Abstract
Scholars have shown much interest in whether diffusion is inflated through planting a piece of information by influential people (influentials). Although a few attempts have been made to discover structural gaps or gap fillers in the Twitter network, these efforts mainly concentrated on applying topological approaches to detect influentials in online networks. Further, though many studies explored diffusion on the Twitter network, they rarely examined the phenomenon with a theoretical framework. Through the #prayforparis Twitter network, this study attempted (1) to identify top influentials by applying multiple centrality measures and word frequency measures and (2) to examine social roles based on structural signatures of the Twitter network through the lens of the Diffusion of Innovation Theory. To fulfill the objectives of this study, the authors employed an innovative multi-method approach combining Social Network Analysis, word frequency analysis via NodeXL and R, and a qualitative approach to examine behavioral and structural relationships of the #prayforparis Twitter network. Top influentials of the network were pop music celebrities who shared condolences to the victims of the 2015 Paris attacks through their tweets. This study identified “celebrity” and “fan” as social roles based on the structural and qualitative analysis of the network as well as metrical examinations, including indegree and outdegree counts of the social roles of the “celebrities” and “fans.” Justin Bieber, the most dominant influential individual in the #prayforparis Twitter network, functioned as a breaking news provider through his tweet about the death of his friend during the Paris attacks. By filling the gap from the past studies, this study utilizes the theoretical improvement in the diffusion research as well as contributes to the methodological approach about influentials and social roles in the Twitter network.