Predicting Voters: The Significant Role of Personal Data for Political Communication in Switzerland’s Social Networks
Social networks like Facebook, Twitter, and many others are becoming increasingly important for political communication. Simultaneously, the misuse of personal data is of rising concern for many policymakers. Personal data collected from social networks offer political actors the possibility to predict the behavior of their potential voters. Methods of psychology are used for target group segmentation and are the basis for persuasive political adverts (Micro-Targeting). Cambridge Analytica used Micro-Targeting during the US elections in 2016 and it is assumed to have contributed to Trump's election victory. However, studies that examine the significance of personal data for political communication in Switzerland are rare to find. Although the elections in October 2019 showed clearly that Swiss parties increasingly use personal data for their campaigns. During my studies, I examined the political significance of digitization and personal data in particular. I developed a research plan that aims at investigating how Swiss parties collect personal data to predict attitudes, motivations, and behaviors from potential voters, how this influences the design, message, and target of their advertisement, and with whom they collaborate in this process. I submitted my project for funding to conduct a first one-year preliminary study to develop basic findings for an extensive four-year study on the topic. Beyond this, I developed a series of experimental tools entitled Scripted Loopholes that investigate the topic from a technological perspective. The ongoing series is accessible on the project website and is exhibited occasionally in the context of media art exhibitions.