This study explores the potential of using tweets to predict article retractions, by analyzing the Twitter mention data of retracted articles as the treatment group and unretracted articles that were matched as a control group. The results show that tweets could predict article retractions with an accuracy of 57%-60% by machine learning models. Sentiment analysis is not effective in predicting article retractions. The study sheds light on a novel method of detecting scientific misconduct in the early stage.

The gender disparity in scientific research has sparked extensive discussion, yet there is currently no consensus on the prevalence of scientific misconduct across genders. This study investigates this issue by collecting 5,256 retracted articles with the gender of their first authors based on the Web of Science and Retraction Watch databases. Considering the overall research productivity of both genders, our results demonstrate that male researchers generally exhibit higher retraction rates than their female counterparts in all disciplines. Female researchers retract slightly more due to falsification, while male researchers tend to retract more due to ethical issues, plagiarism, and authorship issues. In most countries with high numbers of retractions, male researchers exhibit higher retraction rates, with Iran being particularly severe. From the perspective of gender disparity, this study emphasizes the importance of addressing scientific misconduct and its underlying causes, to create a climate of accountability in the scientific community.