Making Pairs That Cooperate: Chatbot Assessment of Receptiveness in Human Conversations

Human biases toward interacting with similar individuals contribute to the formation of ideological echo chambers and erode trust in others. However, facilitating constructive discussions can potentially counteract these biases and build trust, even when a consensus is not reached. This work explores such potential by developing an algorithmic assessment of linguistic features that promote trust and examining the impact of strategically pairing individuals based on this assessment. Guided by the conversational grounding theory, we first analyze the linguistic features of 123 interpersonal dyadic discussions (2,809 messages) on our online chat system and develop a classifier to identify individuals who use the communication style that promotes trust development. We then conduct a randomized controlled experiment with 530 human subjects in 265 pairs to measure the effect of assigning discussion partners based on the classifier’s assessment of participants' prior interactions with a chatbot. Our results show that algorithmically assigned pairs exhibit higher trust in their conversation partners than random pairs, irrespective of opinion similarity. We discuss the implications of our strategic pairing approach for enhancing collaboration and trust in various social settings.