{"title":"机器如何有效防御机器生成的假新闻?实证研究","authors":"Meghana Moorthy Bhat, S. Parthasarathy","doi":"10.18653/v1/2020.insights-1.7","DOIUrl":null,"url":null,"abstract":"We empirically study the effectiveness of machine-generated fake news detectors by understanding the model’s sensitivity to different synthetic perturbations during test time. The current machine-generated fake news detectors rely on provenance to determine the veracity of news. Our experiments find that the success of these detectors can be limited since they are rarely sensitive to semantic perturbations and are very sensitive to syntactic perturbations. Also, we would like to open-source our code and believe it could be a useful diagnostic tool for evaluating models aimed at fighting machine-generated fake news.","PeriodicalId":441528,"journal":{"name":"First Workshop on Insights from Negative Results in NLP","volume":"251 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":"{\"title\":\"How Effectively Can Machines Defend Against Machine-Generated Fake News? An Empirical Study\",\"authors\":\"Meghana Moorthy Bhat, S. Parthasarathy\",\"doi\":\"10.18653/v1/2020.insights-1.7\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We empirically study the effectiveness of machine-generated fake news detectors by understanding the model’s sensitivity to different synthetic perturbations during test time. The current machine-generated fake news detectors rely on provenance to determine the veracity of news. Our experiments find that the success of these detectors can be limited since they are rarely sensitive to semantic perturbations and are very sensitive to syntactic perturbations. Also, we would like to open-source our code and believe it could be a useful diagnostic tool for evaluating models aimed at fighting machine-generated fake news.\",\"PeriodicalId\":441528,\"journal\":{\"name\":\"First Workshop on Insights from Negative Results in NLP\",\"volume\":\"251 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"12\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"First Workshop on Insights from Negative Results in NLP\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.18653/v1/2020.insights-1.7\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"First Workshop on Insights from Negative Results in NLP","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.18653/v1/2020.insights-1.7","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
How Effectively Can Machines Defend Against Machine-Generated Fake News? An Empirical Study
We empirically study the effectiveness of machine-generated fake news detectors by understanding the model’s sensitivity to different synthetic perturbations during test time. The current machine-generated fake news detectors rely on provenance to determine the veracity of news. Our experiments find that the success of these detectors can be limited since they are rarely sensitive to semantic perturbations and are very sensitive to syntactic perturbations. Also, we would like to open-source our code and believe it could be a useful diagnostic tool for evaluating models aimed at fighting machine-generated fake news.
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