Extracting Human Temporal Orientation from Facebook Language

By H. A. Schwartz, Gregory J. Park, Maarten Sap, Evan Weingarten, Johannes C. Eichstaedt, Margaret L. Kern, David J. Stillwell, Michal Kosinski, Jonah A. Berger, Martin E. P. Seligman and 1 more. 2015

34 citations recorded by the source corpus.

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.

Subfields: Experimental and Cognitive Psychology, Literature and Literary Theory, Sociology and Political Science, Natural Language Processing, Social Sciences & Humanities

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Experimental and Cognitive Psychology

In Literature and Literary Theory

In Sociology and Political Science

In Natural Language Processing

In Social Sciences & Humanities

Citations in the corpus

Recollection versus Imagination: Exploring Human Memory and Cognition via Neural Language Models (2020, methodology): “Such approaches have been used in prior efforts to investigate author mental states, temporal orientation, or counterfactual thinking in language (Tausczik and Pennebaker, 2010; Schwartz et al., 2015; Son et al., 2017).”
The role of personality, age, and gender in tweeting about mental illness (2015, background): “…in order to predict different user traits such as: age (Nguyen et al., 2011), gender (Sap et al., 2014), location (Cheng et al., 2010), impact (Lampos et al., 2014), political preference (Volkova et al., 2014), temporal orientation (Schwartz et al., 2015) or personality (Schwartz et al., 2013).”