把校报建成高校新闻专业实习基地的思考

新闻学是一门实践性极强的学科,近年来,不少高校竞相开设新闻专业,新闻学教育的快速发展带来多方面的问题,学生实习难也日渐显现.新闻学教育应与高校校报加强合作,把校报打造成本校新闻专业学生的实习基地,可以在一定程度上缓解新闻专业学生实习难的现状.     

欧洲足球五大联赛电视转播权的国际政治经济学分析

欧洲足球五大联赛是全球足球市场运作最成功的品牌赛事之一,在全球足球电视转播权市场上具有举足轻重的地位。在国际政治经济学(IPE)的理论分析框架下,欧洲足球五大联赛电视转播权在资本利益边界、全球生产规制和单边霸权逻辑上呈现追逐利润最大化、满足多元利益主体和"价高者得"等IPE症候。欧洲足球五大联赛电视转播权聚集了大量的垄断资本,在资本逐利下有着强烈的利益冲动。在文化工业的全球生产机制影响下,欧洲足球五大联赛电视转播权以联赛价值和市场需要为商业逻辑,谋求联赛商业利益最大化。五大联赛电视转播权的全球化是传播欧洲足球理念和竞技价值的意识形态扩散化过程,在全球足球电视转播权市场上确立了赛事传播的霸权地位。     

新闻出版公共服务建设工程的物流战略与物流能力

新闻出版公共服务建设工程从营销网点建设、节约成本、提升服务能力出发,需要重视实施物流战略和规划;经营性出版发行中盘成为建设工程的主要物流服务提供商,政府给予补助,要加强建设工程的物流项目招标与管理,依托数字技术,建立物流联盟;建设工程要从六个维度考察第三方物流企业的服务能力。     

“凤凰”金牌主播吴小莉和她的舞台

香港凤凰卫视开播十周年了。十年来,她培养出一批金牌记者和节目主持人．吴小莉就是其中的一个。一批金牌主持人的崛起．使得凤凰台在世界华文媒体中声誉鹊起．取得了华人视角的世界话语权。吴小莉热爱祖国,勤奋聪慧．颇有人缘。采访新闻时深入现场,善解人意．具有独特的新闻人风格,深受观众喜爱。“凤凰”起飞和吴小莉的成功可资大陆媒体人借鉴。     

中国新闻周刊的生存状况与发展路径

中国大陆地区的新闻类周刊1990年代以来取得长足发展,也面临新的挑战与危机。“生逢其时”的社会环境与“生不逢时”的媒体竞争环境,使其努力寻找更为适合自己的生存路径,包括对“刊”形态与“周”时态的重新认识,对“忙人”与“有影响力”的人的重新界定,对“品质”与“风格”的不同选择,以及从“品牌化生存”到“两栖化生存”的拓展,以期借助于其他类型传播媒体的同步传播或“二次传播,”形成品牌,扩大影响,牢牢站稳属于自己的一片媒体空间。     

Automated monitoring of online news accuracy with change classification models

In the past decade, news consumption has shifted from printed news media to online alternatives. Although these come with advantages, online news poses challenges as well. Notable here is the increased competition between online newspapers and other online news providers to attract readers. Hereby, speed is often favored over quality. As a consequence, the need for new tools to monitor online news accuracy has grown. In this work, a fundamentally new and automated procedure for the monitoring of online news accuracy is proposed. The approach relies on the fact that online news articles are often updated after initial publication, thereby also correcting errors. Automated observation of the changes being made to online articles and detection of the errors that are corrected may offer useful insights concerning news accuracy. The potential of the presented automated error correction detection model is illustrated by building supervised classification models for the detection of objective, subjective and linguistic errors in online news updates respectively. The models are built using a large news update data set being collected during two consecutive years for six different Flemish online newspapers. A subset of 21,129 changes is then annotated using a combination of automated and human annotation via an online annotation platform. Finally, manually crafted features and text embeddings obtained by four different language models (TF-IDF, word2vec, BERTje and SBERT) are fed to three supervised machine learning algorithms (logistic regression, support vector machines and decision trees) and performance of the obtained models is subsequently evaluated. Results indicate that small differences in performance exist between the different learning algorithms and language models. Using the best-performing models, $F_2$-scores of 0.45, 0.25 and 0.80 are obtained for the classification of objective, subjective and linguistic errors respectively.