Impacts of Predictive Text on Writing Content

Abstract

People are increasingly communicating using digital technologies, especially using written language. Many modern text entry systems include features that offer predictive suggestions of words and phrases. These features have been designed and evaluated for the goal of making text entry more efficient. However, when these predictions are shown to writers to use, they also serve as suggestions of what content to write. This dissertation addresses two main questions: what effects do current predictive text system designs have on writing content, and what are some challenges and opportunities to designing the effects that predictive systems can have on writing content? The human-subjects studies conducted found evidence that the text that people write using predictive text entry systems reflects bias of these systems. Specifically, the presence of word suggestions decreased the degree to which writers chose the sorts of words that the system would not have expected in an image captioning task. Phrase suggestions resulted in even more predictable writing than word suggestions. Finally, phrase suggestions that were generated with a bias towards positive sentiment resulted in people writing more positive content, which implies that biases in training data could lead to biases in the content people create. Several pilot studies towards designing suggestions with different effects failed because writers viewed the experimental suggestions as irrelevant, so the dissertation presents work along two approaches towards enabling alternative suggestion designs. First, it discusses how to design suggestion systems that can adjust content attributes without overly compromising suggestion acceptability by applying an approach from reinforcement learning. Then it discusses how to design suggestions to guide writers about what topics to include, including a study comparing the perceived relevance of two designs.