Event construal and temporal distance in natural language

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• 作者：Sudeep Bhatia^a ; ^{bhatiasu@sas.upenn.edu" class="auth_mail" title="E-mail the corresponding author} ; Lukasz Walasek^b ; ^{L.Walasek@warwick.ac.uk" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Construal level theory ; Psychological distance ; Natural language ; Big data
• 刊名：Cognition
• 出版年：2016
• 出版时间：July 2016
• 年：2016
• 卷：152
• 期：Complete
• 页码：1-8
• 全文大小：628 K

文摘

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.