The Internet has experienced a constant growth and development, both in the past and nowadays, creating digital traces that can be collected and processed to define different individual schemes, themselves useful to discern both single- and group-related behaviors. This is indeed a time when a huge quantity of information has an outstanding speed of diffusion throughout the networks: Data equipped with these features have thereby been labeled as big data (Mayer-Schönberger and Cukier 2013; Erl et al. 2016). Some figures per minute, hinting at an approximate dimension of the phenomenon, are the following: millions of e-mails, Twitter and Facebook posts, thousands of Instagram photos and the like. As a consequence, such massive volumes of data are becoming a basic feature of the society and, at the same time, the ability to analyze, correlate and learn from them is turning into a useful element to compete as well as to support growth, productivity and innovation in different fields (Lin et al. 2010; Bello-Orgaz et al. 2016). In this perspective, broadband Internet connections and social media are playing a fundamental role in strengthening the relationship between companies and their customers: They have the power to dramatically change the marketing strategies (Erevelles et al. 2016) and the political attitudes making successful predictions (Song et al. 2014). This is also proven by the movement of a great amount of investments toward social media-driven decision systems, during the last years, to the detriment of traditional alternatives (Bowen and Bowen 2016; del Val et al. 2016). Although big data can be definitely considered a blessing for decision-making, having big data does not automatically lead to better marketing as they are intertwined with some important challenges and issues: the impossibility to use a unique central unit and classical storage facilities, the need for real time analytics, the correctness of the insights, privacy preservation and so on (Lesk 2013; Lu et al. 2015). An important role in the context of social media analytics is played by machine learning and computational intelligence techniques. Indeed, text mining, user profiling and localization, sentiment analysis, social sensing and the like are just some of the means used to perform a deep analysis of the traces that people continuously leave on social media (Zeng et al. 2010; Liu 2012; Li et al. 2016). Recently, a number of contributions for exploiting machine learning and computational intelligence techniques for big data analysis have been discussed in the specialized literature (Wu et al. 2014; Jamshidi et al. 2015; Moreno et al. 2016; Jha et al. 2016).