Discovering influential text using convolutional neural networks
This addresses the limitation of testing only pre-specified text treatments in social science experiments, offering a flexible approach for discovering influential text clusters, though it is incremental in combining existing techniques.
The authors tackled the problem of discovering text phrases that influence human reactions by connecting NLP interpretability with convolutional neural networks, resulting in a method that learns a greater variety of text treatments and meets or exceeds benchmark methods in predictive ability.
Experimental methods for estimating the impacts of text on human evaluation have been widely used in the social sciences. However, researchers in experimental settings are usually limited to testing a small number of pre-specified text treatments. While efforts to mine unstructured texts for features that causally affect outcomes have been ongoing in recent years, these models have primarily focused on the topics or specific words of text, which may not always be the mechanism of the effect. We connect these efforts with NLP interpretability techniques and present a method for flexibly discovering clusters of similar text phrases that are predictive of human reactions to texts using convolutional neural networks. When used in an experimental setting, this method can identify text treatments and their effects under certain assumptions. We apply the method to two datasets. The first enables direct validation of the model's ability to detect phrases known to cause the outcome. The second demonstrates its ability to flexibly discover text treatments with varying textual structures. In both cases, the model learns a greater variety of text treatments compared to benchmark methods, and these text features quantitatively meet or exceed the ability of benchmark methods to predict the outcome.