Social Media Analytics

• In Event: Social Media and the Public Sphere: An Agenda for Future Research

Abstract

Social media analytics, and other forms of “big data,” provide promising avenues for communications research. While certainly data analyses based on early adopters of any given technology, or platform, will present demographic and psychographic skews, these initial procedures, operationalizations and models will allow for future refinement and advancement. Ultimately, as use becomes more generalized and normalized, social media analytic models will improve their predictive abilities. However, there are certain myths about social media analytics that need to be treated with caution. Myth number 1: Big data lets the data speak for itself. As most researchers have been aware for a long time, data does not speak for itself. On the contrary, data is a social production that is open to interpretation, reconfiguration and explanation. Big data is no different, if anything, more interpretation, reconfiguration and explanation will be needed. Myth number 2: Big data is big enough. Without denying the amazing opportunities that social media analytics provide, we have to keep in mind that most platforms capture but a sliver of people’ interactions, so even capturing multiple slices you will never be able to capture the full complexity of human interaction. Myth number 3: Big data is about the traces we unwittingly leave behind. While self-report as a mode of observation has limitations that have been discussed for years, as people become cognizant of being observed, big data will be as susceptible to the well-known Hawthorne effect as “small data.” This presentation analyzes some of the opportunities and limitations of big data in general and social media analytics in particular. Focusing on a series of examples that use “big data” we provide an assessment of the potential of big data to advance social science research.

Author

• Hernando Rojas, U of Wisconsin - Madison