{"title":"推特能预测文章撤回吗?","authors":"Erdong Zheng, Hui-Zhen Fu, Zhichao Fang","doi":"10.55835/644126a8763e8d2091a0cfdc","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":334841,"journal":{"name":"27th International Conference on Science, Technology and Innovation Indicators (STI 2023)","volume":"108 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Can tweets predict article retractions?\",\"authors\":\"Erdong Zheng, Hui-Zhen Fu, Zhichao Fang\",\"doi\":\"10.55835/644126a8763e8d2091a0cfdc\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":334841,\"journal\":{\"name\":\"27th International Conference on Science, Technology and Innovation Indicators (STI 2023)\",\"volume\":\"108 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-05-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"27th International Conference on Science, Technology and Innovation Indicators (STI 2023)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.55835/644126a8763e8d2091a0cfdc\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"27th International Conference on Science, Technology and Innovation Indicators (STI 2023)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.55835/644126a8763e8d2091a0cfdc","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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.