Online political advertising is a central aspect of modern election
campaigning for influencing public opinion. Computational analysis of political
ads is of utmost importance in political science to understand the
characteristics of digital campaigning. It is also important in computational
linguistics to study features of political discourse and communication on a
large scale. In this work, we present the first computational study on online
political ads with the aim to (1) infer the political ideology of an ad
sponsor; and (2) identify whether the sponsor is an official political party or
a third-party organization. We develop two new large datasets for the two tasks
consisting of ads from the U.S.. Evaluation results show that our approach that
combines textual and visual information from pre-trained neural models
outperforms a state-of-the-art method for generic commercial ad classification.
Finally, we provide an in-depth analysis of the limitations of our
best-performing models and linguistic analysis to study the characteristics of
political ads discourse.

本研究首次对美国的在线政治广告进行计算研究，通过结合预训练的神经模型对文本和视觉信息进行分析，实现了对广告赞助者的政治意识形态进行推测以及对广告赞助者的类型进行识别，研究结果表明该方法在分类模型方面具有更优越的性能，并进行了关于政治广告语境特征的深入分析和语言学分析。