Influence detection between blog posts through blog features, content analysis, and community identity

Purpose - This study aims to investigate three common approaches - quantitative blog features analysis, content analysis, and community identification - to detect influence in the blogosphere (i.e. among blog posts). Design/methodology/approach - Quantitative analysis of blog features, together with manual sentiment and agreement analysis and community identification, were performed on blog postings and their content. Correlation studies of the selected influential variables were conducted to determine the effectiveness of each variable. Findings - Agreement expressed by the linking blogger with the linked blogger, similar sentiments expressed by both bloggers on common topics, and community identity are statistically significant features for detecting influence in the linked blogs. Research limitations/implications - A small data set of 196 blog posting pairs was used for the study as the blog features and content are analysed manually. Nonetheless statistical analysis on the data set identified significant features that could be used in future studies to automate the influence detection process. Practical implications - Knowing the effects of blog features and content analysis in detecting influence among blog posts allows a better influence detection method to determine the main chain of information propagation within the blogosphere and the identities of influential bloggers. Originality/value - The approach of using blog features, content analysis, and community identity provides a comprehensive evaluation of influence in the blogosphere. Unlike previous content analysis approaches that measure document similarity (i.e. common terms) between linked blog posts, our study applies sentiment and agreement analysis to consider the context of the whole blog post content.