推特能预测文章撤回吗?

Erdong Zheng, Hui-Zhen Fu, Zhichao Fang
{"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}
引用次数: 0

摘要

本研究通过分析被撤稿文章作为实验组和未撤稿文章作为对照组的推特提及数据,探索了利用推特预测文章撤稿的潜力。结果显示,推文可以通过机器学习模型预测文章撤稿,准确率为57%-60%。情感分析不能有效预测文章撤稿。这项研究揭示了一种在早期发现科学不端行为的新方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Can tweets predict article retractions?
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.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信