A social hypertext model for finding community in blogs

Abstract

Blogging has become the newest communication medium for creating a virtual community, a set of blogs linking back and forth to one another's postings, while discussing common topics. In this paper, we examine how communities can be discovered through interconnected blogs as a form of social hypertext [14]. We propose a method and model that detects structures of community in the social network of blogs by integrating McMillan and Chavis' sense of community [26] along with network analysis [8,11].From the model, we measure community in the blogs by aligning centrality measures from social network analysis [17] with measures of sense of community obtained using behavioural surveys.We then illustrate the use of this approach with a case study built around an independent music blog. The strength of community measures were found to be well aligned with the network structure, based on centrality measures. Even though the sample size from the case study was small, once the structure and measure of communities are calibrated according to our social hypertext model, communities can be automatically found and measured for other blogs without the need for behavioural surveys.

Identifying the influential bloggers in a community

“…Researchers work on community detection to explore various communities in Blogosphere [7,28,20,3]. Authors in [2] consider influence a characteristic of virtual communities, among others like membership, reinforcement of needs, shared emotional connection, whose presence governs the establishment of a community. Link structures and overlapping between different sub-communities are used to help identify influence between them.…”

Blogging at work and the corporate attention economy

“…More recent work looks at the lifespan of blogs [10] and social network influence in blogs [1,4,24]. There is now an increasing interest in corporate blogging.…”

A Review of Existing Measures, Methods and Framework for Tracking Online Community in Social Network

“…A centrality measure can be used to identify the most important people at the center of a network or those that are well connected. Various type of centrality measures such as degree [13,14] ,closeness [15,16,17], betweenness [18,19,20], information [21,22], eigenvector [23.24], and dependence centrality [25,26] have been used for characterizing the social behaviour and connectedness of nodes within networks. The centrality measures find more active people from one or more subgroups.…”