论文标题
转发 - 伯特：使用语言特征和信息扩散在社交网络上的政治倾斜检测
Retweet-BERT: Political Leaning Detection Using Language Features and Information Diffusion on Social Networks
论文作者
论文摘要
鉴于社交媒体消费的增加，估计社交媒体使用者的政治倾向是一个具有挑战性，而且越来越紧迫的问题。我们介绍了retweet-bert，这是一个简单且可扩展的模型，以估算Twitter用户的政治倾向。 retweet-bert利用转发网络结构和用户配置文件描述中使用的语言。我们的假设源于具有类似意识形态的人的网络和语言学的模式。转发 - 伯特（Retweet-Bert）对其他最先进的基线展示了竞争性能，在最近的两个Twitter数据集（一个COVID-19数据集和2020年美国总统选举数据集）中，达到96％-97％的宏观F1。我们还执行手动验证，以验证培训数据中不在培训数据中的用户的转发性能。最后，在Covid-19的案例研究中，我们说明了Twitter上政治回声室的存在，并表明它主要存在于右倾的用户中。我们的代码是开源的，我们的数据已公开可用。
Estimating the political leanings of social media users is a challenging and ever more pressing problem given the increase in social media consumption. We introduce Retweet-BERT, a simple and scalable model to estimate the political leanings of Twitter users. Retweet-BERT leverages the retweet network structure and the language used in users' profile descriptions. Our assumptions stem from patterns of networks and linguistics homophily among people who share similar ideologies. Retweet-BERT demonstrates competitive performance against other state-of-the-art baselines, achieving 96%-97% macro-F1 on two recent Twitter datasets (a COVID-19 dataset and a 2020 United States presidential elections dataset). We also perform manual validation to validate the performance of Retweet-BERT on users not in the training data. Finally, in a case study of COVID-19, we illustrate the presence of political echo chambers on Twitter and show that it exists primarily among right-leaning users. Our code is open-sourced and our data is publicly available.
