Influence diffusion detection between linked bloggers

Abstract

The easy access and availability of blogs have encouraged web-users to change from consumers to providers of information. Providers of such content exert a certain level of influence on the receivers, giving rise to the interest in influence detection within the blogosphere. Previous works have focused on simple blog features to detect blogosphere influence which may not yield accurate results as influence itself is a complex concept that requires more in-depth analysis, where other intrinsic blog features, such as sentiments and agreement expressed in the blog posts or bloggers’ influence styles, that provide further knowledge on the blogs’ influence could be used to improve influence diffusion detection performance. This study aims to develop a model that uses sentiments on common topics, agreement expressions, and influence styles as possible features to detect influence diffusion between linked bloggers. Influence is defined and limited to the scope in the capacity of the linked blogger to exert on the linking blogger to agree with or have similar sentiments on the discussed topics.The objectives addressed in this dissertation study are: 1) Identify the relevant blog features that are useful in detecting influence diffusion; 2) Establish an automatic sentiment analysis approach that takes into consideration the complex relationships between words in the blog posts for influence diffusion detection; and 3) Develop an influence diffusion detection model using similar sentiments between the linking and linked bloggers, agreement towards the linked bloggers, and bloggers’ influence styles as features to improve performance. The first phase of the study involved statistical analysis on the blog dataset to identify the various blog features that could indicate influence. Results from the initial study show that similar sentiments on common concepts between linked blog posts give the clearest indication of influence as compared with other blog features. Recent sentiment analysis studies focused on the functional relationships between words using typed dependency parsing, which provides a refined analysis on the grammar and semantics of textual data. Consequently, the second phase of the study evaluated and established an automatic sentiment analysis process based on a linguistic and semantic analysis approach, which further considered the complex relationships between words in the context of the target terms. Context in this case refers to the neighboring terms of a current term within the blog post. In addition, influence as a concept could be correlated with other factors such as the different influence styles. Bloggers are not restricted to a monolithic description of influence based on link existence, and may differ in the manner in which they exert influence. Knowing the bloggers’ influence styles can better describe how influence is propagated in the network. Based on the findings in phase one and two, the third phase analyzed bloggers’ influence styles and developed the Influence Diffusion Detection (IDD) model…