TY - GEN
T1 - Extracting human temporal orientation from Facebook language
AU - Schwartz, H. Andrew
AU - Park, Gregory J.
AU - Sap, Maarten
AU - Weingarten, Evan
AU - Eichstaedt, Johannes
AU - Kern, Margaret L.
AU - Stillwell, David
AU - Kosinski, Michal
AU - Berger, Jonah
AU - Seligman, Martin
AU - Ungar, Lyle H.
N1 - Publisher Copyright:
© 2015 Association for Computational Linguistics.
PY - 2015
Y1 - 2015
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84960119692&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84960119692&partnerID=8YFLogxK
U2 - 10.3115/v1/n15-1044
DO - 10.3115/v1/n15-1044
M3 - Conference contribution
AN - SCOPUS:84960119692
T3 - NAACL HLT 2015 - 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Conference
SP - 409
EP - 419
BT - NAACL HLT 2015 - 2015 Conference of the North American Chapter of the Association for Computational Linguistics
PB - Association for Computational Linguistics (ACL)
T2 - Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2015
Y2 - 31 May 2015 through 5 June 2015
ER -