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Online Engagement with Retracted Articles: Who, When, and How?

Henry K. Dambanemuya, Rod Abhari, Nicholas Vincent, Emőke-Ágnes Horvát

TL;DR

The paper investigates how Twitter users discuss retracted articles, comparing attention and engagement to non-retracted articles and analyzing differences across user types and pre/post retraction windows. By integrating Retraction Watch, Altmetric, and Twitter API data, and by classifying users into five types, the study reveals that retracted articles attract more attention and engagement than non-retracted ones, with the public and bots contributing most to attention and academics and science communicators driving higher engagement with retraction-related content. Most engagement occurs before retraction, while post-retraction activity shows distinct patterns, including high bot activity that often emphasizes the fact of retraction. Keyword-based analysis further shows that retraction-related discussions are more prevalent and closely tied to engagement, suggesting avenues for platform-assisted early detection and improved science communication. Overall, the findings highlight the heterogeneity of online responses to retractions and point to practical design interventions for social media platforms to support accurate scientific discourse and timely corrections.

Abstract

Retracted research discussed on social media can spread misinformation. Yet we lack an understanding of how retracted articles are mentioned by academic and non-academic users. This is especially relevant on Twitter due to the platform's prominent role in science communication. Here, we analyze the pre- and post-retraction differences in Twitter attention and engagement metrics for over 3,800 retracted English-language articles alongside comparable non-retracted articles. We subset these findings according to five user types detected by our supervised learning classifier: members of the public, academics, bots, science practitioners, and science communicators. We find that retracted articles receive greater user attention (tweet count) and engagement (likes, retweets, and replies) than non-retracted articles, especially among members of the public and bots, with the majority of user engagement happening before retraction. Our results highlight the prominent role of non-experts in discussions of retracted research and suggest an opportunity for social media platforms to contribute towards early detection of problematic scientific research online.

Online Engagement with Retracted Articles: Who, When, and How?

TL;DR

The paper investigates how Twitter users discuss retracted articles, comparing attention and engagement to non-retracted articles and analyzing differences across user types and pre/post retraction windows. By integrating Retraction Watch, Altmetric, and Twitter API data, and by classifying users into five types, the study reveals that retracted articles attract more attention and engagement than non-retracted ones, with the public and bots contributing most to attention and academics and science communicators driving higher engagement with retraction-related content. Most engagement occurs before retraction, while post-retraction activity shows distinct patterns, including high bot activity that often emphasizes the fact of retraction. Keyword-based analysis further shows that retraction-related discussions are more prevalent and closely tied to engagement, suggesting avenues for platform-assisted early detection and improved science communication. Overall, the findings highlight the heterogeneity of online responses to retractions and point to practical design interventions for social media platforms to support accurate scientific discourse and timely corrections.

Abstract

Retracted research discussed on social media can spread misinformation. Yet we lack an understanding of how retracted articles are mentioned by academic and non-academic users. This is especially relevant on Twitter due to the platform's prominent role in science communication. Here, we analyze the pre- and post-retraction differences in Twitter attention and engagement metrics for over 3,800 retracted English-language articles alongside comparable non-retracted articles. We subset these findings according to five user types detected by our supervised learning classifier: members of the public, academics, bots, science practitioners, and science communicators. We find that retracted articles receive greater user attention (tweet count) and engagement (likes, retweets, and replies) than non-retracted articles, especially among members of the public and bots, with the majority of user engagement happening before retraction. Our results highlight the prominent role of non-experts in discussions of retracted research and suggest an opportunity for social media platforms to contribute towards early detection of problematic scientific research online.
Paper Structure (31 sections, 2 equations, 4 figures, 5 tables)

This paper contains 31 sections, 2 equations, 4 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Related Work: Online Attention to Scientific Retractions
  3. Data & Methods
  4. Datasets: Altmetric and Retraction Watch
  5. Retracted Articles.
  6. Non-Retracted Articles.
  7. Attention & Engagement.
  8. Language.
  9. Ethics Statement.
  10. User Type Classification
  11. Retraction Status and Time-Frame
  12. Keyword Analysis
  13. Results
  14. RQ1: User attention and engagement with (non)-retracted articles
  15. Retracted articles receive more attention (i.e., original tweets, excluding retweets and replies) compared to non-retracted articles.
  16. ...and 16 more sections

Figures (4)

  • Figure 1: Percentage of Twitter users in the retracted (N=9,514) and non-retracted (N=2,495) groups that belong to each user type and identified through human annotation and a rule-based algorithm.
  • Figure 2: Distributions of Twitter attention and engagement metrics for retracted and control (non-retracted) articles.
  • Figure 3: Mean and standard deviation (error bars) of user attention (tweets) and engagement (likes) with retracted and non-retracted articles by user types.
  • Figure 4: Percentage of tweets that mention the top-5 retraction-related keywords broken down by user type. User types are inferred using a supervised decision tree classifier.