Are Widely Known Findings Easier to Retract?

TL;DR

The paper investigates whether widely known findings are easier to retract by testing predictions from a social-diffusion framework using large-scale data. It combines a Lacroix-like $SIR$-inspired model with empirical analyses of Retraction Watch, Microsoft Academic Graph, and Altmetric to examine two predictions: $P_1$ (larger absolute post-retraction citation declines for highly cited papers) and $P_2$ (greater retraction-related Altmetric attention for highly cited papers). The results show that highly cited papers experience larger drops in post-retraction citations and receive more attention during retraction, supporting the idea that social diffusion influences retraction efficacy. The findings underscore the role of information spread in scientific self-correction and suggest policy steps to improve retraction communication, especially for high-impact work, while acknowledging limitations of using citations as proxies for familiarity or trust.

Abstract

Failures of retraction are common in science. Why do these failures occur? And, relatedly, what makes findings harder or easier to retract? We use data from Microsoft Academic Graph, Retraction Watch, and Altmetric -- including retracted papers, citation records, and Altmetric scores and mentions -- to test recently proposed answers to these questions. A recent previous study by LaCroix et al. employ simple network models to argue that the social spread of scientific information helps explain failures of retraction. One prediction of their models is that widely known or well established results, surprisingly, should be easier to retract, since their retraction is more relevant to more scientists. Our results support this conclusion. We find that highly cited papers show more significant reductions in citation after retraction and garner more attention to their retractions as they occur.

Figures (3)

• Figure 1: Delayed retractions are more effective. Results for fully connected network of size $N=100$ where agents share newly learned beliefs for 200 time steps of simulation.
• Figure 2: Highly cited retracted papers experience greater drops in post-retraction citations.(a) Absolute post-retraction citation difference between each retracted paper and its matched control (outcome 1). (b) Post-retraction citation ratio between retracted and matched papers (outcome 2).
• Figure 3: Highly cited retracted papers receive more attention during retraction.(a) shows distribution of log-transformed Altmetric scores in the 6 months before and after retraction. The x-axis represents monthly time windows relative to the retraction date, where 0 corresponds to the month of retraction (omitted for clarity), -1 indicates the month immediately preceding retraction, and +1 the month immediately following. The y-axis displays the log-transformed Altmetric score for each paper within a given month. (b) and (c) respectively show the average Altmetric score and the average number of mentions at the time of retraction for different citation groups.