Mapping Movements – Social Movement Research and Big Data: Critiques and Alternatives

Abstract

The integration of social media and other online and mobile platforms and technologies into social movements around the globe has received significant academic attention around questions such as 'did social media cause a successful revolution?' and 'what information flows are apparent during protests on social media?'. Phenomena such as the Occupy Movement and the Arab Spring, which both saw widespread social media activity, have been the subjects of numerous studies in different fields, taking advantage of the wealth of social media data available (for example, Conover, Davis, et al., 2013; Costanza-Chock, 2012; Gaby & Caren, 2012; Papacharissi & Oliveira, 2012; Starbird & Palen, 2012). Automated processes and tools for capturing and processing such activity have meant that large social media datasets, of tweets in particular, are increasingly common sources for research into social movements and their activities and coverage online. However, the growing use of big data, social media-oriented approaches in the study of social movements raises new analytical and ethical challenges. There are important differences between big data research methodologies and previous approaches to social movement research, including a radically altered relationship between researchers and movement participants. Social media data capture and analysis around these topics can be carried out without having to be physically near or involved in the movements in question, which raises concerns about how to evaluate potential risks to participants; reciprocity; the accessibility of research to activists, including for comment and criticism; and how researchers engage with movement participants as knowledge-producers. Analytically, the use of big data methods for social movement research requires a careful attention to the biases in available data. Biases and gaps in the data may be introduced through strategic avoidance of social media or self-censorship by activists; the limitations of platform architecture and content policies; practices such as subtweeting and screen-capping which deliberately obscure links between accounts; the use of images and other non-text forms not captured by big data tools; the openness of different social media platforms to data capture; and the limitations of data capture tools themselves. In response to these challenges and concerns, we advocate the use of a mixed-methods approach that combines participant observation, in-depth interviews, and big data methods. This approach offers a framework for balancing the benefits of new quantitative methods with a need to prioritise an ethical approach to social movement research, as well as correcting some of the biases introduced by big data methods. This approach has been developed through the Mapping Movements project, which has examined movements and events in North America, Africa, and Europe. This chapter draws most prominently on the first published case study of the project, looking at the use of Twitter within the Occupy Oakland movement (Croeser & Highfield, 2014). This research demonstrates that a mixed-methods approach allows a better understanding of the contexts of social movements and their uses of social media. Considering both the online and the physical aspects of social movements enables a nuanced analysis of social media use by activists, looking beyond the object of study (the social medium of choice) at a quantitative level, to examine the intersections between these aspects of social movements. Crucially, our work demonstrates how blended methods can combine the strengths of different research approaches to collectively overcome the limitations of big, social media data, providing detail and explanation for activity found in – and hidden from – these datasets, addressing some of the gaps in big data research. We have not, however, dealt with the Occupy Oakland case study in detail here; rather, we have attempted to outline some of the most pressing ethical and analytical issues for big data research on social movements which are more broadly relevant, and to offer potential avenues which may provide (partial) solutions.