# Analyzing Online Political Advertisements

arXiv: https://arxiv.org/abs/2105.04047 · HTML: https://mlsanity.com/paper/2105.04047/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 74 references that are in the map.
- First posted on arXiv 9 May 2021.

## Builds on (most cited first, 24 of 24)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [HuggingFace's Transformers: State-of-the-art Natural Language Processing](https://mlsanity.com/paper/1910.03771/) (2019)
- [Self-training with Noisy Student improves ImageNet classification](https://mlsanity.com/paper/1911.04252/) (2019)
- [DenseCap: Fully Convolutional Localization Networks for Dense Captioning](https://mlsanity.com/paper/1511.07571/) (2015)
- [Supervised Multimodal Bitransformers for Classifying Images and Text](https://mlsanity.com/paper/1909.02950/) (2019)
- [Automatic Understanding of Image and Video Advertisements](https://mlsanity.com/paper/1707.03067/) (2017)
- [Multi-view Models for Political Ideology Detection of News Articles](https://mlsanity.com/paper/1809.03485/) (2018)
- [Multi-Task Ordinal Regression for Jointly Predicting the Trustworthiness and the Leading Political Ideology of News Media](https://mlsanity.com/paper/1904.00542/) (2019)
- [Acquiring Background Knowledge to Improve Moral Value Prediction](https://mlsanity.com/paper/1709.05467/) (2017)
- [ADVISE: Symbolism and External Knowledge for Decoding Advertisements](https://mlsanity.com/paper/1711.06666/) (2017)
- [Equal But Not The Same: Understanding the Implicit Relationship Between Persuasive Images and Text](https://mlsanity.com/paper/1807.08205/) (2018)
- [Analyzing Political Parody in Social Media](https://mlsanity.com/paper/2004.13878/) (2020)
- [M2P2: Multimodal Persuasion Prediction using Adaptive Fusion](https://mlsanity.com/paper/2006.11405/) (2020)
- [Topic-Specific Sentiment Analysis Can Help Identify Political Ideology](https://mlsanity.com/paper/1810.12897/) (2018)
- [Understanding the Political Ideology of Legislators from Social Media Images](https://mlsanity.com/paper/1907.09594/) (2019)
- [Story Understanding in Video Advertisements](https://mlsanity.com/paper/1807.11122/) (2018)
- [Beyond Visual Semantics: Exploring the Role of Scene Text in Image Understanding](https://mlsanity.com/paper/1905.10622/) (2019)
- [Persuasive Faces: Generating Faces in Advertisements](https://mlsanity.com/paper/1807.09882/) (2018)
- [Image as Data: Automated Visual Content Analysis for Political Science](https://mlsanity.com/paper/1810.01544/) (2018)
- [Understanding Visual Ads by Aligning Symbols and Objects using Co-Attention](https://mlsanity.com/paper/1807.01448/) (2018)
- [An Analysis of United States Online Political Advertising Transparency](https://mlsanity.com/paper/1902.04385/) (2019)

## Built on by (most cited first, 11 of 11)

- [Detecting Dementia from Speech and Transcripts using Transformers](https://mlsanity.com/paper/2110.14769/) (2021)
- [Point-of-Interest Type Prediction using Text and Images](https://mlsanity.com/paper/2109.00602/) (2021)
- [Combining Humor and Sarcasm for Improving Political Parody Detection](https://mlsanity.com/paper/2205.03313/) (2022)
- [Identifying and Characterizing Active Citizens who Refute Misinformation in Social Media](https://mlsanity.com/paper/2204.10080/) (2022)
- [PAR: Political Actor Representation Learning with Social Context and Expert Knowledge](https://mlsanity.com/paper/2210.08362/) (2022)
- [Context-aware attention layers coupled with optimal transport domain adaptation and multimodal fusion methods for recognizing dementia from spontaneous speech](https://mlsanity.com/paper/2305.16406/) (2023)
- [Looking Beyond the Obvious: A Survey on Abstract Concept Recognition for Video Understanding](https://mlsanity.com/paper/2508.20765/) (2025)
- [A Multimodal Analysis of Influencer Content on Twitter](https://mlsanity.com/paper/2309.03064/) (2023)
- [Improving Multimodal Classification of Social Media Posts by Leveraging Image-Text Auxiliary Tasks](https://mlsanity.com/paper/2309.07794/) (2023)
- [On VLMs for Diverse Tasks in Multimodal Meme Classification](https://mlsanity.com/paper/2505.20937/) (2025)
- [Alignment Helps Make the Most of Multimodal Data](https://mlsanity.com/paper/2405.08454/) (2024)

## Abstract (arXiv)

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.