Thread With Caution: Proactively Helping Users Assess and Deescalate Tension in Their Online Discussions

TL;DR

This paper introduces a proactive tool that helps online discussion participants recognize and deescalate tension in real-time, aiming to reduce incivility through user empowerment rather than moderation alone.

Contribution

It presents a novel paradigm and algorithmic tool that proactively informs users about conversation tension, supported by a user study demonstrating its effectiveness.

Findings

Participants found the tool valuable for identifying missed tension cues.

The tool prompted users to reflect and revise their replies.

Drafts showed reduced escalation risk when warned by the tool.

Abstract

Incivility remains a major challenge for online discussion platforms, to such an extent that even conversations between well-intentioned users can often derail into uncivil behavior. Traditionally, platforms have relied on moderators to -- with or without algorithmic assistance -- take corrective actions such as removing comments or banning users. In this work we propose a complementary paradigm that directly empowers users by proactively enhancing their awareness about existing tension in the conversation they are engaging in and actively guides them as they are drafting their replies to avoid further escalation. As a proof of concept for this paradigm, we design an algorithmic tool that provides such proactive information directly to users, and conduct a user study in a popular discussion platform. Through a mixed methods approach combining surveys with a randomized controlled experiment, we uncover qualitative and quantitative insights regarding how the participants utilize and react to this information. Most participants report finding this proactive paradigm valuable, noting that it helps them to identify tension that they may have otherwise missed and prompts them to further reflect on their own replies and to revise them. These effects are corroborated by a comparison of how the participants draft their reply when our tool warns them that their conversation is at risk of derailing into uncivil behavior versus in a control condition where the tool is disabled. These preliminary findings highlight the potential of this user-centered paradigm and point to concrete directions for future implementations.