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.

通过自然语言处理技术以及其他已确立的自然语言处理方法，本文提出了一个综合框架，以分析在线新闻文本，特别是通过专门针对此目的进行训练的语言模型。该框架整合了十个新闻标准，用于评估新闻文章的质量，研究人员、媒体组织和读者可以更好地评估和理解他们所消费和产生的内容。然而，该方法存在一些限制，如难以检测微妙的偏见及与不断变化的语言模式保持步伐的连续更新需求。

数字时代维护新闻整体性的全面自然语言处理框架

Maintaining Journalistic Integrity in the Digital Age: A Comprehensive  NLP Framework for Evaluating Online News Content

Automatic article commenting is helpful in encouraging user engagement and
interaction on online news platforms. However, the news documents are usually
too long for traditional encoder-decoder based models, which often results in
general and irrelevant comments. In this paper, we propose to generate comments
with a graph-to-sequence model that models the input news as a topic
interaction graph. By organizing the article into graph structure, our model
can better understand the internal structure of the article and the connection
between topics, which makes it better able to understand the story. We collect
and release a large scale news-comment corpus from a popular Chinese online
news platform Tencent Kuaibao. Extensive experiment results show that our model
can generate much more coherent and informative comments compared with several
strong baseline models.

本研究提出了基于图到序列模型的评论生成方法，该模型将输入的新闻文章作为一个话题交互图进行建模，以更好地理解文章的内部结构和主题之间的联系，从而比传统编码器 - 解码器模型在在线新闻平台中为用户提供更连贯和有用的评论。