Analyzing and Predicting the Opinion Dynamics of Some Online Social Networks

Abstract

The world of web has witnessed colossal changes in the past decade. Web usage has seen a tremendous variation, from a mere browsing platform to a central hub of social interactions. Emails, blogs, Facebook, LinkedIn, Google groups, Twitter etc. form an indispensable part of daily life of an average individual, with social networking topping the list. With the advent of Social Network, it has become very popular trend for people to share what they are doing and what they are feeling regarding certain topics across various social network platform. newlineWith the participation of more active users, a lot of information is shared which is shaping the opinion of people on almost about anything in multiple dimensions. The process of opinion formation is a crucial factor as it can unfold different aspects of human behaviour about varied events. This information is valuable as it provides individual spontaneous emotional realities. Therefore, it is a need to analyse the user generated content and predict the social diffusion dynamics which occurs in online social networks. newlineOpinion formation is also dependent on the relationship among users which leads to opinion dynamics. Social network analysis (SNA) is an efficient tool to map and quantify the relationship and information flow between the entities. Many opinion models have been presented that explore how the local individual behaviour affects collective phenomena. Moreover, there are several phenomena in nature which closely resemble the human social network pattern such as, the Fire fly, Ant colony etc. Statistical physics methods are also used to explore how the local rules affect the collective behaviour of agents in a social network. In these models, agents hold one of several possible opinions, corresponding to discrete opinion models, or the opinions of agents take value from a certain range of real numbers, i.e., continuous opinion models. newlineThis thesis reveals two natural phenomena, Ostwald Ripening and grain growth, which have never been applied on social networks b