Developing Age and Gender Predictive Lexica over Social Media

Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1146–1151, October 25-29, 2014, Doha, Qatar. c⃝2014 Association for Computational Linguistics Developing Age and Gender Predictive Lexica over Social Media Maarten Sap1 Gregory Park1 Johannes C. Eichstaedt1 Margaret L. Kern1 David Stillwell3 Michal Kosinski3 Lyle H. Ungar2 and H. Andrew Schwartz2 1Department of Psychology, University of Pennsylvania 2Computer & Information Science, University of Pennsylvania 3Psychometrics Centre, University of Cambridge maarten@sas.upenn.edu Abstract Demographic lexica have potential for widespread use in social science, economic, and business applications. We derive predic- tive lexica (words and weights) for age and gender using regression and classification models from word usage in Facebook, blog, and Twitter data with associated demographic labels. The lexica, made publicly available,1 achieved state-of-the-art accuracy in language based age and gender prediction over Face- book and Twitter, and were evaluated for generalization across social media genres as well as in limited message situations. 1 Introduction Use of social media has enabled the study of psycho- logical and social questions at an unprecedented scale (Lazer et al., 2009). This allows more data-driven dis- covery alongside the typical hypothesis-testing social science process (Schwartz et al., 2013b). Social me- dia may track disease rates (Paul and Dredze, 2011; Google, 2014), psychological well-being (Dodds et al., 2011; De Choudhury et al., 2013; Schwartz et al., 2013a), and a host of other behavioral, psychological and medical phenomena (Kosinski et al., 2013). Unlike traditional hypothesis-driven social science, such large-scale social media studies rarely take into account—or have access to—age and gender informa- tion, which can have a major impact on many ques- tions. For example, females live almost five years longer than males (cdc, 2014; Marengoni et al., 2011). Men and women, on average, differ markedly in their interests and work preferences (Su et al., 2009). With age, personalities gradually change, typically becom- ing less open to experiences but more agreeable and conscientious (McCrae et al., 1999). Additionally, so- cial media language varies by age (Kern et al., 2014; Pennebaker and Stone, 2003) and gender (Huffaker and Calvert, 2005). Twitter may have a male bias (Mislove et al., 2011), while social media in general skew to- wards being young and female (pew, 2014). Accessible tools to predict demographic variables can substantially enhance social media’s utility for so- 1download at http://www.wwbp.org/data.html cial science, economic, and business applications. For example, one can post-stratify population-level results to reflect a representative sample, understand variation across age and gender groups, or produce personalized marketing, services, and sentiment recommendations; a movie may be generally disliked, except by people in a certain age group, whereas a product might be used primarily by one gender. This paper describes the creation of age and gen- der predictive lexica from a dataset of Facebook users who agreed to share their status updates and reported their age and gender. The lexica, in the form of words with associated weights, are derived from a penalized linear regression (for continuous valued age) and sup- port vector classification …

Content truncated to 500 words