Exploring value concerns in social media issue reports: a human-LLM study of Bluesky and Mastodon

Design choices in social media platforms have repeatedly undermined human values, as highlighted by the recent U.S. Senate hearing on child safety failures in major platforms. Issue-tracking systems like GitHub Issues serve as primary venues for discussing these design decisions, revealing trade-offs that may affect user values. This paper examines the GitHub issue reports of two popular decentralised social media platforms, Bluesky and Mastodon, to identify scenarios that serve as emerging evidence of undermining values, i.e. Value Concerns. We select these platforms due to their focus on values like autonomy, transparency, and privacy. We utilise the reasoning capabilities of Large Language Models (LLMs) for inference of value concerns. The inferred concerns are iteratively refined and then evaluated by human evaluators from computing and social sciences; the endorsed concerns are categorised to reveal mechanisms of undermining values in social media, offering insights for mitigating them.