Age Prediction in Blogs: A Study of Style, Content, and Online Behavior in Pre- and Post-Social Media Generations

Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, pages 763–772, Portland, Oregon, June 19-24, 2011. c⃝2011 Association for Computational Linguistics Age Prediction in Blogs: A Study of Style, Content, and Online Behavior in Pre- and Post-Social Media Generations Sara Rosenthal Department of Computer Science Columbia University New York, NY 10027, USA sara@cs.columbia.edu Kathleen McKeown Department of Computer Science Columbia University New York, NY 10027, USA kathy@cs.columbia.edu Abstract We investigate whether wording, stylistic choices, and online behavior can be used to predict the age category of blog authors. Our hypothesis is that significant changes in writing style distinguish pre-social me- dia bloggers from post-social media blog- gers. Through experimentation with a range of years, we found that the birth dates of students in college at the time when social media such as AIM, SMS text messaging, MySpace and Facebook first became popular, enable accurate age pre- diction. We also show that internet writing characteristics are important features for age prediction, but that lexical content is also needed to produce significantly more accurate results. Our best results allow for 81.57% accuracy. 1 Introduction The evolution of the internet has changed the way that people communicate. The introduction of instant messaging, forums, social networking and blogs has made it possible for people of ev- ery age to become authors. The users of these social media platforms have created their own form of unstructured writing that is best char- acterized as informal. Even how people com- municate has dramatically changed, with multi- tasking increasing and responses generated im- mediately. We should be able to exploit those differences to automatically determine from blog posts whether an author is part of a pre- or post- social media generation. This problem is called age prediction and raises two main questions: • Is there a point in time that proves to be a significantly better dividing line between pre and post-social media generations? • What features of communication most di- rectly reveal the generation in which a blog- ger was born? We hypothesize that the dividing line(s) oc- cur when people in generation Y1, or the millen- nial generation, (born anywhere from the mid- 1970s to the early 2000s) were typical college- aged students (18-22). We focus on this gen- eration due to the rise of popular social media technologies such as messaging and online social networks sites that occurred during that time. Therefore, we experimented with binary clas- sification into age groups using all birth dates from 1975 through 1988, thus including students from generation Y who were in college during the emergence of social media technologies. We find five years where binary classification is sig- nificantly more accurate than other years: 1977, 1979, and 1982-1984. The appearance of social media technologies such as AOL Instant Messen- ger (AIM), weblogs, SMS text messaging, Face- book and MySpace occurred when people with these birth dates were in college. We explore two of these years in more detail, 1979 and 1984, and examine a wide …

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