This paper describes a method for using Transformer-based Language Models (TLMs) to understand public opinion from social media posts. In this approach, we train a set of GPT models on several COVID-19 tweet corpora that reflect populations of users with distinctive views. We then use prompt-based queries to probe these models to reveal insights into the biases and opinions of the users. We demonstrate how this approach can be used to produce results which resemble polling the public on diverse social, political and public health issues. The results on the COVID-19 tweet data show that transformer language models are promising tools that can help us understand public opinions on social media at scale.

翻译：本文描述了使用基于变换语言模式(TLMs)从社交媒体文章中了解舆论的方法。在这种方法中,我们用若干COVID-19推文公司对一套GPT模式进行了培训,这些模式反映了具有不同观点的用户群体。然后,我们利用基于即时的询问来调查这些模式,以揭示对用户偏见和观点的洞察力。我们展示了如何利用这一方法产生类似公众在社会、政治和公共卫生问题上的民意调查结果。COVID-19推文数据的结果显示,变动语言模式是很有希望的工具,可以帮助我们理解大规模社会媒体上的公众意见。