Detecting Topic Authoritative Social Media Users: A Multilayer Network Approach

After the impressive diffusion of social media and microblogging websites of the last few years, the identification of users having the capability of influencing other users’ choices is an important research topic because of the opportunities it can offer to many business companies. Most of the existing approaches, however, detect influencers by relying on centrality measures computed on networks that connect users having different types of inter-relationships. In this paper, we propose a method capable of finding influential users by exploiting the contents of the messages posted by them to express opinions on items, by modeling these contents with a three-layer network. Layers represent users, items, and keywords, along with intra-layer interactions among the actors of the same layer. Inter-layer connections are triples (<inline-formula><tex-math notation="LaTeX">$u$</tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$i$</tex-math></inline-formula>, <inline-formula><tex-math notation="LaTeX">$k$</tex-math> </inline-formula>) expressing the information that a user <inline-formula><tex-math notation="LaTeX">$u$</tex-math> </inline-formula> comments on an item <inline-formula><tex-math notation="LaTeX">$i$</tex-math></inline-formula> by using a keyword <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula>. By exploiting multilinear algebra, we present a method capable of extracting the most active users stating their point of view about dominant items tagged with dominant keywords. We conduct a series of experiments on different real-world datasets collected from Twitter and Yelp Social Networks about different topics. Experimental results show the ability of our approach to find influential users that are both authoritative in the user network and very active in posting opinions about the topic of interest.