Using Twitter Mentions and a Graph Database to Analyse Social Network Centrality

Abstract

Social networks are one category of social media that facilitates the formation of communities, sharing of content, and meeting people. Twitter is a popular microblogging and social networking service. Social media marketers within business organisations, are interested in identifying popular social network users, known as influencers who can be targeted for purposes of word-of-mouth branding. For Twitter, influencers are those users who have many followers. Influencers are typically identified through graph mining of social networks data. This type of mining involves the analysis of links between the graph nodes which store data for social network members. Follows relationships are commonly used to analyse Twitter social networks. The purpose of this paper is to demonstrate how mentioned relationships in Twitter data can be used to create a social networks graph database. Centrality measures are then used to analyse the social networks. It is demonstrated that the analysis of social networks based on the mentioned relationships can provide more information about influencers compared to the analysis of social networks based on the follows relationships.