{"title":"网络世界中公众对科学的理解","authors":"Svetlomir Zdravkov","doi":"10.53656/str2022-3-4-pub","DOIUrl":null,"url":null,"abstract":"Internet mediation is playing an increasingly important role in informing the public about scientific news. Thus, it became the main source of data that formed the public’s image of science. The digital traces that users leave on many online platforms are an important source of empirical data that is barely being used; it may reveal new ways to connect science and society. That is why we propose a new conceptual approach within the Public Understanding of Science, which will lay the foundations for future empirical research. It integrates the combination of Actor Network Theory and machine learning in the analysis of large text arrays, which allow both quantitative measurement and qualitative analysis of popular scientific discussions in the online space.","PeriodicalId":40820,"journal":{"name":"Strategies for Policy in Science and Education-Strategii na Obrazovatelnata i Nauchnata Politika","volume":"2 1","pages":""},"PeriodicalIF":0.4000,"publicationDate":"2022-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Public Understanding of Science in the Network World\",\"authors\":\"Svetlomir Zdravkov\",\"doi\":\"10.53656/str2022-3-4-pub\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Internet mediation is playing an increasingly important role in informing the public about scientific news. Thus, it became the main source of data that formed the public’s image of science. The digital traces that users leave on many online platforms are an important source of empirical data that is barely being used; it may reveal new ways to connect science and society. That is why we propose a new conceptual approach within the Public Understanding of Science, which will lay the foundations for future empirical research. It integrates the combination of Actor Network Theory and machine learning in the analysis of large text arrays, which allow both quantitative measurement and qualitative analysis of popular scientific discussions in the online space.\",\"PeriodicalId\":40820,\"journal\":{\"name\":\"Strategies for Policy in Science and Education-Strategii na Obrazovatelnata i Nauchnata Politika\",\"volume\":\"2 1\",\"pages\":\"\"},\"PeriodicalIF\":0.4000,\"publicationDate\":\"2022-06-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Strategies for Policy in Science and Education-Strategii na Obrazovatelnata i Nauchnata Politika\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.53656/str2022-3-4-pub\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"EDUCATION & EDUCATIONAL RESEARCH\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Strategies for Policy in Science and Education-Strategii na Obrazovatelnata i Nauchnata Politika","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.53656/str2022-3-4-pub","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"EDUCATION & EDUCATIONAL RESEARCH","Score":null,"Total":0}
Public Understanding of Science in the Network World
Internet mediation is playing an increasingly important role in informing the public about scientific news. Thus, it became the main source of data that formed the public’s image of science. The digital traces that users leave on many online platforms are an important source of empirical data that is barely being used; it may reveal new ways to connect science and society. That is why we propose a new conceptual approach within the Public Understanding of Science, which will lay the foundations for future empirical research. It integrates the combination of Actor Network Theory and machine learning in the analysis of large text arrays, which allow both quantitative measurement and qualitative analysis of popular scientific discussions in the online space.