When do Words Matter? Understanding the Impact of Lexical Choice on Audience Perception using Individual Treatment Effect Estimation
When do Words Matter? Understanding the Impact of Lexical Choice on Audience Perception using Individual Treatment Effect Estimation
复制标题
言语什么时候很重要？
DOI：
10.1609/aaai.v33i01.33017233
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发表时间：
2018
期刊：
影响因子：
--
通讯作者：
A. Culotta
中科院分区：
文献类型：
--
作者：
Zhao Wang;A. Culotta
跨学科的研究表明，词汇选择会影响观众的感知。例如，用户在社交媒体资料中如何描述自己会影响他们的社会经济地位。然而，我们缺乏一般的方法来估计词汇选择对特定句子感知的因果效应。虽然随机对照试验可能提供了很好的估计，但它们并没有扩展到考虑所有词汇选择所需的潜在数百万次比较。相反，在本文中，我们首先提供了两类方法来估计在给定的句子中将一个词改为另一个词对感知的影响。第一类算法建立在准实验设计的基础上，根据观察数据估计个体治疗效果。第二类将治疗效果估计视为分类问题。我们使用三个数据源（Yelp，Twitter和Airbnb）进行实验，发现算法估计值与随机对照试验产生的结果一致。此外，我们发现，它是可能的跨域转移治疗效果分类器，仍然保持高精度。
Studies across many disciplines have shown that lexical choice can affect audience perception. For example, how users describe themselves in a social media profile can affect their perceived socio-economic status. However, we lack general methods for estimating the causal effect of lexical choice on the perception of a specific sentence. While randomized controlled trials may provide good estimates, they do not scale to the potentially millions of comparisons necessary to consider all lexical choices. Instead, in this paper, we first offer two classes of methods to estimate the effect on perception of changing one word to another in a given sentence. The first class of algorithms builds upon quasi-experimental designs to estimate individual treatment effects from observational data. The second class treats treatment effect estimation as a classification problem. We conduct experiments with three data sources (Yelp, Twitter, and Airbnb), finding that the algorithmic estimates align well with those produced by randomized-control trials. Additionally, we find that it is possible to transfer treatment effect classifiers across domains and still maintain high accuracy.