We Are What We Watch: Movie Plots Predict the Personalities of Those who “Like” Them Gideon Nave Marketing Department, The Wharton School, University of Pennsylvania 3730 Walnut St., Philadelphia PA 19104, USA Tel: +1 (215) 898 8248 Email: [email protected] Jason Rentfrow Social & Developmental Psychology, University of Cambridge Free School Lane, Cambridge, Cambridgeshire, CB2 3RQ, UK Tel: +44 (0)1223 (7) 67805 E-mail: [email protected] Sudeep Bhatia Psychology Department, University of Pennsylvania 3720 Walnut Street, Philadelphia PA 19104, USA Tel: +1 (215) 898 5096 Email: [email protected] Acknowledgments We thank Dylan Manfredi for research assistance. We thank Eric Bradlow, Joshua Eliashberg, Nadja C. Furtner, and Olivier Toubia for their comments on earlier versions of the manuscript. Gideon Nave thanks Carlos and Rosa de la Cruz and the Wharton School of Business Dean’s Research Fund for ongoing support. Sudeep Bhatia received funding from the National Science Foundation grant SES-1847794 ABSTRACT The proliferation of media streaming services has increased the volume of personalized video consumption, allowing marketers to reach massive audiences and deliver a range of customized content at scale. However, relatively little is known about how consumers’ psychological makeup is manifested in their media preferences. The present paper addresses this gap in a pre- registered study of the relationship between movie plots, quantified via user-generated keywords, and the aggregate personality profiles of those who “like” them on social media. We find that movie plots can be used to accurately predict aggregate fans’ personalities, above and beyond the demographic characteristics of fans, and general film characteristics such as quality, popularity, and genre. Further analysis reveals various associations between the movies’ psychological themes and their fans’ personalities, indicating congruence between the two. For example, films with keywords related to anxiety are liked more among people who are high in Neuroticism and low in Extraversion. In contrast, angry and violent movies are liked more by people who are low in Agreeableness. Our findings provide a fine-grained mapping between personality dimensions and preferences for media content, and demonstrate how these links can be leveraged for assessing audience psychographics at scale. Keywords: personality, media preferences, psychological assessment, text analysis, machine learning. INTRODUCTION With the growing prevalence of on-demand streaming services, the variety of video content available for personalized consumption is rapidly increasing. As of 2020, the streaming service Netflix alone had 182 million users, who watched over one billion hours of video content in a given week (Watson 2020). Despite the ubiquity of personalized video consumption, and even though marketers have long recognized the importance of understanding the psychological characteristics of media audiences (Eliashberg, Elberse, and Leenders 2006; Toubia et al. 2019; Wells 1975; Beane and Ennis 1987; Weinstein 1994), relatively little is known about the associations between movie preferences and individual differences in psychological traits. Furthermore, the capacity to measure the psychographics associated with enjoyment of specific content has remained limited by the survey-based nature of psychological assessment. The current work aims to address the above gaps in a pre-registered investigation of the links between movie plots and the psychological characteristics of the people who like them on Facebook. Our study focuses on the Big Five personality dimensions (Openness to experience, Conscientiousness, Extraversion, Agreeableness and Neuroticism), which are indicative of many aspects of consumer behavior—including spending habits (Matz, Gladstone, and Stillwell 2016; Gladstone, Matz, and Lemaire 2019; Brown and Taylor 2014; Holbrook and Olney 1995), preferences for brands (Huang, Mitchell, and Rosenaum-Elliott 2012; Lin 2010), responsiveness to ads (Matz et al. 2017; Hirsh, Kang, and Bodenhausen 2012), brand loyalty (Lin 2010), and word of mouth activity (Mooradian and Swan 2006). We systematically map the relationships between the Big Five and movie preferences, and leverage these associations to develop a scalable, automated method for assessing the psychographics related to liking of distinct content. What Do We Know about Personality and Media Preferences? For decades, media researchers have developed and tested theories to explain the appeal of particular content. According to Uses and Gratifications Theory, individuals are drawn to media that satisfies their psychological and social needs (Katz, Blumler, and Gurevitch 1973; McGuire 1974; Rosengren 1974; Palmgreen, Wenner, and