This paper presents a pipeline with minimal human influence for scraping and
detecting bias on college newspaper archives. This paper introduces a framework
for scraping complex archive sites that automated tools fail to grab data from,
and subsequently generates a dataset of 14 student papers with 23,154 entries.
This data can also then be queried by keyword to calculate bias by comparing
the sentiment of a large language model summary to the original article. The
advantages of this approach are that it is less comparative than reconstruction
bias and requires less labelled data than generating keyword sentiment. Results
are calculated on politically charged words as well as control words to show
how conclusions can be drawn. The complete method facilitates the extraction of
nuanced insights with minimal assumptions and categorizations, paving the way
for a more objective understanding of bias within student newspaper sources.

该论文介绍了一种几乎没有人为干预的流程，用于抓取和检测大学报纸档案中的偏见。该论文介绍了一种框架，用于从自动化工具无法获取数据的复杂档案网站中进行抓取，并生成了 14 份学生论文的数据集，总共含有 23,154 个条目。该数据还可以通过关键词查询，通过比较大型语言模型摘要和原始文章的情绪来计算偏见。该方法具有较少的比较性，比重建偏见更少地需要有标签的数据。结果基于具有政治色彩的词汇以及控制词汇来计算，以展示如何得出结论。这种完整的方法有助于从学生报纸来源中提取细微的见解，且假设和分类最少，为更客观地理解偏见铺平了道路。