Conversational receptiveness: Improving engagement with opposing views

By Michael Yeomans, J. Minson, Hanne K. Collins, Frances S. Chen, Francesca Gino. 2020

112 citations recorded by the source corpus.

Abstract We examine “conversational receptiveness” – the use of language to communicate one’s willingness to thoughtfully engage with opposing views. We develop an interpretable machine-learning algorithm to identify the linguistic profile of receptiveness (Studies 1A-B). We then show that in contentious policy discussions, government executives who were rated as more receptive - according to our algorithm and their partners, but not their own self-evaluations - were considered better teammates, advisors, and workplace representatives (Study 2). Furthermore, using field data from a setting where conflict management is endemic to productivity, we show that conversational receptiveness at the beginning of a conversation forestalls conflict escalation at the end. Specifically, Wikipedia editors who write more receptive posts are less prone to receiving personal attacks from disagreeing editors (Study 3). We develop a “receptiveness recipe” intervention based on our algorithm. We find that writers who follow the recipe are seen as more desirable partners for future collaboration and their messages are seen as more persuasive (Study 4). Overall, we find that conversational receptiveness is reliably measurable, has meaningful relational consequences, and can be substantially improved using our intervention (183 words).

Subfields: Artificial Intelligence, Communication, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/ff3b0be4fb7debf1312c92381577292288755674

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Yejin Choi
2. Ximing Lu
3. Daniel Khashabi
4. Maarten Sap
5. Liwei Jiang
6. Mohit Bansal
7. Youngjae Yu
8. Gunhee Kim

In Communication

1. Yejin Choi
2. Maarten Sap

In Sociology and Political Science

1. Maarten Sap
2. Yejin Choi
3. Liwei Jiang
4. Ximing Lu
5. Diyi Yang
6. Yulia Tsvetkov
7. Noah A. Smith

Citations in the corpus

• ProsocialDialog: A Prosocial Backbone for Conversational Agents (2022, background): “To improve receptiveness when giving constructive feedback (i.e., aiming to encourage listeners to be willing to change their behaviors or opinions), carefully curating conversations according to psychology and communication studies is critical (Yeomans et al., 2020).”