Rosengren 1985; Ruggiero 2000). One prediction of the theory is that individuals prefer media content that converges with their personalities. Empirical tests of this prediction have shown correlations between the Big Five traits and self-reported time devoted to and pleasure obtained from consuming different media types. For example, individuals high in Openness tend to enjoy reading books and spend less time watching TV (Finn 1997), in line with the trait’s association with a greater need for cognition (Dollinger 2003). Within social-personality psychology, some researchers have further explored the personality correlates of preferences for specific content types (e.g., Rentfrow, Goldberg, and Zilca 2011; Weaver 1991, 2003; Weaver, Brosius, and Mundorf 1993; Möller and Karppinen 1983; Golbeck and Norris 2013). For example, Rentfrow and colleagues investigated associations between personality and preferences for film, music, television, and books. They revealed distinct preference dimensions defined in terms of similar genres (e.g., horror, thriller, heavy metal) as opposed to medium (film, television, music). Furthermore, individual differences in media preference dimensions appear to correlate with personality traits. For instance, preferences for “cerebral” media genres were associated with Openness, and preferences for “light-hearted” media content were linked to Extraversion and Agreeableness. Limitations of Current Knowledge Despite these advances, several shortcomings of previous research restrict our understanding of the links between personality and preferences for media content and limit the capacity to translate past findings into actionable marketing strategies. One limitation concerns how media preferences have been assessed—typically via self-reported liking of a small number of broad genre categories (e.g., Hirschman 1985; Rentfrow, Goldberg, and Zilca 2011; Kraaykamp and Eijck 2005; Möller and Karppinen 1983).1 In other cases, titles of specific movies, books, and TV shows that exemplify genres have also been used (Weaver 1991). Although genres have yielded consistent findings across studies, reliance on self-reported genre preferences as the unit of analysis has important drawbacks. First, genres are very broad categories, and there is no consensus about how many and which genres should be represented. Some researchers have used as few as six categories (Weaver 1991) and others as many as eighteen (Rentfrow, Goldberg, and Zilca 2011). Moreover, the correspondence between genres and actual media content is often not straightforward. For example, the Academy-Award winning film The Shape of Water (2017) was classified by the Internet Movie Database (IMDb) as belonging to no less than five different genres: Adventure, Drama, Fantasy, Romance, and Thriller.2 Thus, because of their broad nature, genres can be difficult to interpret and yield imprecise preference assessments. Second, media content is complex and multidimensional. Reducing such content into a few broadly defined genre classifications might eliminate important and potentially valuable information. For example, the films Good Morning Vietnam (1987) and The Big Short (2015) belong to the same three genre categories (Biography, Comedy, and Drama), according to IMDb. However, the former tells the story of an irreverent DJ broadcasting on the US Armed Forces Radio station during the Vietnam War. In contrast, the latter is about a group of investors betting against the housing market before the 2008 financial crisis. Relying solely on genre categories fails to account for substantial differences between these films' content and precludes the possibility of capturing more nuanced information. Of note, practitioners in the entertainment industry recognize that genre-based classification is incomplete. For example, Netflix uses no less than 76,897 “micro-genre” categories, including esoteric labels such as “Scary Cult Movies from the 1980s” and “Visually-striking foreign nostalgic dramas” (Madrigal 2014). Content recommendation systems, as well as recent choice-models of media consumption in the marketing literature, have also represented media content using much richer sets of attributes (Toubia et al. 2019; Jung and Lee 2004; Melville, Mooney, and Nagarajan 2002; Eliashberg, Hui, and Zhang 2014) that are typically derived via automated text analysis (Berger et al. 2020; Humphreys and Wang 2017). Third, the broadness of genres makes it difficult to assess more general content attributes, such as quality and popularity. Consequently, previous studies have not examined the impact that such characteristics may have on preferences. For example, it may be the case that extroverts have strong preferences for media content that is more