Social media interactions are popularly implicated in psychological changes like radicalization. However, there are currently no viable methods to assess whether social media interactions actually lead to such changes. The purpose of the current research was to develop a methodological paradigm that can assess such longitudinal change in individuals’ social media posts. Using this method, we analyzed the longitudinal timelines of 110 Twitter users (40,053 tweets) who had expressed support for Daesh (also known as Islamic State, or ISIS) and we compared them to a baseline sample of twitter timelines (215,008 tweets by 109 users) to investigate the factors associated with within-person increases in conformity to the vernacular and linguistic style of tweets that supported violent extremism. We found that conformity to both extremist group vernacular and linguistic style increased over time, and with mobilizing online interactions. Thus, we show how to detect within-person changes over time in social media data and suggest why these changes occur, and in doing so, validate a methodological paradigm that can detect and predict within-person change in psychological group memberships through social media interactions.

Smith, L. G. E., Wakeford, L., Cribbin, T. F., Barnett, J., & Hou, W. K. (2020, in press). Detecting psychological change through mobilizing interactions and changes in extremist linguistic style. Computers in Human Behavior.

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See our recent Q&A on the SAGE MethodSpace blog about the challenges of conducting social research using Twitter data.

In the few years since the advent of ‘Big Data’ research, social media analytics has begun to accumulate studies drawing on social media as a resource and tool for research work. Yet, there has been relatively little attention paid to the development of methodologies for handling this kind of data. The few works that exist in this area often reflect upon the implications of ‘grand’ social science methodological concepts for new social media research (i.e. they focus on general issues such as sampling, data validity, ethics, etc.). By contrast, we advance an abductively oriented methodological suite designed to explore the construction of phenomena played out through social media. To do this, we use a software tool – Chorus – to illustrate a visual analytic approach to data. Informed by visual analytic principles, we posit a two-by-two methodological model of social media analytics, combining two data collection strategies with two analytic modes. We go on to demonstrate each of these four approaches ‘in action’, to help clarify how and why they might be used to address various research questions.

Brooker, P., Barnett, J., & Cribbin, T. (2016). Doing social media analytics. Big Data & Society, 3(2). Download paper

Harvesting and visualising ‘big’ social media data is an increasingly feasible practice for social scientists. Yet whilst there is an emerging and substantial body of literature utilising social media as a data resource, there are a number of computational issues affecting data collection and analysis that for the large part remain hidden from the researcher’s view but which may problematise the findings we can legitimately draw from social media. This chapter outlines and explores two such issues as they occur for data taken from Twitter , commenting on how they might be handled in the undertaking of digital social science research. Here, we agree wholeheartedly subscribe to with Procter et al.’s insistence ‘that social researchers be trained in the underlying concepts of computational methods and tools so they can decide when and how to apply them’ (2013: 209). As such, we aim to outline highlight certain technical features and/or constraints pertaining to the collection and processing of Twitter data. In doing this we aim to, thereby helping help researchers to incorporate a technical understanding of the mechanics of digital research tools into robust and thoughtful analyses of their data.

Brooker, P., Barnett, J., Cribbin, T., & Sharma, S. (2015). Have we even solved the first ‘big data challenge?’ Practical issues concerning data collection and visual representation for social media analytics. In H. Snee, C. Hine, Y. Morey, S. Roberts, & H. Watson (Eds.), Digital Methods for Social Science:  An Interdisciplinary Guide to Research Innovation. Palgrave Macmillan. or e-copy on Google Books

The power and promise of social media as a resource and tool for doing social research is widely recognised and much vaunted. Social media data is becoming an increasingly attractive resource for social scientists, but the question remains as to what exactly we might want to do with data like this. The present study describes a small-scale interdisciplinary project in medical sociology which instigated the development of an innovative method for making practical use of ‘big data’ drawn from Twitter. What results is a depiction of how a collaboration between software developers, requirements engineers and social scientists demonstrated a need for a new method of data capture, a description of the method by which that need was addressed, and a discussion of the value of the insights that can be drawn through using that method.

Brooker, P., Barnett, J., Cribbin, T., Lang, A., & Martin, J. (2013). User-Driven Data Capture: Locating and Analysing Twitter Conversation about Cystic Fibrosis without Keywords. In SAGE Research Methods Cases. London, United Kingdom: SAGE Publications, Ltd. doi: