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This study investigates the nature of discussions surrounding retracted scientific articles on Bluesky, categorizing posts as either demonstrating "good practice" (e.g., acknowledging retraction status) or "bad practice" (e.g., spreading misinformation). Through manual coding of a dataset comprising 284 retracted articles and over 900 Bluesky posts, the research found that the vast majority (89.9%) of posts after retraction reflect good practices, significantly outweighing those spreading misinformation. The analysis also reveals that sentiment analysis is more effective at identifying potential flaws before retraction than detecting misinformation spread afterward.
Despite fears of rampant misinformation, Bluesky discussions of retracted science papers overwhelmingly demonstrate "good practice" like acknowledging retractions, suggesting social media can support research integrity.
Post-publication peer review (PPPR) has emerged as an important supplement to traditional peer review, with social media playing a growing role in publicising potential problems in published research. However, it remains unclear whether social media discussions of retracted articles primarily reflect good practices, such as exposing flaws and acknowledging retraction status, or bad practices, such as overlooking retractions and continuing to disseminate scientific misinformation. In this study, we collected Bluesky posts referencing scholarly articles from Altmetric and retrieved metadata for the referenced articles using OpenAlex. The final dataset included 284 retracted articles with 79 pre-retraction posts and 857 post-retraction posts, 59 retraction notices with 186 posts, and 609,461 non-retracted articles with 1,344,756 posts. We manually coded Bluesky posts discussing retracted articles to identify instances of good and bad practice. The results show that posts demonstrating good practice (89.9%) substantially outnumbered those demonstrating bad practice (10.1%). Posts reflecting good practice also had more user engagement. In the pre-retraction phase, good practice posts constituted a slight minority (43.0%), whereas in the post-retraction phase they were dominant (94.2%). Most negative posts in the pre-retraction phase (90.0%) had good practice while only 17.3% positive posts in the post-retraction phase showed bad practice. Thus, sentiment analysis can be helpful to filter posts that could flag potential flaws before retraction, but it may struggle to accurately identify the spread of misinformation after retraction. More broadly, this study highlights the potential of Bluesky to support responsible scientific communication, public scrutiny, and research integrity.
