## 2 Background  
Over the past few years, several programmers and researchers were working on developing new tools, algorithms, and programming models to extract relevant information from Big Data. Generally, the volume of data to be processed exceeds the computing capabilities of traditional IT systems, so clusters and Clouds are exploited to effectively run parallel and distributed applications, capable of ensuring scalability and acceptable execution times [3].  
Even novel data mining techniques are created for extracting useful knowledge in different application fields, including trend discovery, trajectory mining, predictive data analytics, sentiment, and opinion mining. Several surveys on this argument were proposed in literature [4; 5; 6] trying to summarize the innumerable research works that were published in the last years on this argument. In the following, some examples of recent social media analysis applications are presented.  
Social media analysis frequently aims at understanding human dynamics and behaviors, such as understanding the most followed touristic routes and the period of year when touristic attractions are visited [11; 12], detecting the crowded areas of a city where transport facilities need to be improved [13], finding the most suitable areas for opening new businesses [14], analyzing the purchasing behavior of users [15], uncovering the behaviors of fans following important sporting events [16].  
Many research projects focus not only on the data analysis process, but also on other data processing tasks (e.g., data cleaning, preparation, and transformation), which are fundamentals while developing a data analysis application. These efforts, in particular, aim at assisting researchers and data analysts in implementing all of the phases that compose data analysis applications without having to start from scratch.  
SOCLE [17] is a data preparation framework specifically designed for social media applications, which provides a large set of operators for data pruning, data enrichment and data normalization. [PERSON] et al. [18] presented a MapReduce framework that provides developers with easy-to-use modules for data collection, data storage, and data analytics (e.g., sentiment analysis and reporting). Still in the context of Twitter data, [PERSON] et al. [19] proposed an unsupervised framework able to discover events from large volumes of tweets through a pipeline process consisting of filtering, extraction, and categorization steps. [PERSON] et al. [13] presented a Cloud-based framework for developing social media analysis applications to support mobility in smart cities. It manages data collection from social media platforms APIs (e.g., Flickr, Foursquare, Twitter) and from other web sources (e.g., websites, blogs). SODATO (SOcial Data Analytics Tool) [20] is a web-based tool for programming data analytics on social media data, which includes some predefined analysis methods, such as sentiment analysis, text analysis, content performance analysis, influencer analytics, and so on.  
Taking into account the advances produced by research in this area, this paper presents the definition of three significant applications of Social Big Data analysis in the fields of trajectory mining, sentiment analysis, and topic detection. Such applications analyze large amounts of data and usually require long computation times. Consequently, to get results in a reasonable time, we used ParSoDA for enabling these applications to run on Cloud. The ParSoDA's runtime was created specifically for dealing with large amounts of data. As a result, it is built on the MapReduce architecture and can run in parallel on distributed computing systems like HPC and Clouds.