Session 1: Interpretive data science: Rendering meaning ‘in the wild’

(Reflections from Stefanie Habersang)

Vern Glaser kicked-off the first session of this fantastic workshop. The aim of this session was to give the audience an idea what type of research we can do with topic modeling. Laura Nelson, Tim Hannigan, and Mark Kennedy reflected on different empirical examples in which they used topic modeling to build theory in social science research. The first empirical example was provided by Laura Nelson. She presented a compelling example how topic modeling helped her to identify new and overlooked tactics in environmental social movements and how these tactics were used by the movement to achieve change at different levels of society. Her research challenged current assumption in which political ideology is seen as a key dimension to distinguish different environmental social movements. Rather she postulates that it is a movement’s goal orientation, that is, at which level of society the movement claims responsibility to initiate environmental change (e.g. individual, collective, or institutional). This example was extremely interesting as it showed the potential to challenge current assumptions and build new robust theory with topic modeling. Another interesting application was presented by Tim Hannigan. In his research he rendered the kernel of a corporate scandal in the British parliament by studying the micro-processes of stigma. He showed that the extent of a scandal and MPs resignation did not depend on the degree of the scandal itself but rather the laughability of the scandal in the first seven days after disclosure. What I think was particularly interesting is that this research was initially not conducted as a project to understand the micro-processes of stigma. Instead it was the iterative back and forth between research question, data analysis, and theory building that finally let to the framing of the paper. Hence, this example nicely illustrated how topic modeling enables abductive reasoning and resonates with a qualitative, interpretive approach to theory-building. Last but not least, Mark Kennedy provided an insightful refection on both presentations. He advocated to “team-up” and to build stronger bridges between qualitative and quantitative research. Teaming up and being a community means that we can both manage the risks and opportunities from big data in a more reflective and fruitful way.

From my perspective, the three panelists’ did not only provide fascinating insights on the type of issues interpretive data science can address, but they also discussed some fundamental implications how to use topic modeling. First, Laura Nelson emphasized the importance of context. Understanding the context in which the data is embedded is essential for interpreting the results. She made it very clear that interpretive data science does not seek to identify universal patterns or physical laws that can be applied to all contexts. Rather interpretation is rooted in a qualitative understanding of the data. This understanding must be used to give voice to marginalized topics/issues in the data and to show diversity rather than uniformity. In line with this argument Laura Nelson skillfully concluded what interpretive data science should be all about: (1) meaning making not universal patterns (2) understanding not social law, and (3) contextual not universal understanding. Second, another very important implication came from Tim Hannigan’s presentation. It is often that we can best grasp the meaning of a large data set through one powerful exemplary story or case. In his example it was the illustrative case of one MP. However, a compelling single story must be supported by strong visualizations and representations. This does not only involve creativity but also exploration and computational skills. Finally, Mark Kennedy emphasized that differences between qualitative and quantitative researchers are less profound than we often think. By combining our complementary skills and using the community to enhance our toolkit we might be able to better explain, understand, and predict social phenomena. Overall, this introduction panel was the perfect kick-off for an inspiring workshop that fostered an inclusive climate to develop a multi-disciplinary community.