Analyzing Online Political Advertisements
This work addresses the need for computational analysis in political science and computational linguistics to understand digital campaigning and political discourse, though it is incremental as it builds on existing methods with new datasets.
The study tackled the problem of analyzing online political advertisements by developing computational methods to infer the political ideology of ad sponsors and identify whether they are official political parties or third-party organizations, resulting in an approach that outperformed a state-of-the-art method for generic commercial ad classification.
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.