## 1 Introduction  
Massive amounts of digital data, generated every day on social media platforms, can be used to extract useful information about human interests, opinions, and dynamics [1]. The analysis of Social Big Data [2] belongs to a research area that deals precisely with studying the activities, interests, behaviors, and opinions of users by analyzing the posts published on social media platforms. A large community of researchers is focused on developing applications for analyzing Social Big Data, usually relying on advanced and scalable algorithms for obtaining accurate results in a reasonable time [3]. Novel machine learning applications are thus defined for extracting useful knowledge in different application fields, including trend discovery, social media analytics, pattern mining, sentiment analysis, and opinion mining. Different surveys were proposed in the literature [4; 5; 6] to summarize and describe the wide range of research papers published in recent years in this area. This paper presents some of the most recent activities we carried out in the field of Social Big Data analysis. In particular, it discusses how these research activities can be exploited and, if necessary, combined for the analysis of large amounts of social media data, aimed at extracting different kind of knowledge from three different perspectives: (i) from the posts published by tourists who visit a city, we can discover the main tourist attractions and also the mobility patterns (i.e., trajectories) across them [7]; (ii) from public discussions on social media close to important electoral events, it is possible to discover the political orientation of citizens and thus estimate the outcome of a political event [8]; (iii) from hashtags used by social media users, we discover the main topics underlying social media conversation and how users refer to them in publishing online content [9]. Concerning the _trajectory analysis_, we introduce AUDESOME, an algorithm for detecting user mobility patterns from content posted on social media. Starting from a large set of geotagged posts (e.g., Flickr posts or tweets), it performs a series of operations for detecting the keywords identifying the Places-of-Interest (Pols) in a given area, the Region-of-Interest (RoI) associated to each PoI, and frequent mobility patterns in user movements across RoIs. Experimental results show that our technique is able to correctly extract the most frequent trajectories and mobility patterns of user groups compared to that of the techniques present in the state of the art. About _opinion mining_, we present Iterative Opinion Mining using Neural Networks (IOM-NN), a data-driven technique that exploits feed-forward neural networks for estimating the political polarization of social media users during elections. The technique repeatedly creates new categorization rules using a limited number of classification rules, which are produced from an initial set of hashtags that are notoriously in favor of specific political parties or factions. In particular, a classification rule uses the words/hashtags in a post to evaluate if it is in favor of a faction or not. Then, the classified posts of each user are used to determine his/her political alignment and, consequently, from the analysis of a large number of users, to estimate the outcome of a political event. Such a technique was tested on a real case study, achieving results that are more accurate than opinion polls and other approaches proposed in the literature. Lastly, about _topic discovery_, we present HASHtag recommendation using Sentence-to-Hashtag Embedding Translation (HASHET), a model for recommending a set of hashtags that are suitable for a given post. In particular, we discuss how the recommendation abilities of our model can be leveraged for linking the content published by users to the main discussion topics underlying social media conversation. HASHET uses two latent spaces, independent from each other, to embed the textual content of a post and the hashtags it contains.