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Political Parties and Microtargeted Election Advertising: Testing the Effectiveness of Personality Congruent Political Advertising.

Elections
Political Parties
Political Psychology
Advertising
Campaign
Communication
Electoral Behaviour
Scott Pruysers
Dalhousie University
Julie Blais
Dalhousie University
Scott Pruysers
Dalhousie University

Abstract

There is a growing concern that political parties have the ability to engage in highly sophisticated microtargeting based on ‘psychographics’ (deeply personal and psychological characteristics such as personality traits). These fears became highly publicized after Cambridge Analytica, a political consulting firm, proudly boasted that it had some 5,000 data points on every American and that this data allowed the firm to target individuals with specific political advertisements based on their underlying personality profile. Before we can (normatively) evaluate the harm that psychographic microtargeting may entail, we must first empirically examine whether political advertising can indeed be tailored in such a manner as to resonate with people at the level of their personality. Utilizing real-world personality tailored advertisements produced by Cambridge Analytica for a US midterm election, this paper explores the effectiveness of such election campaign advertising. Study 1 draws on a survey of experts in personality psychology. Here we consider the face validity of the advertisements (i.e., are experts able to correctly identify the trait being targeted) as well as experts’ views about the efficacy and potential harm of psychological microtargeting during elections. Study 2 then draws upon an original between-subjects experimental design with a nationally representative sample of over 2,000 US citizens where participants were randomly assigned to view one of the ads. Here we consider whether advertisements that match participants’ personality traits are more effective (i.e., vote intention, likability, etc.). Analyses and hypotheses for Study 2 were pre-registered to the Open Science Framework (OSF) prior to date collection (https://osf.io/5wpyt/).