The wide adoption of social networks over the past years has resulted in an ocean of data which presents an interesting opportunity for performing data mining and knowledge discovery in a real-world context. The enormity and high variance of the information that propagates through large user communities influence the public discourse in society and set trends and agendas in topics that range from marketing, education, business and medicine to politics, technology, and the entertainment industry. Mining the contents of social networks provides an opportunity to discover social structure characteristics, to analyze action patterns qualitatively and quantitatively, and gives the ability to predict future events. In recent years, decision makers have become savvy about how to translate social data into actionable information in order to leverage them for a competitive edge. Moreover,  social networks expose different aspects of the social behavior of its users. In this respect, many users of social networks are known as influencers. The influencers are users that usually publish their opinions about different topics, products, and services on the social networks, and then affect intentionally or unintentionally the opinions, emotions, or behaviors of other users on the social networks. Because of the high impact of influencers on the opinions and behaviors of other users, many organizations are interested in discovering influencers on social networks to increase the promotion and sale of their products and services. However, the discovery of influencers on social networks is a very complex problem that requires developing models, techniques, and algorithms for an appropriate analysis.

Traditional research in social network mining mainly focuses on theories and methodologies for community discovery, pattern detection, and evolution, behavioral analysis and anomaly (misbehavior) detection. While interesting and definitely worthwhile, the main distinguishing focus of this joint workshop will be the use of social network data for building predictive models that can be used to uncover hidden and unexpected aspects of user-generated content in order to extract actionable insights from them and for analyzing different aspects of social influence, such as influence maximization and discovering influencers. Thus, the focus is on algorithms and methods for (social) network analysis, data mining techniques to gain actionable real-world insights, and models and approaches for understanding influence dissemination and discovering influential users in social networks.