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Extracting Human Temporal Orientation from Facebook Language

Schwartz, H. Andrew, Park, Gregory, Sap, Maarten, Weingarten, Evan, Eichstaedt, Johannes, Kern, Margaret, Stillwell, David, Kosinski, Michal, Berger, Jonah, Seligman, Martin, Ungar, Lyle·2015·Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
DOI 10.3115/v1/n15-1044
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Figure 1 Kernel density estimates of user-level proportions of past, present, and future classified messages, broken down by gender. Vertical bars represent means.
Figure 1 Kernel density estimates of user-level proportions of past, present, and future classified messages, broken down by gender. Vertical bars represent means.CC-BY · From the PDF
Figure 2 Standardized temporal orientation of users over their age. Shaded area indicates ± 1 standard error around loess smoothed estimates.
Figure 2 Standardized temporal orientation of users over their age. Shaded area indicates ± 1 standard error around loess smoothed estimates.CC-BY · From the PDF
Figure 3 Daily proportion of future-oriented messages leading up to and following birthdays. Younger men: < 23 , N = 423; Older men: ≥ 23 , N = 402.
Figure 3 Daily proportion of future-oriented messages leading up to and following birthdays. Younger men: < 23 , N = 423; Older men: ≥ 23 , N = 402.CC-BY · From the PDF

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Cites

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Metadata

Authors
Schwartz, H. A., Park, G., Sap, M., Weingarten, E., Eichstaedt, J., Kern, M., Stillwell, D., Kosinski, M., Berger, J., Seligman, M., & Ungar, L.
Venue
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Publisher
Association for Computational Linguistics
Published
2015
Pages
409-419
DOI
10.3115/v1/n15-1044
Cited by
13
References
10

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