What makes you change your mind? An empirical investigation in online group decision-making conversations
This work addresses a specific challenge in understanding conversational dynamics for researchers in computational social science or NLP, but it is incremental as it builds on existing datasets and methods.
The paper tackled the problem of detecting what causes individuals to change their minds in online group decision-making conversations, finding that a language-aware model with learning-to-rank training is the best approach, though the task remains non-trivial.
People leverage group discussions to collaborate in order to solve complex tasks, e.g. in project meetings or hiring panels. By doing so, they engage in a variety of conversational strategies where they try to convince each other of the best approach and ultimately reach a decision. In this work, we investigate methods for detecting what makes someone change their mind. To this end, we leverage a recently introduced dataset containing group discussions of people collaborating to solve a task. To find out what makes someone change their mind, we incorporate various techniques such as neural text classification and language-agnostic change point detection. Evaluation of these methods shows that while the task is not trivial, the best way to approach it is using a language-aware model with learning-to-rank training. Finally, we examine the cues that the models develop as indicative of the cause of a change of mind.