CoChat
Sign Up
Cited by 13

The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.

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
Open this paper in CoChat — free account

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.3115/v1/n15-1044

Cites

10 of 10 references verified0 citation errors · 0 not checkable

Statistics

Figures

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

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 10 references

Research works printed but not linked: 39

  1. Jean Adams and Daniel Nettle. 2009. Time perspective, per- sonality and smoking, body mass, and physical activity: An empirical study. British Journal of Health Psychology, 14(1):83-105.
  2. James F Allen. 1983. Maintaining knowledge about temporal intervals. Communications of the ACM, 26(11):832-843.
  3. Roy F Baumeister, Kathleen D Vohs, and David C Funder. 2007. Psychology as the science of self-reports and finger movements: Whatever happened to actual behavior? Per- spectives on Psychological Science, 2(4):396-403.
  4. Shane Bergsma, Mark Dredze, Benjamin Van Durme, Theresa Wilson, and David Yarowsky. 2013. Broadly improving user classification via communication-based name and lo- cation clustering on twitter. In Anuual Meeting of HLT- NAACL, pages 1010-1019.
  5. Christopher M Bishop. 2006. Pattern recognition and machine learning, volume 4. Springer New York.
  6. John N Boyd and Philip G Zimbardo. 2005. Time perspective, health, and risk taking. In Understanding behavior in the context of time, pages 85-107.
  7. John D Burger, John Henderson, George Kim, and Guido Zarrella. 2011. Discriminating gender on twitter. In Pro- ceedings of the Conference on Empirical Methods in Natu- ral Language Processing, pages 1301-1309. Association for Computational Linguistics.
  8. Yi Chang, Xuanhui Wang, Qiaozhu Mei, and Yan Liu. 2013. Towards twitter context summarization with user influence models. In Proceedings of the sixth ACM international con- ference on Web search and data mining, pages 527-536. ACM.
  9. Leon Derczynski, Diana Maynard, Niraj Aswani, and Kalina Bontcheva. 2013. Microblog-genre noise and impact on semantic annotation accuracy. In Proceedings of the 24th ACM Conference on Hypertext and Social Media, pages 21- 30. ACM.
  10. Fien Desmyter and Rudi De Raedt. 2012. The relationship between time perspective and subjective well-being of older adults. Psychologica Belgica, 52(1):19-38.
  11. ED Diener, Robert A Emmons, Randy J Larsen, and Sharon Griffin. 1985. The satisfaction with life scale. Journal of personality assessment, 49(1):71-75.
  12. Ed Diener, Louis Tay, and Shigehiro Oishi. 2013. Rising in- come and the subjective well-being of nations. Journal of Personality and Social Psychology, 104(2):267-276.
  13. Rong-En Fan, Kai-Wei Chang, Cho-Jui Hsieh, Xiang-Rui Wang, and Chih-Jen Lin. 2008. LIBLINEAR: A library for large linear classification. Journal of Machine Learning Research, 9:1871-1874.
  14. Wei Feng and Jianyong Wang. 2013. Retweet or not?: person- alized tweet re-ranking. In Proceedings of the sixth ACM international conference on Web search and data mining, pages 577-586. ACM.
  15. William J Friedman. 2000. The development of children's knowledge of the times of future events. Child Development, 71(4):913-932.
  16. Pierre Geurts, Damien Ernst, and Louis Wehenkel. 2006. Ex- tremely randomized trees. Machine learning, 63(1):3-42.
  17. Lewis R Goldberg. 1990. An alternative" description of per- sonality": the big-five factor structure. Journal of Personal- ity and Social Ssychology, 59(6):1216-1229.
  18. Sumit Goswami, Sudeshna Sarkar, and Mayur Rustagi. 2009. Stylometric analysis of bloggers age and gender. In Third International AAAI Conference on Weblogs and Social Me- dia.
  19. Pedro Calais Guerra, Wagner Meira Jr, and Claire Cardie. 2014. Sentiment analysis on evolving social streams: How self- report imbalances can help. In Proceedings of the 7th ACM international conference on Web search and data mining, pages 443-452. ACM.
  20. Kelli A Keough, Philip G Zimbardo, and John N Boyd. 1999. Who's smoking, drinking, and using drugs? Time perspec- tive as a predictor of substance use. Basic and Applied Social Psychology, 21(2):149-164.
