The rapid growth of online news platforms has led to an increased need for reliable methods to evaluate the quality and credibility of news articles. This paper proposes a comprehensive framework to analyze online news texts using natural language processing (NLP) techniques, particularly a language model specifically trained for this purpose, alongside other well-established NLP methods. The framework incorporates ten journalism standards-objectivity, balance and fairness, readability and clarity, sensationalism and clickbait, ethical considerations, public interest and value, source credibility, relevance and timeliness, factual accuracy, and attribution and transparency-to assess the quality of news articles. By establishing these standards, researchers, media organizations, and readers can better evaluate and understand the content they consume and produce. The proposed method has some limitations, such as potential difficulty in detecting subtle biases and the need for continuous updating of the language model to keep pace with evolving language patterns.

翻译：随着在线新闻平台的迅速发展，对可靠方法以评估新闻文章质量与可信度的需求日益增长。本文提出一个综合框架，利用自然语言处理（NLP）技术（特别是专门为此目的训练的语言模型，以及其它成熟的NLP方法）分析在线新闻文本。该框架纳入十项新闻学标准——客观性、平衡与公正、可读性与清晰度、煽情与标题党、伦理考量、公共利益与价值、来源可信度、相关性与时效性、事实准确性、归因与透明度——以评估新闻文章质量。通过确立这些标准，研究人员、媒体组织和读者能更好地评估和理解他们所消费和制作的内容。所提出的方法存在一定局限，例如在检测微妙偏见方面可能存在困难，以及语言模型需持续更新以跟上语言模式演变。