Models of Social Groups in Blogosphere Based on Information about Comment Addressees and Sentiments

TL;DR

This paper analyzes social group structures in the blogosphere by modeling relations based on comment addressees and sentiments, using sequential data analysis and clustering algorithms to identify stable groups over time.

Contribution

It introduces models that incorporate comment addressees and sentiment analysis for identifying social groups in blogs, advancing understanding of dynamic social structures.

Findings

Identified stable social groups in blogs over time

Compared different models based on addressee and sentiment

Demonstrated the effectiveness of CPM algorithm in social group detection

Abstract

This work concerns the analysis of number, sizes and other characteristics of groups identified in the blogosphere using a set of models identifying social relations. These models differ regarding identification of social relations, influenced by methods of classifying the addressee of the comments (they are either the post author or the author of a comment on which this comment is directly addressing) and by a sentiment calculated for comments considering the statistics of words present and connotation. The state of a selected blog portal was analyzed in sequential, partly overlapping time intervals. Groups in each interval were identified using a version of the CPM algorithm, on the basis of them, stable groups, existing for at least a minimal assumed duration of time, were identified.