French Resistance, Why Big Data is not yet leading the Presidential Game in France
Résumé
Over the last years, micro-targeting has become a core activity of electoral campaigns, leading to the development of computational propaganda (Woolley and Howard, 2019) as well as analytic activism (Kreiss, 2017). In a historical perspective, the current state of digital campaigning is the one of individual voter mobilization, based on big data, after a period of community building and activist mobilisation based on the Obama model (Gibson, 2020). However, most of the case studies are based on the US or the UK. Conversely, France is described as a "late bloomer" in this process (Gibson, 2020, 134). The French Presidential election campaign 2017 specifically has shown a limited emergence of technological businesses working in the field of big data politics (Ehrard, Bambade, Colin, 2019).
This contribution examines to what extent French digital campaign teams of 2017, supporting candidates Macron, Mélenchon, Fillon and Hamon for the Presidential election, as well as activists use Big Data techniques, and what are their understanding and interest for these techniques. The research is based on 45 individual in-depth interviews undertaken from 2018 to 2020, with campaign teams members (24 interviews) and digital activists (21 interviews) .The contribution show sthe diversity of what Big Data means in digital campaigning. It also includes better knowledge of "resistance" to Big Data. Resistance to Big Data in election campaigning is based on structural factors such as funding and legal conditions, that result in political and legal risks for candidates. Actors, whether they are professional or activists, also underline practicability factors, such as the difficulty of collecting reliable and numerous data, of using the tools or of developing databases in a limited amount of time. Discourses regarding digital activism and the coordination of online communities remain very present and powerful. Therefore, resistance is maybe less ideological than the result of deceptive experiences with Big Data techniques as well as scepticism regarding the potential results they provide.