Detection of strong attractors in social media networks

Background: Detection of influential actors in social media such as Twitter or Facebook plays an important role for improving the quality and efficiency of work and services in many fields such as education and marketing. Methods: The work described here aims to introduce a new approach that characterizes the influence of actors by the strength of attracting new active members into a networked community. We present a model of influence of an actor that is based on the attractiveness of the actor in terms of the number of other new actors with which he or she has established relations over time. Results: We have used this concept and measure of influence to determine optimal seeds in a simulation of influence maximization using two empirically collected social networks for the underlying graphs. Conclusions: Our empirical results on the datasets demonstrate that our measure stands out as a useful measure to define the attractors comparing to the other influence measures

Influence Assessment in Twitter Multi-Relational Network

International audienceInfluence in Twitter has become recently a hot research topic since this micro-blogging service is widely used to share and disseminate information. Some users are more able than others to influence and persuade peers. Thus, studying most influential users leads to reach a large-scale information diffusion area, something very useful in marketing or political campaigns. In this paper, we propose a new approach for influence assessment on Twitter network, it is based on a modified version of the conjunctive combination rule in belief functions theory in order to combine different influence markers such as retweets, mentions and replies. We experiment the proposed method on a large amount of data gathered from Twitter in the context of the European Elections 2014 and deduce top influential candidates