标题： 政治偏见来源推文中的宣传分析 显示英文标题

标题： Analysis of Propaganda in Tweets From Politically Biased Sources

摘要： 新闻媒体以具有政治关联而广为人知，许多国家级媒体培养政治偏见以迎合不同的受众。 为这些新闻媒体工作的记者对其报道的新闻有重大影响。 在这项工作中，我们提出了一种方法来分析与受欢迎的新闻媒体有关的记者，在使用某种类似宣传的语言传播其偏见中的作用。 我们介绍了JMBX（Journalist Media Bias on X），这是一个系统收集和标注的1874条推文的数据集，这些推文来自Twitter（现在称为X）。 这些推文由来自10家新闻媒体的知名记者撰写，这些新闻媒体的政治偏见从极端左翼到极端右翼不等。 我们从数据中提取了几个见解，并得出结论，与那些隶属于具有温和政治倾向的媒体的记者相比，隶属于具有极端偏见媒体的记者更有可能在写作中使用类似宣传的语言。 我们通过OpenAI和Google的八种不同大型语言模型（LLM）进行了比较。 我们发现，与针对宣传检测进行微调的基于BERT的模型相比，大型语言模型在检测社交媒体和新闻文章中的宣传方面表现更好。 虽然使用大型语言模型（LLMs）的性能提升显著，但它们带来了可观的经济和环境成本。 这项研究分析了基于令牌使用的财务成本和环境影响，利用估算与LLM操作相关的碳排放的工具。 显示英文摘要

摘要： News outlets are well known to have political associations, and many national outlets cultivate political biases to cater to different audiences. Journalists working for these news outlets have a big impact on the stories they cover. In this work, we present a methodology to analyze the role of journalists, affiliated with popular news outlets, in propagating their bias using some form of propaganda-like language. We introduce JMBX(Journalist Media Bias on X), a systematically collected and annotated dataset of 1874 tweets from Twitter (now known as X). These tweets are authored by popular journalists from 10 news outlets whose political biases range from extreme left to extreme right. We extract several insights from the data and conclude that journalists who are affiliated with outlets with extreme biases are more likely to use propaganda-like language in their writings compared to those who are affiliated with outlets with mild political leans. We compare eight different Large Language Models (LLM) by OpenAI and Google. We find that LLMs generally performs better when detecting propaganda in social media and news article compared to BERT-based model which is fine-tuned for propaganda detection. While the performance improvements of using large language models (LLMs) are significant, they come at a notable monetary and environmental cost. This study provides an analysis of both the financial costs, based on token usage, and the environmental impact, utilizing tools that estimate carbon emissions associated with LLM operations.