{"title":"为公共健康和安全自动识别谣言:主题分类与多维特征融合相结合的策略","authors":"Yuxuan Zhang, Song Huang","doi":"10.1016/j.jksuci.2024.102087","DOIUrl":null,"url":null,"abstract":"<div><p>With the COVID-19 outbreak, health-related rumors have attracted significant attention from governments and global society. These rumors often mislead the public through multimedia, amplifying their negative impact and potentially manipulating public health narratives. On social media, detecting these rumors faces unique challenges, especially for emerging health events. Existing detection algorithms struggle because they mainly learn event-specific features that are not applicable to new or unseen events. To overcome this, we developed an end-to-end framework called the Health Domain Multimodal Rumor Detection Neural Network (HDRNN). This framework extracts invariant features and effectively detects new health-related rumors. It consists of three components: a multimodal feature extractor, a rumor detector, and an event discriminator. The multimodal feature extractor extracts text and visual features from posts, working with rumor detectors to learn discriminative features. Event discriminator remove specific features while retaining shared ones across events. Extensive experiments on datasets from Tencent News and Sina Weibo show that our HDRNN model excels in multimodal health rumor detection, surpassing current methods.</p></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":null,"pages":null},"PeriodicalIF":5.2000,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1319157824001769/pdfft?md5=1281c1b6c006ff6ac8e07b89476a3d71&pid=1-s2.0-S1319157824001769-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Automatic rumor recognition for public health and safety: A strategy combining topic classification and multi-dimensional feature fusion\",\"authors\":\"Yuxuan Zhang, Song Huang\",\"doi\":\"10.1016/j.jksuci.2024.102087\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>With the COVID-19 outbreak, health-related rumors have attracted significant attention from governments and global society. These rumors often mislead the public through multimedia, amplifying their negative impact and potentially manipulating public health narratives. On social media, detecting these rumors faces unique challenges, especially for emerging health events. Existing detection algorithms struggle because they mainly learn event-specific features that are not applicable to new or unseen events. To overcome this, we developed an end-to-end framework called the Health Domain Multimodal Rumor Detection Neural Network (HDRNN). This framework extracts invariant features and effectively detects new health-related rumors. It consists of three components: a multimodal feature extractor, a rumor detector, and an event discriminator. The multimodal feature extractor extracts text and visual features from posts, working with rumor detectors to learn discriminative features. Event discriminator remove specific features while retaining shared ones across events. Extensive experiments on datasets from Tencent News and Sina Weibo show that our HDRNN model excels in multimodal health rumor detection, surpassing current methods.</p></div>\",\"PeriodicalId\":48547,\"journal\":{\"name\":\"Journal of King Saud University-Computer and Information Sciences\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":5.2000,\"publicationDate\":\"2024-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S1319157824001769/pdfft?md5=1281c1b6c006ff6ac8e07b89476a3d71&pid=1-s2.0-S1319157824001769-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of King Saud University-Computer and Information Sciences\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1319157824001769\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of King Saud University-Computer and Information Sciences","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1319157824001769","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
Automatic rumor recognition for public health and safety: A strategy combining topic classification and multi-dimensional feature fusion
With the COVID-19 outbreak, health-related rumors have attracted significant attention from governments and global society. These rumors often mislead the public through multimedia, amplifying their negative impact and potentially manipulating public health narratives. On social media, detecting these rumors faces unique challenges, especially for emerging health events. Existing detection algorithms struggle because they mainly learn event-specific features that are not applicable to new or unseen events. To overcome this, we developed an end-to-end framework called the Health Domain Multimodal Rumor Detection Neural Network (HDRNN). This framework extracts invariant features and effectively detects new health-related rumors. It consists of three components: a multimodal feature extractor, a rumor detector, and an event discriminator. The multimodal feature extractor extracts text and visual features from posts, working with rumor detectors to learn discriminative features. Event discriminator remove specific features while retaining shared ones across events. Extensive experiments on datasets from Tencent News and Sina Weibo show that our HDRNN model excels in multimodal health rumor detection, surpassing current methods.
期刊介绍:
In 2022 the Journal of King Saud University - Computer and Information Sciences will become an author paid open access journal. Authors who submit their manuscript after October 31st 2021 will be asked to pay an Article Processing Charge (APC) after acceptance of their paper to make their work immediately, permanently, and freely accessible to all. The Journal of King Saud University Computer and Information Sciences is a refereed, international journal that covers all aspects of both foundations of computer and its practical applications.