Understanding User-Community Engagement by Multi-faceted Features: A Case Study on Twitter
The widespread use of social networking websites in recent years has suggested a need for effective methods to understand the new forms of user engagement, the factors impacting them, and the fundamental reasons for such engagements. We perform exploratory analysis on Twitter to understand the dynamics of user engagement by studying what attracts a user to participate in discussions on a topic. We identify various factors which might affect user engagement, ranging from content properties, network topology to user characteristics on the social network, and use them to predict user joining behavior. As opposed to traditional ways of studying them separately, these factors are organized in our framework, People-Content-Network Analysis (PCNA), mainly designed to enable understanding of human social dynamics on the web. We perform experiments on various Twitter user communities formed around topics from diverse domains, with varied social significance, duration and spread. Our findings suggest that capabilities of content, user and network features vary greatly, motivating the incorporation of all the factors in user engagement analysis, and hence, a strong need can be felt to study dynamics of user engagement by using the PCNA framework. Our study also reveals certain correlation between types of event for discussion topics and impact of user engagement factors.
& Sheth, A. P.
(2011). Understanding User-Community Engagement by Multi-faceted Features: A Case Study on Twitter. .