{"title":"通过主题建模了解Deepfake研究和趋势","authors":"Chen Chen, Dion Hoe‐Lian Goh","doi":"10.1002/pra2.895","DOIUrl":null,"url":null,"abstract":"ABSTRACT Deepfake research has gained traction in recent years. Surveys have been conducted to summarize work on the detection and generation of deepfakes. However, a more comprehensive and quantitative overview that encompasses both technical and non‐technical areas is lacking. We address this gap using topic modelling to discover deepfake research topics in academic publications. Our results show that while detection techniques topics dominate the research field, other areas, such as privacy and legal research, offer potential avenues for further exploration.","PeriodicalId":37833,"journal":{"name":"Proceedings of the Association for Information Science and Technology","volume":"46 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Understanding Deepfake Research and Trends through Topic Modelling\",\"authors\":\"Chen Chen, Dion Hoe‐Lian Goh\",\"doi\":\"10.1002/pra2.895\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"ABSTRACT Deepfake research has gained traction in recent years. Surveys have been conducted to summarize work on the detection and generation of deepfakes. However, a more comprehensive and quantitative overview that encompasses both technical and non‐technical areas is lacking. We address this gap using topic modelling to discover deepfake research topics in academic publications. Our results show that while detection techniques topics dominate the research field, other areas, such as privacy and legal research, offer potential avenues for further exploration.\",\"PeriodicalId\":37833,\"journal\":{\"name\":\"Proceedings of the Association for Information Science and Technology\",\"volume\":\"46 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the Association for Information Science and Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1002/pra2.895\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"Social Sciences\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Association for Information Science and Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1002/pra2.895","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Social Sciences","Score":null,"Total":0}
Understanding Deepfake Research and Trends through Topic Modelling
ABSTRACT Deepfake research has gained traction in recent years. Surveys have been conducted to summarize work on the detection and generation of deepfakes. However, a more comprehensive and quantitative overview that encompasses both technical and non‐technical areas is lacking. We address this gap using topic modelling to discover deepfake research topics in academic publications. Our results show that while detection techniques topics dominate the research field, other areas, such as privacy and legal research, offer potential avenues for further exploration.
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