Social data analysis

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Social data analysis is the data-driven analysis of how people interact in social contexts, often with data obtained from social networking services. The goal may be to simply understand human behavior or even to propagate a story of interest to the target audience. Techniques may involve understanding how data flows within a network, identifying influential nodes (people, entities etc.), or discovering trending topics.

Social data analysis usually comprises two key steps: 1) gathering data generated from social networking sites (or through social applications), and 2) analysis of that data, in many cases requiring real-time (or near real-time) data analysis, measurements which understand and appropriately weigh factors such as influence, reach, and relevancy, an understanding of the context of the data being analyzed, and the inclusion of time horizon considerations. In short, social data analytics involves the analysis of social media in order to understand and surface insights which is embedded within the data.^[1]

Social data analysis can provide a new slant on business intelligence where social exploration of data can lead to important insights that the user of analytics did not envisage/explore. The term was introduced by Martin Wattenberg in 2005^[2] and recently also addressed as big social data analysis in relation to big data computing.

Systems are available to assist users in analyzing social data. They allow users to store data sets and create corresponding visual representations. The discussion mechanisms often use frameworks such as a blogs and wikis to drive this social exploration/Collaborative intelligence.