Estimating domain-based user influence in social networks

Abstract

Social networks and microblogging systems play a fundamental role in the diffusion of information. The information, from different sources, reaches each user through multiple connections, the study of which is indispensable for the sake of understanding the dynamics of its evolution and expansion. In this paper, we propose a system which enables to delve in the spread of information over a network along with the changes in the user relationships with respect to the domain of discussion. To cope up with the goal, considering Twitter as a case study, we analyse the tweets as the starting point or as the generators of the information which later flows through subsequent retweets. Furthermore, we integrate a N-Gram model classification approach for categorizing, under various domains, the information shared within the social network under consideration. We finally leverage this formalization to propose a domain-based model which aims to estimate the influence of a user, on a community, in the domain under consideration. In conclusion, using a sample of the Twitter network we then present a set of case studies and real case scenarios that show the validity of the proposed approach.

A Prediction Method of Mobile User Preference Based on the Influence between Users

“…Anger et al considered the content of communication between users and mutual information in the constructed model of influence [23]. Cataldi et al proposed a calculation method of user influence based on domain by analyzing the mutual information between users [24]. Guo et al employed the maximum likelihood estimation to calculate the influence between users by analyzing the history logs of user behavior [25].…”

A dynamic influence model of social network hotspot based on grey system

“…They are social identity, social interaction, retweet depth and opinion leader. Social identity, social interaction and retweet depth can be obtained by statistics of the topic's online data [6,18]. However, the opinion leader is based on the fan characteristic value [19][20][21].…”

“…Both user influence analysis and information diffusion analysis mentioned above focus on micro behavior level [18][19][20]. However, our user influence analysis focuses on the middle level.…”

On Behaviour-Based Network Inference and Distribution-Based Network Control

“…The work of Cataldi et al (2013) and Cataldi and Aufaure (2014) extended the existing topical influence work by incorporating behaviour in the form of Twitter 'retweets' (equivalent to the broadcast of a message received from another user). In this case, instead of only considering the follower graph and topical similarity between users, a new graph is constructed for each defined topic where directed edges from a node s to another node t exists if and only if s has retweeted something from t relating to the topic.…”

“…In addition to this, most of these measures produce global influence values and, in many cases, it would be difficult to determine specific influence levels between pairs of nodes using the same approach. While the work of Cataldi et al (2013) and Hajian and White (2011) have begun to explicitly include behaviour of users in influence measurement, including some local measures of influence between users, the following subsection will discuss methods for influence measurement that rely solely on behavioural analysis.…”