Alireza Karduni, Isaac Cho, Ryan Wesslen, Sashank Santhanam, Svitlana Volkova, Dustin L. Arendt, Samira Shaikh, Wenwen Dou
{"title":"容易受到错误信息的影响?: Verifi !","authors":"Alireza Karduni, Isaac Cho, Ryan Wesslen, Sashank Santhanam, Svitlana Volkova, Dustin L. Arendt, Samira Shaikh, Wenwen Dou","doi":"10.1145/3301275.3302320","DOIUrl":null,"url":null,"abstract":"We present Verifi2, a visual analytic system to support the investigation of misinformation on social media. Various models and studies have emerged from multiple disciplines to detect or understand the effects of misinformation. However, there is still a lack of intuitive and accessible tools that help social media users distinguish misinformation from verified news. Verifi2 uses state-of-the-art computational methods to highlight linguistic, network, and image features that can distinguish suspicious news accounts. By exploring news on a source and document level in Verifi2, users can interact with the complex dimensions that characterize misinformation and contrast how real and suspicious news outlets differ on these dimensions. To evaluate Verifi2, we conduct interviews with experts in digital media, communications, education, and psychology who study misinformation. Our interviews highlight the complexity of the problem of combating misinformation and show promising potential for Verifi2 as an educational tool on misinformation.","PeriodicalId":153096,"journal":{"name":"Proceedings of the 24th International Conference on Intelligent User Interfaces","volume":"7 Suppl 3 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"25","resultStr":"{\"title\":\"Vulnerable to misinformation?: Verifi!\",\"authors\":\"Alireza Karduni, Isaac Cho, Ryan Wesslen, Sashank Santhanam, Svitlana Volkova, Dustin L. Arendt, Samira Shaikh, Wenwen Dou\",\"doi\":\"10.1145/3301275.3302320\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We present Verifi2, a visual analytic system to support the investigation of misinformation on social media. Various models and studies have emerged from multiple disciplines to detect or understand the effects of misinformation. However, there is still a lack of intuitive and accessible tools that help social media users distinguish misinformation from verified news. Verifi2 uses state-of-the-art computational methods to highlight linguistic, network, and image features that can distinguish suspicious news accounts. By exploring news on a source and document level in Verifi2, users can interact with the complex dimensions that characterize misinformation and contrast how real and suspicious news outlets differ on these dimensions. To evaluate Verifi2, we conduct interviews with experts in digital media, communications, education, and psychology who study misinformation. Our interviews highlight the complexity of the problem of combating misinformation and show promising potential for Verifi2 as an educational tool on misinformation.\",\"PeriodicalId\":153096,\"journal\":{\"name\":\"Proceedings of the 24th International Conference on Intelligent User Interfaces\",\"volume\":\"7 Suppl 3 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-07-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"25\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 24th International Conference on Intelligent User Interfaces\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3301275.3302320\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 24th International Conference on Intelligent User Interfaces","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3301275.3302320","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
We present Verifi2, a visual analytic system to support the investigation of misinformation on social media. Various models and studies have emerged from multiple disciplines to detect or understand the effects of misinformation. However, there is still a lack of intuitive and accessible tools that help social media users distinguish misinformation from verified news. Verifi2 uses state-of-the-art computational methods to highlight linguistic, network, and image features that can distinguish suspicious news accounts. By exploring news on a source and document level in Verifi2, users can interact with the complex dimensions that characterize misinformation and contrast how real and suspicious news outlets differ on these dimensions. To evaluate Verifi2, we conduct interviews with experts in digital media, communications, education, and psychology who study misinformation. Our interviews highlight the complexity of the problem of combating misinformation and show promising potential for Verifi2 as an educational tool on misinformation.