{"title":"分析对机器视频新闻态度的驱动因素:新华知音》案例研究","authors":"Peng Duan","doi":"10.1177/20594364241262681","DOIUrl":null,"url":null,"abstract":"The COVID-19 pandemic has highlighted the need for reliable information and news sources. In 2020, Xinhua News Agency launched “Zhiyun,” an epidemic reporting robot that can generate COVID-19 news reports based on visual data. This development raises key issues regarding the effectiveness of machine-generated news compared to traditional sources, especially news related to major public health events such as the pandemic. Using the Cognitive-Affective-Conative Model and ANCOVA method, this paper conducts experimental research to obtain data and studies the impact of machine-made news on the audience’s attitude towards COVID-19 news. The analysis used a 2 × 2 factorial online experimental method to test the impact of two variables: “theme” and “news format.” The research results indicate that the theme and news format significantly affect the audience’s attitude towards epidemic news, and machine-generated video news received a more positive response than news written by human journalists. Based on the results of this study, it can be concluded that machine-generated news has great potential to provide accessible and reliable information during major public health events such as COVID-19. This study has significant implications for the news industry, indicating the possibility of increasing the use of machine news production in the future.","PeriodicalId":3,"journal":{"name":"ACS Applied Electronic Materials","volume":"44 47","pages":""},"PeriodicalIF":4.3000,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Analyzing drivers of attitudes toward machine video news: A Xinhua Zhiyun case study\",\"authors\":\"Peng Duan\",\"doi\":\"10.1177/20594364241262681\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The COVID-19 pandemic has highlighted the need for reliable information and news sources. In 2020, Xinhua News Agency launched “Zhiyun,” an epidemic reporting robot that can generate COVID-19 news reports based on visual data. This development raises key issues regarding the effectiveness of machine-generated news compared to traditional sources, especially news related to major public health events such as the pandemic. Using the Cognitive-Affective-Conative Model and ANCOVA method, this paper conducts experimental research to obtain data and studies the impact of machine-made news on the audience’s attitude towards COVID-19 news. The analysis used a 2 × 2 factorial online experimental method to test the impact of two variables: “theme” and “news format.” The research results indicate that the theme and news format significantly affect the audience’s attitude towards epidemic news, and machine-generated video news received a more positive response than news written by human journalists. Based on the results of this study, it can be concluded that machine-generated news has great potential to provide accessible and reliable information during major public health events such as COVID-19. This study has significant implications for the news industry, indicating the possibility of increasing the use of machine news production in the future.\",\"PeriodicalId\":3,\"journal\":{\"name\":\"ACS Applied Electronic Materials\",\"volume\":\"44 47\",\"pages\":\"\"},\"PeriodicalIF\":4.3000,\"publicationDate\":\"2024-06-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACS Applied Electronic Materials\",\"FirstCategoryId\":\"98\",\"ListUrlMain\":\"https://doi.org/10.1177/20594364241262681\",\"RegionNum\":3,\"RegionCategory\":\"材料科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Applied Electronic Materials","FirstCategoryId":"98","ListUrlMain":"https://doi.org/10.1177/20594364241262681","RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
Analyzing drivers of attitudes toward machine video news: A Xinhua Zhiyun case study
The COVID-19 pandemic has highlighted the need for reliable information and news sources. In 2020, Xinhua News Agency launched “Zhiyun,” an epidemic reporting robot that can generate COVID-19 news reports based on visual data. This development raises key issues regarding the effectiveness of machine-generated news compared to traditional sources, especially news related to major public health events such as the pandemic. Using the Cognitive-Affective-Conative Model and ANCOVA method, this paper conducts experimental research to obtain data and studies the impact of machine-made news on the audience’s attitude towards COVID-19 news. The analysis used a 2 × 2 factorial online experimental method to test the impact of two variables: “theme” and “news format.” The research results indicate that the theme and news format significantly affect the audience’s attitude towards epidemic news, and machine-generated video news received a more positive response than news written by human journalists. Based on the results of this study, it can be concluded that machine-generated news has great potential to provide accessible and reliable information during major public health events such as COVID-19. This study has significant implications for the news industry, indicating the possibility of increasing the use of machine news production in the future.
期刊介绍:
ACS Applied Electronic Materials is an interdisciplinary journal publishing original research covering all aspects of electronic materials. The journal is devoted to reports of new and original experimental and theoretical research of an applied nature that integrate knowledge in the areas of materials science, engineering, optics, physics, and chemistry into important applications of electronic materials. Sample research topics that span the journal's scope are inorganic, organic, ionic and polymeric materials with properties that include conducting, semiconducting, superconducting, insulating, dielectric, magnetic, optoelectronic, piezoelectric, ferroelectric and thermoelectric.
Indexed/Abstracted:
Web of Science SCIE
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CAS
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