Analyzing Online Political Advertisements

TL;DR

This study introduces a computational approach to analyze online political ads, aiming to determine the sponsor's political ideology and organizational type using combined textual and visual data, outperforming existing methods.

Contribution

It presents the first large-scale datasets and models for classifying political ad ideology and sponsor type, integrating multimodal information for improved accuracy.

Findings

Our approach outperforms state-of-the-art commercial ad classification methods.

Textual and visual features combined enhance classification accuracy.

In-depth linguistic analysis reveals characteristics of political ad discourse.

Abstract

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.