Extracting Human Temporal Orientation from Facebook Language

Human Language Technologies: The 2015 Annual Conference of the North American Chapter of the ACL, pages 409–419, Denver, Colorado, May 31 – June 5, 2015. c⃝2015 Association for Computational Linguistics Extracting Human Temporal Orientation from Facebook Language H. Andrew Schwartz,1,4 Gregory J. Park,2 Maarten Sap,2 Evan Weingarten3, Johannes Eichstaedt,2 Margaret L. Kern,5 David Stillwell,6 Michal Kosinski,7 Jonah Berger,3 Martin Seligman,2 Lyle H. Ungar1 1Computer & Information Science, 2Psychology, 3Wharton, University of Pennsylvania 4Computer Science, Stony Brook University 6Psychometrics Centre, Cambridge University5Graduate School of Education, University of Melbourne 7Graduate School of Business, Stanford University hansens@seas.upenn.edu, gregpark@sas.upenn.edu Abstract People vary widely in their temporal orientation—how often they emphasize the past, present, and future—and this affects their finances, health, and happiness. Traditionally, temporal orientation has been assessed by self-report questionnaires. In this paper, we develop a novel behavior-based assessment using human language on Facebook. We first create a past, present, and future message classifier, engineering features and evaluating a variety of classification techniques. Our message classifier achieves an accuracy of 71.8%, compared with 52.8% from the most frequent class and 58.6% from a model based entirely on time expression features. We quantify a users’ overall temporal orientation based on their distribution of messages and validate it against known human correlates: conscientiousness, age, and gender. We then explore social scientific questions, finding novel associations with the factors openness to experience, satisfaction with life, depression, IQ, and one’s number of friends. Further, demonstrating how one can track orientation over time, we find differences in future orientation around birthdays. 1 Introduction How much one emphasizes the past, present, or fu- ture is predictive of many human factors such as oc- cupational and educational success, engagement in risky behavior, financial stability, depression, and health (Boyd and Zimbardo, 2005; Zimbardo and Boyd, 1999). However, studies on the human ex- perience of time are filled with diverse measurement methods (Strathman and Joireman, 2005), mostly in- volving questionnaires which are expensive to ad- minister multiple times or at scale and can be subject to confounds when compared to other questionnaire based assessments. Text mining and language processing techniques can provide a more objective and scalable measure- ment of temporal orientation, one’s tendency to em- phasize the past, present, or future. Whereas most prior computational linguistics and text mining tem- poral studies have focused on events, there has been a lack of work looking at the temporal orientation of people. Such measures, which were not practical before the growth of social media, can open many avenues of large-scale psychological discovery into the consequences of temporal orientation and yield applications such as targeted marketing, loan repay- ment forecasting, understanding economic patterns, or even quantified self-help tools to encourage more future-mindedness. In this paper, we develop a temporal orienta- tion measure based on language in social media. The measure uses a message-level classifier of past, present, and future, aggregated over users to create user-level assessments. We evaluate the message- level classifier over hand annotated data and the de- rived user-level model against known …

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