媒体的多语种粗粒度政治立场分类：ChatGPT与Bard报纸的编辑路线
Multilingual Coarse Political Stance Classification of Media. The Editorial Line of a ChatGPT and Bard Newspaper
AI总结：
本研究构建了一个多语种新闻语料库来训练立场分类器，发现ChatGPT生成的新闻文章编辑路线随时间演变，且其立场在不同语言间存在差异。
摘要：
中立性难以实现，且在政治上是主观的。传统媒体通常采用一种编辑路线，其潜在读者可将其作为媒体偏见的指标。目前有几个平台根据新闻机构的政治偏见对其进行评级。编辑路线和评级有助于读者收集平衡的新闻观点。但在指令遵循语言模型出现之际，诸如撰写报纸文章等任务可以委派给计算机。在不强加带有偏见的人设的情况下，基于人工智能的新闻机构在偏见评级中会处于什么位置？在这项工作中，我们使用真实新闻机构的评级，创建了一个多语种新闻语料库，其中包含粗粒度的立场标注（左翼和右翼）以及自动提取的主题标注。我们表明，在此数据上训练的分类器能够识别英语、德语、西班牙语和加泰罗尼亚语中大多数未见报纸的编辑路线。然后，我们将分类器应用于由ChatGPT和Bard在不同时期用这4种语言撰写的101篇类似报纸的文章。我们观察到，与传统报纸类似，ChatGPT的编辑路线随时间演变，并且作为一个数据驱动的系统，生成文章的立场在不同语言之间存在差异。
英文摘要：
Neutrality is difficult to achieve and, in politics, subjective. Traditional media typically adopt an editorial line that can be used by their potential readers as an indicator of the media bias. Several platforms currently rate news outlets according to their political bias. The editorial line and the ratings help readers in gathering a balanced view of news. But in the advent of instruction-following language models, tasks such as writing a newspaper article can be delegated to computers. Without imposing a biased persona, where would an AI-based news outlet lie within the bias ratings? In this work, we use the ratings of authentic news outlets to create a multilingual corpus of news with coarse stance annotations (Left and Right) along with automatically extracted topic annotations. We show that classifiers trained on this data are able to identify the editorial line of most unseen newspapers in English, German, Spanish and Catalan. We then apply the classifiers to 101 newspaper-like articles written by ChatGPT and Bard in the 4 languages at different time periods. We observe that, similarly to traditional newspapers, ChatGPT editorial line evolves with time and, being a data-driven system, the stance of the generated articles differs among languages.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
