Litcius/Paper detail

Slowing it Down: Towards Facilitating Interpersonal Mindfulness in Online Polarizing Conversations Over Social Media

Teale W. Masrani, Jack Jamieson, Naomi Yamashita, Helen Ai He

2023Proceedings of the ACM on Human-Computer Interaction13 citationsDOIOpen Access PDF

Abstract

Discussions about polarizing topics are essential to have, yet they can easily become hostile, aggressive, or distressing on current social media platforms. Content moderation interventions aim to mitigate this issue, though such approaches are reactionary, removing harmful content only after it has been posted. We conducted a mixed-methods experiment with 40 participants to investigate how a design friction that manipulates the temporal flow during a contentious conversation can foster interpersonal mindfulness, a trait critical for productive communication. Dyads were randomly assigned into the Control Group which received no intervention, and the Experiment Group where participants were limited to sending one message per two-minute interval. Triangulating quantitative and qualitative data from conversation logs, questionnaires, interviews, and computational text analysis, our findings revealed a two-fold effect: Experiment Group participants felt simultaneously frustrated by the intervention as it disrupted the pacing of their conversation and interfered with rapport-building, and appreciative of the intervention as it nudged them towards writing thoughtful and task-focused messages. We discuss implications of these findings for future investigation into the design of temporal interventions to influence interpersonal mindfulness during polarizing online conversations.

Topics & Concepts

ConversationMindfulnessInterpersonal communicationPsychologyPsychological interventionIntervention (counseling)Social mediaModerationSocial psychologyDevelopmental psychologyApplied psychologyPsychotherapistCommunicationComputer sciencePsychiatryWorld Wide WebHate Speech and Cyberbullying DetectionImpact of Technology on AdolescentsSentiment Analysis and Opinion Mining