What makes you change your mind? An empirical investigation in online group decision-making conversations

TL;DR

This paper investigates the factors that lead individuals to change their minds during online group decision-making conversations, using neural models and change point detection on a specialized dataset.

Contribution

It introduces a novel approach combining neural text classification and change point detection to identify reasons for opinion change in group discussions.

Findings

Language-aware models outperform others in detecting mind changes.

Learning-to-rank training improves detection accuracy.

Models reveal cues indicative of reasons for opinion shifts.

Abstract

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.