A lot has been written in the public press about fake news and the apparently magic abilities of novel, data-driven technologies to manipulate voters’ beliefs and, hence, the outcomes of democratic elections. The problem is rooted in recent developments regarding the information available about voters, means to provide information to voters, and the nature of information acquisition by voters.
These developments concern (a) data-driven voter research and the possibility of political microtargeting, and (b) news consumption of growing numbers of people using social media and news aggregators that obfuscate the origin of news, leading to voter unawareness about the news sender’s identity. Academic empirical research on the topic is scarce but growing. Theoretical research, however, that carefully analyses the ability of political interest groups to spread messages via social media to voters in a credible way, is lacking.
In a new working paper, “Big Data and Democracy” (joint with Freek van Gils and Wieland Müller), we provide a theoretical framework in which we can analyze the effects that microtargeting by political interest groups and unawareness of voters about the political position of a news sender have on election outcomes in comparison to “conventional” news reporting. The model framework does not only offer new theoretical insights, it also allows the theory-based discussion of policy proposals, such as to ban microtargeting or to require news platforms to signal the political orientation of a news item’s originator.
P.S. This is the sister paper to “Competing with Big Data” (joint with Christoph Schottmüller), which has had and is having quite some policy impact. Where “Competing with Big Data” studies effects of datafication on competition on data-driven markets, “Big Data and Democracy” studies effects of datafication on democratic elections and the process of political opinion formation. In both papers, we identify the core problem and analyze solution proposals, trying to inform policy makers.