Big Data Analytics and the Social Web: a Tutorial for the Social Scientist

Abstract

The social web or web 2.0 has become the biggest and most accessible repository of data about human (social) behavior in history. Due to a knowledge gap between big data analytics and established social science methodology, this enormous source of information, has yet to be exploited for new and interesting studies in various social and humanities related fields. To make one step towards closing this gap, we provide a detailed step-by-step tutorial on some of the most important web mining and analytics methods on a real-world study of Croatia’s biggest political blogging site. The tutorial covers methods for data retrieval, data conversion, cleansing and organization, data analysis (natural language processing, social and conceptual network analysis) as well as data visualization and interpretation. All tools that have been implemented for the sake of this study, data sets through the various steps as well as resulting visualizations have been published on-line and are free to use. The tutorial is not meant to be a comprehensive overview and detailed description of all possible ways of analyzing data from the social web, but using the steps outlined herein one can certainly reproduce the results of the study or use the same or similar methodology for other datasets. Results of the study show that a special kind of conceptual network generated by natural language processing of articles on the blogging site, namely a conceptual network constructed by the rule that two concepts (keywords) are connected if they were extracted from the same article, seem to be the best predictor of the current political discourse in Croatia when compared to the other constructed conceptual networks. These results indicate that a comprehensive study has to be made to investigate this conceptual structure further with an accent on the dynamic processes that have led to the construction of the network.