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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.15504 |
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| _version_ | 1866916701825662976 |
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| author | Memon, Shahan Ali West, Jevin D. O'Connor, Cailin |
| author_facet | Memon, Shahan Ali West, Jevin D. O'Connor, Cailin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_15504 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Are Widely Known Findings Easier to Retract? Memon, Shahan Ali West, Jevin D. O'Connor, Cailin Digital Libraries Social and Information Networks 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. |
| title | Are Widely Known Findings Easier to Retract? |
| topic | Digital Libraries Social and Information Networks |
| url | https://arxiv.org/abs/2504.15504 |