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Newsmakers and news sources in “hybrid media system” (the case of the full-scaled Russian invasion coverage by Ukrainian media)

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Mendeley Data2026-04-18 收录
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The aim is to define and compare the most popular newsmakers and news sources for Ukrayins’ka Pravda (UP – the oldest, qualitative, and the most read online media), representing professional mass media, and for Ukrayina Seychas (US – one of the most popular Telegram channels), representing new media, and to describe the specifics of news production in the “hybrid media system”. The hypothesis of the research: H1 Professional mass media (UP) and new media (Telegram channel US) have strong differences in choosing both newsmakers (mentions) and news sources (newsmakers, quoted in the headlines), because leading officials and politicians (Ukrainian and international) are not recognizable for fragmented new media audience, who mostly follow information of personal interest in high-competitive media environment with many choices. H2 Both the percent of mentions and the percent of quoted newsmakers are larger for professional mass media (UP), than for new media (US), as far as new media perceive news as entertainment and do not follow the norms and traditions of classic news writing: to include recognizable newsmaker, references to sources, etc. H3 Professional mass media (UP) mention both personalities and organizations/institutions as far as consider them newsworthy, on the contrary, new media (Telegram channel US) publish news about popular personalities – to attract public’s attention, to encourage readers to share content. H4 In the “hybrid media system” qualitative mass media give preferences to reliable sources, whereas for new media it is not obligatory, thus, UP will use more reliable (official) sources, while in contrast there will be more unreliable (non-official) sources in US’s texts. Content and Method For this research computer assisted method of news collection (headlines) and newsmakers and news sources identification was used (Python language) . With request module news archive html-pages (htpps://Pravda.com.ua/archives/) were collected from Ukrayins’ka Pravda website (from 24th of February till 31st of October). With bs4 module headlines were got 35577 total. To identify a popular newsmaker – a mention of both a person’s surname and an organization/institution name were considered. With re module and a vocabulary of the most popular personalities and organizations/institutions a list of the most popular newsmakers was created To define a news source, three the most common for UP types of references were exploited: A proper name or an abbreviation before a semicolon; A proper noun or an abbreviation after a dash; A proper noun or an abbreviation after a quotation mark and a semicolon. As for US, at first the chat history was exported (the same period). The first sentence considered the headline (total 30784). As a rule, here the first sentence and a headline match. The same vocabulary and regular expressions were used to identify newsmakers and news sources.

本研究旨在明确并对比代表专业大众媒体的乌克兰最古老、高质量且阅读量最高的在线媒体《乌克兰真理报》(Ukrayins’ka Pravda,简称UP),以及代表新媒体的热门Telegram频道《乌克兰现在》(Ukrayina Seychas,简称US)中最具影响力的新闻制造者与新闻来源,并阐释混合媒体系统(hybrid media system)下新闻生产的特异性。 ### 研究假设 H1:专业大众媒体(UP)与新媒体(Telegram频道US)在选择新闻制造者(提及对象)和新闻来源(标题中引用的新闻制造者)方面均存在显著差异,原因在于在选择众多、竞争激烈的媒体环境中,碎片化的新媒体受众主要关注自身感兴趣的信息,而高级官员及政界人士(乌克兰及国际)对该受众群体而言辨识度较低。 H2:专业大众媒体(UP)的提及占比与被引用新闻来源占比均高于新媒体(US),因为新媒体将新闻视为娱乐内容,不遵循经典新闻写作的规范与传统——即纳入辨识度高的新闻制造者、引用来源等。 H3:专业大众媒体(UP)会同时提及个人与组织/机构,因其认为二者均具备新闻价值;与之相反,新媒体(Telegram频道US)则会发布关于热门人物的新闻,以吸引公众注意力、促进读者分享内容。 H4:在混合媒体系统中,高质量大众媒体倾向于选用可靠来源,而新媒体则无此硬性要求。因此,UP将更多使用可靠(官方)来源,与之形成对比的是,US的文本中会出现更多不可靠(非官方)来源。 ### 研究内容与方法 本研究采用计算机辅助方法开展新闻(标题)收集以及新闻制造者与新闻来源的识别工作,所用编程语言为Python。 通过requests模块从《乌克兰真理报》官网(https://Pravda.com.ua/archives/)采集2月24日至10月31日期间的新闻存档HTML页面,经bs4模块处理后共获取35577条新闻标题。 本研究将“同时提及人物姓氏与组织/机构名称”的对象定义为热门新闻制造者,通过re模块结合涵盖最具影响力的人物与组织/机构的词汇表,生成热门新闻制造者列表。 针对新闻来源的界定,针对UP采用三种最常见的引用类型: 1. 分号前的专有名词或缩写; 2. 破折号后的专有名词或缩写; 3. 引号与分号后的专有名词或缩写。 针对US,则先导出同期的聊天记录,其中第一条句子被视为新闻标题(共30784条),通常情况下该首句与标题一致。随后使用相同的词汇表与正则表达式来识别新闻制造者与新闻来源。
创建时间:
2023-01-10
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