  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.
