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The quality of a user's social media experience is determined both by the content they see and by the quality of the conversation and interaction around it. In this paper, we look at replies to tweets from mainstream media outlets and official government agencies and assess if they are good faith, engaging honestly and constructively with the original post, or bad faith, attacking the author or derailing the conversation. We assess automated approaches that may help in making this determination and then show that within our dataset of replies to mainstream media outlets and government agencies, bad faith interactions constitute 68.3% of all replies we studied, suggesting potential concerns about the quality of discourse in these specific conversational contexts. This is particularly true from verified accounts, where 91.7% of replies were bad faith. Given that verified accounts are algorithmically amplified, we discuss the implications of our work for understanding the user experience on social media.

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In Bad Faith: Assessing Discussion Quality on Social Media

  • 会议论文

  • 发表日期：

    2026年

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  • Celia Chen,
  • Alex Leitch,
  • William Jordan Conway,
  • Eric Cotugno,
  • Emily Klein,
  • Rajesh Kumar Gnanasekaran,
  • Kristin Buckstad Hamilton,
  • Casi Sherman,
  • Celia Sterrn,
  • Logan C. Stevens,
  • Rebecca Zarrella,
  • Jennifer Golbeck

摘要

The quality of a user's social media experience is determined both by the content they see and by the quality of the conversation and interaction around it. In this paper, we look at replies to tweets from mainstream media outlets and official government agencies and assess if they are good faith, engaging honestly and constructively with the original post, or bad faith, attacking the author or derailing the conversation. We assess automated approaches that may help in making this determination and then show that within our dataset of replies to mainstream media outlets and government agencies, bad faith interactions constitute 68.3% of all replies we studied, suggesting potential concerns about the quality of discourse in these specific conversational contexts. This is particularly true from verified accounts, where 91.7% of replies were bad faith. Given that verified accounts are algorithmically amplified, we discuss the implications of our work for understanding the user experience on social media.