Activity Analysis of Users in the Implicit Communities Formed on Twitter

Considering the progressive prevalence of online social networks, research for analyzing these networks is growing fast. In these networks, various virtual communities are formed, and analysis of these communities in social networks is important. Some virtual communities are formed implicitly. For example, the individuals for whom a special hashtag or topic is promoted in their messages can be the members of an implicit community. Detection of this type of communities and their analysis play a significant role in the analysis of the users’ behavior and discovery of dominant opinions in that community. In this research, a virtual community on Twitter has been collected based on suicide hashtag (#suicide). Then, this community has been statistically and behaviorally analyzed according to the reactions of individuals to the community messages. Eventually, a fast and practical method has been presented to find the influential messages and recognize the community leaders. The methods and results in this research can also be generalized for analyzing the dataset of Scale-free networks based on different factors.

Keywords:

Social network analysis , Twitter , Implicit community , suicide hashtag