We study how political polarization is reflected in the social media posts used by media outlets to promote their content online. In particular, we track the Twitter posts of several media outlets over the course of more than three years (566K tweets), and the engagement with these tweets from other users (104M retweets), modeling the relationship between the tweet text and the political diversity of the audience. We build a tool that integrates our model and helps journalists craft tweets that are engaging to a politically diverse audience, guided by the model predictions. To test the real-world impact of the tool, we partner with the PBS documentary series Frontline and run a series of advertising experiments on Twitter. We find that in seven out of the ten experiments, the tweets selected by our model were indeed engaging to a more politically diverse audience, illustrating the effectiveness of our approach.
翻译:我们研究政治两极分化如何反映在媒体机构用来在网上宣传其内容的社交媒体文章中。特别是,我们追踪了三年多来(566K推特推特)几个媒体机构的推特文章,以及其他用户(104M retweets)对这些推特的接触,模拟了推特文本与受众政治多样性之间的关系。我们建立了一个工具,在模型预测的指导下,整合了我们的模型,帮助记者将推文带给不同政治受众。为了测试该工具对现实世界的影响,我们与PBS系列纪录片Frontline合作,在Twitter上进行了一系列广告实验。我们发现,在10个实验中,7个实验中,我们模型所选的推文确实吸引了政治上更多样化的受众,表明了我们方法的有效性。