Trusting tech firms’ big data for political microtargeting? A qualitative analysis of parties’ communication managers risk and trust perceptions

Abstract

ABSTRACT Parties use political microtargeting (PMT) to address voter subsegments individually. Due to limited resources and legal restrictions, parties often rely on Meta’s platforms and advertising ecosystem for PMT. However, using these external infrastructures and big data analyses might be risky for parties, since big data are criticized for their validity, robustness, representativeness, and usefulness. A mechanism to tolerate risks is trust. With the theoretical background of trust in technology, this study investigates the extent to which parties’ communication managers perceive risks in their strategic use of PMT on Meta’s platforms and how they evaluate big data’s trustworthiness. Based on in-depth expert interviews with German parties’ communication managers on state level, the results show that parties’ communication managers perceive various risks in relying on Meta’s platforms and are ambivalent about PMT’s big data analytics in terms of quality and reliability. To minimize risk perceptions, parties adopt strategies such as inputting their own data or creating own target audiences. Parties’ risk perceptions only partially influence their trust in big data and Meta. Despite varying degrees of trust, PMT is still used in campaigns as it has become too common and necessary to compete with other parties.