Sanchez Villegas, D., Mokaram, S. and Aletras, N. orcid.org/0000-0003-4285-1965 (Submitted: 2021) Analyzing online political advertisements. arXiv. (Submitted)
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 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 a linguistic analysis to study the characteristics of political ads discourse.
Metadata
Item Type: | Article |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © 2021 The Author(s). Available under the terms of the Creative Commons Attribution Licence (http://creativecommons.org/licenses/by/4.0). |
Dates: |
|
Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Funding Information: | Funder Grant number The Leverhulme Trust RPG-2020-148 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 20 May 2021 11:11 |
Last Modified: | 20 May 2021 11:11 |
Published Version: | https://arxiv.org/abs/2105.04047v1 |
Status: | Submitted |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:174344 |