Psychological targeting as an effective approach to digital mass persuasion

Significance Building on recent advancements in the assessment of psychological traits from digital footprints, this paper demonstrates the effectiveness of psychological mass persuasion—that is, the adaptation of persuasive appeals to the psychological characteristics of large groups of individuals with the goal of influencing their behavior. On the one hand, this form of psychological mass persuasion could be used to help people make better decisions and lead healthier and happier lives. On the other hand, it could be used to covertly exploit weaknesses in their character and persuade them to take action against their own best interest, highlighting the potential need for policy interventions. People are exposed to persuasive communication across many different contexts: Governments, companies, and political parties use persuasive appeals to encourage people to eat healthier, purchase a particular product, or vote for a specific candidate. Laboratory studies show that such persuasive appeals are more effective in influencing behavior when they are tailored to individuals’ unique psychological characteristics. However, the investigation of large-scale psychological persuasion in the real world has been hindered by the questionnaire-based nature of psychological assessment. Recent research, however, shows that people’s psychological characteristics can be accurately predicted from their digital footprints, such as their Facebook Likes or Tweets. Capitalizing on this form of psychological assessment from digital footprints, we test the effects of psychological persuasion on people’s actual behavior in an ecologically valid setting. In three field experiments that reached over 3.5 million individuals with psychologically tailored advertising, we find that matching the content of persuasive appeals to individuals’ psychological characteristics significantly altered their behavior as measured by clicks and purchases. Persuasive appeals that were matched to people’s extraversion or openness-to-experience level resulted in up to 40% more clicks and up to 50% more purchases than their mismatching or unpersonalized counterparts. Our findings suggest that the application of psychological targeting makes it possible to influence the behavior of large groups of people by tailoring persuasive appeals to the psychological needs of the target audiences. We discuss both the potential benefits of this method for helping individuals make better decisions and the potential pitfalls related to manipulation and privacy.

[1]  P. M. Podsakoff,et al.  Self-Reports in Organizational Research: Problems and Prospects , 1986 .

[2]  Delroy L. Paulhus,et al.  Enhancement and Denial in Socially Desirable Responding , 1991 .

[3]  R. McCrae,et al.  An introduction to the five-factor model and its applications. , 1992, Journal of personality.

[4]  Prashanth U. Nyer,et al.  The role of emotions in marketing , 1999 .

[5]  Roddy Cowie,et al.  FEELTRACE: an instrument for recording perceived emotion in real time , 2000 .

[6]  Youngme Moon,et al.  Personalization and Personality: Some Effects of Customizing Message Style Based on Consumer Personality , 2002 .

[7]  C. Ai,et al.  Interaction terms in logit and probit models , 2003 .

[8]  E. Higgins,et al.  Regulatory fit and persuasion: transfer from "Feeling Right.". , 2004, Journal of personality and social psychology.

[9]  Traci Mann,et al.  Dispositional motivations and message framing: a test of the congruency hypothesis in college students. , 2004, Health psychology : official journal of the Division of Health Psychology, American Psychological Association.

[10]  George Y. Bizer,et al.  Self‐Schema Matching and Attitude Change: Situational and Dispositional Determinants of Message Elaboration , 2005 .

[11]  Bernd Marcus,et al.  Personality in cyberspace: personal Web sites as media for personality expressions and impressions. , 2006, Journal of personality and social psychology.

[12]  John A. List,et al.  What Do Laboratory Experiments Tell Us About the Real World , 2006 .

[13]  John A. Johnson,et al.  The international personality item pool and the future of public-domain personality measures ☆ , 2006 .

[14]  P. Costa,et al.  Pathological gambling and the five-factor model of personality ☆ , 2007 .

[15]  S. Noar,et al.  Does tailoring matter? Meta-analytic review of tailored print health behavior change interventions. , 2007, Psychological bulletin.

[16]  Steven D. Levitt,et al.  What Do Laboratory Experiments Measuring Social Preferences Reveal About the Real World , 2007 .

[17]  Abigail A. Scholer,et al.  Regulatory Fit and Persuasion: Basic Principles and Remaining Questions , 2008 .

[18]  John R. Hauser,et al.  Website Morphing , 2009, Mark. Sci..

[19]  A. Pentland,et al.  Computational Social Science , 2009, Science.

[20]  A. Pentland,et al.  Life in the network: The coming age of computational social science: Science , 2009 .

[21]  Tal Yarkoni Personality in 100,000 Words: A large-scale analysis of personality and word use among bloggers. , 2010, Journal of research in personality.

[22]  Jennifer Golbeck,et al.  Predicting Personality from Twitter , 2011, 2011 IEEE Third Int'l Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third Int'l Conference on Social Computing.

[23]  R. Pieters,et al.  Emotion-Induced Engagement in Internet Video Advertisements , 2012 .

[24]  Yunxin Liu,et al.  MoodScope: building a mood sensor from smartphone usage patterns , 2013, MobiSys.

[25]  Margaret L. Kern,et al.  Personality, Gender, and Age in the Language of Social Media: The Open-Vocabulary Approach , 2013, PloS one.

[26]  T. Graepel,et al.  Private traits and attributes are predictable from digital records of human behavior , 2013, Proceedings of the National Academy of Sciences.

[27]  Gregory J. Park,et al.  Automatic personality assessment through social media language. , 2015, Journal of personality and social psychology.

[28]  S. Gosling,et al.  Facebook as a research tool for the social sciences: Opportunities, challenges, ethical considerations, and practical guidelines. , 2015, The American psychologist.

[29]  M. Kosinski,et al.  Computer-based personality judgments are more accurate than those made by humans , 2015, Proceedings of the National Academy of Sciences.

[30]  Adam D. Galinsky,et al.  Dynamics of Communicator and Audience Power: The Persuasiveness of Competence versus Warmth , 2016 .

[31]  Alessandro Perina,et al.  The Pictures We Like Are Our Image: Continuous Mapping of Favorite Pictures into Self-Assessed and Attributed Personality Traits , 2017, IEEE Transactions on Affective Computing.