benefit of having access to millions of users. Despite the broad interest, there is insufficient research on aspects of social media use, and very limited empirical research examining the social media within the public sector. In this study, we present a social media user behavior model as a function of different user types, i.e. light, heavy and automated users. In the model, different user types exhibit varying social media knowledge behaviors driven from different motivations, interests, and goals. The users' knowledge behaviors are analyzed in terms of knowledge creation, framing and targeting. Data of 160,000 tweets by nearly 40,000 twitter users during the year of 2014 for the city of Newark (NJ, USA) was collected and analyzed. The findings imply that 1) Light users reuse an existing content more often while heavy and automated users create an original content more often; 2) Per user, the automated users frame more than the heavy users who frame more than the light users; and 3) Light users tends to target a specific audience or specific locale while heavy and automated users broadcast to general audience rather than a specific targeted one.