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 (u, i, k) expressing the information that a user u comments on an item i by using a keyword k. 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 realworld 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.

Microblogs have become an important origin of information regarding events happening in a location during a time period. Analyzing and clustering these streams of short textual messages is an important research activity which is attracting the interest of both public and private organizations, since the extracted knowledge can be exploited to enhance the comprehension of people behavior and the onset of emergency situations. Clustering these streams requires efficient algorithms capable of analyzing this continuos deluge of data. The paper proposes an online algorithm that incrementally groups tweet streams into clusters. The approach summarizes the examined tweets into the cluster centroids generated so far. The assignment of a tweet to a centroid uses a similarity measure that takes into account both the cluster age and the terms occurring in the tweet. Experiments on messages posted by users in the Manhattan area show that the method is able to extract events effectively taking place in the examined period.