The Library
Event construal and temporal distance in natural language
Tools
Tools
Tools
Bhatia, Sudeep and Walasek, Lukasz (2016) Event construal and temporal distance in natural language. Cognition, 152 . pp. 1-8. doi:10.1016/j.cognition.2016.03.011
|
PDF
WRAP_1-s2.0-S001002771630066X-main.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (825Kb) | Preview |
Official URL: http://dx.doi.org/10.1016/j.cognition.2016.03.011
Abstract
Construal level theory proposes that events that are temporally proximate are represented more concretely than events that are temporally distant. We tested this prediction using two large natural language text corpora. In study 1 we examined posts on Twitter that referenced the future, and found that tweets mentioning temporally proximate dates used more concrete words than those mentioning distant dates. In study 2 we obtained all New York Times articles that referenced U.S. presidential elections between 1987 and 2007. We found that the concreteness of the words in these articles increased with the temporal proximity to their corresponding election. Additionally the reduction in concreteness after the election was much greater than the increase in concreteness leading up to the election, though both changes in concreteness were well described by an exponential function. We replicated this finding with New York Times articles referencing US public holidays. Overall, our results provide strong support for the predictions of construal level theory, and additionally illustrate how large natural language datasets can be used to inform psychological theory.
| Item Type: | Journal Article | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| Subjects: | B Philosophy. Psychology. Religion > BF Psychology | ||||||||
| Divisions: | Faculty of Social Sciences > Warwick Business School > Behavioural Science Faculty of Social Sciences > Warwick Business School |
||||||||
| Library of Congress Subject Headings (LCSH): | Language and languages, Computational linguistics, Big data | ||||||||
| Journal or Publication Title: | Cognition | ||||||||
| Publisher: | Elsevier | ||||||||
| ISSN: | 0010-0277 | ||||||||
| Official Date: | July 2016 | ||||||||
| Dates: |
|
||||||||
| Volume: | 152 | ||||||||
| Number of Pages: | 8 | ||||||||
| Page Range: | pp. 1-8 | ||||||||
| DOI: | 10.1016/j.cognition.2016.03.011 | ||||||||
| Status: | Peer Reviewed | ||||||||
| Publication Status: | Published | ||||||||
| Access rights to Published version: | Open Access | ||||||||
| Funder: | Economic and Social Research Council (Great Britain) (ESRC), Leverhulme Trust (LT) | ||||||||
| Grant number: | ES/K002201/1 (ESRC), RP2012-V-022 (LT) |
Request changes or add full text files to a record
Repository staff actions (login required)
 |
View Item |
Downloads
Downloads per month over past year
