S. Rizzo, Flavio Bertini, D. Montesi, Carlo Stomeo
{"title":"社交媒体中的文本水印","authors":"S. Rizzo, Flavio Bertini, D. Montesi, Carlo Stomeo","doi":"10.1145/3110025.3116203","DOIUrl":null,"url":null,"abstract":"One of the most shared content in Social Media (SM) is text, making it vulnerable to copy and authorship misappropriation. Due to the low data noise, watermark embedding is very hard. This problem is exacerbated in the context of SM, where the amount of data in a single message can be extremely small, like in Twitter. Firstly, in this paper we investigate whether SM do applies watermarks on the texts. Then, we propose a text watermarking method able to work on all the SM platforms considered, while ensuring visual indistinguishability and length preservation of the original text and robustness to copy and paste. We conduct an extended evaluation on eighteen different SM platforms by using 6,000 posts from six public figures' profiles.","PeriodicalId":399660,"journal":{"name":"Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017","volume":"33 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"20","resultStr":"{\"title\":\"Text Watermarking in Social Media\",\"authors\":\"S. Rizzo, Flavio Bertini, D. Montesi, Carlo Stomeo\",\"doi\":\"10.1145/3110025.3116203\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"One of the most shared content in Social Media (SM) is text, making it vulnerable to copy and authorship misappropriation. Due to the low data noise, watermark embedding is very hard. This problem is exacerbated in the context of SM, where the amount of data in a single message can be extremely small, like in Twitter. Firstly, in this paper we investigate whether SM do applies watermarks on the texts. Then, we propose a text watermarking method able to work on all the SM platforms considered, while ensuring visual indistinguishability and length preservation of the original text and robustness to copy and paste. We conduct an extended evaluation on eighteen different SM platforms by using 6,000 posts from six public figures' profiles.\",\"PeriodicalId\":399660,\"journal\":{\"name\":\"Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017\",\"volume\":\"33 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-07-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"20\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3110025.3116203\",\"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 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3110025.3116203","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
One of the most shared content in Social Media (SM) is text, making it vulnerable to copy and authorship misappropriation. Due to the low data noise, watermark embedding is very hard. This problem is exacerbated in the context of SM, where the amount of data in a single message can be extremely small, like in Twitter. Firstly, in this paper we investigate whether SM do applies watermarks on the texts. Then, we propose a text watermarking method able to work on all the SM platforms considered, while ensuring visual indistinguishability and length preservation of the original text and robustness to copy and paste. We conduct an extended evaluation on eighteen different SM platforms by using 6,000 posts from six public figures' profiles.