  21. Kurt Lewin. 1942. Time perspective and morale. In Resolving social conflicts and Field theory in social science, pages 80- 93. APA, Washington, D. C. Nira Liberman, Yaacov Trope, Sean M McCrea, and Steven J Sherman. 2007. The effect of level of construal on the tem- poral distance of activity enactment. Journal of Experimen- tal Social Psychology, 43(1):143-149.
  22. Xiao Ling and Daniel S Weld. 2010. Temporal information extraction. In Proceedings of the 24th AAAI Conference on Artificial Intelligence.
  23. F. Mairesse, M.A. Walker, M.R. Mehl, and R.K. Moore. 2007. Using linguistic cues for the automatic recognition of per- sonality in conversation and text. Journal of Artificial Intel- ligence Research, 30(1):457-500.
  24. Robert R McCrae and O P John. 1992. An introduction to the five-factor model and its applications. Journal of Personal- ity, 60(2):175-215.
  25. In Alan Strathman and Jeff Joireman, editors, Understand- ing behavior in the context of time, pages 31-57.
  26. Rina Panigrahy, Marc Najork, and Yinglian Xie. 2012. How user behavior is related to social affinity. In Proceedings of the fifth ACM international conference on Web search and data mining, pages 713-722. ACM.
  27. Delroy L Paulhus and Simine Vazire. 2007. The self-report method. Handbook of research methods in personality psy- chology, pages 224-239.
  28. Daniele Quercia, Renaud Lambiotte, David Stillwell, Michal Kosinski, and Jon Crowcroft. 2012. The personality of pop- ular facebook users. In Proceedings of the ACM 2012 con- ference on Computer Supported Cooperative Work, pages 955-964. ACM.
  29. Lenore Sawyer Radloff. 1977. The ces-d scale a self-report depression scale for research in the general population. Ap- plied psychological measurement, 1(3):385-401.
  30. Maarten Sap, Gregory Park, Johannes C. Eichstaedt, Mar- garet L. Kern, David J. Stillwell, Michal Kosinski, Lyle H. Ungar, and H. Andrew Schwartz. 2014. Developing age and gender predictive lexica over social media. In EMNLP 2014, pages 1146-1151. Association for Computational Lin- guistics.
  31. Martin EP Seligman, Peter Railton, Roy F Baumeister, and Chandra Sripada. 2013. Navigating into the future or driven by the past. Perspectives on Psychological Science, 8(2):119-141.
  32. Partha Pratim Talukdar, Derry Wijaya, and Tom Mitchell. 2012. Coupled temporal scoping of relational facts. In Proceed- ings of the fifth ACM international conference on Web search and data mining, pages 73-82. ACM.
  33. Kristina Toutanova, Dan Klein, Christopher D Manning, and Yoram Singer. 2003. Feature-rich part-of-speech tagging with a cyclic dependency network. In Proceedings of the 2003 Conference of the North American Chapter of the Asso- ciation for Computational Linguistics on Human Language Technology-Volume 1, pages 173-180. Association for Com- putational Linguistics.
  34. Naushad UzZaman, Hector Llorens, Leon Derczynski, Marc Verhagen, James Allen, and James Pustejovsky. 2013. SemEval-2013 task 1: TempEval-3: Evaluating time expres- sions, events, and temporal relations. In Proceedings of the 7th International Workshop on Semantic Evaluation.
  35. Marc Verhagen, Inderjeet Mani, Roser Sauri, Robert Knip- pen, Seok Bae Jang, Jessica Littman, Anna Rumshisky, John Phillips, and James Pustejovsky. 2005. Automating temporal annotation with TARSQI. In Proceedings of the ACL 2005 on interactive poster and demonstration sessions, pages 81-84. Association for Computational Linguistics.
  36. Marc Verhagen, Robert Gaizauskas, Frank Schilder, Mark Hep- ple, Graham Katz, and James Pustejovsky. 2007. Semeval- 2007 task 15: Tempeval temporal relation identification. In Proceedings of the 4th International Workshop on Seman- tic Evaluations, pages 75-80. Association for Computational Linguistics.
  37. Marc Verhagen, Roser Sauri, Tommaso Caselli, and James Pustejovsky. 2010. SemEval-2010 task 13: TempEval-2. In Proceedings of the 5th International Workshop on Seman- tic Evaluation, pages 57-62. Association for Computational Linguistics.
  38. Paul Webley and Ellen K Nyhus. 2006. Parents influence on childrens future orientation and saving. Journal of Economic Psychology, 27(1):140-164.
  39. Miaomiao Wen, Zeyu Zheng, Hyeju Jang, Guang Xiang, and Carolyn Penstein Rosé. 2013. Extracting events with infor- mal temporal references in personal histories in online com- munities. In Proceedings of 51st annual meeting of the ACL, pages 836-842.

Cited by

Sorted by citations

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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review