Bhatia Sudeep, Walasek Lukasz
Department of Psychology, University of Pennsylvania, Philadelphia, PA, USA.
Department of Psychology and Behavioral Science Group at the Warwick Business School, University of Warwick, Coventry, United Kingdom.
Cognition. 2016 Jul;152:1-8. doi: 10.1016/j.cognition.2016.03.011. Epub 2016 Mar 22.
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.
解释水平理论提出，时间上接近的事件比时间上遥远的事件得到更具体的表征。我们使用两个大型自然语言文本语料库对这一预测进行了检验。在研究1中，我们检查了推特上提及未来的帖子，发现提及时间上接近日期的推文比提及遥远日期的推文使用了更多具体的词汇。在研究2中，我们获取了1987年至2007年间所有提及美国总统选举的《纽约时报》文章。我们发现，这些文章中词汇的具体程度随着与相应选举时间的接近而增加。此外，选举后具体程度的下降远大于选举前具体程度的增加，不过具体程度的这两种变化都能很好地用指数函数来描述。我们用提及美国公共假日的《纽约时报》文章重复了这一发现。总体而言，我们的结果为解释水平理论的预测提供了有力支持，此外还说明了大型自然语言数据集可如何用于为心理学理论提供信息。
