# Facebook Ads Monitor: An Independent Auditing System for Political Ads on Facebook

arXiv: https://arxiv.org/abs/2001.10581 · HTML: https://mlsanity.com/paper/2001.10581/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jan 2020.

## Built on by (most cited first, 9 of 9)

- [Problematic Machine Behavior: A Systematic Literature Review of Algorithm Audits](https://mlsanity.com/paper/2102.04256/) (2021)
- [An Audit of Misinformation Filter Bubbles on YouTube: Bubble Bursting and Recent Behavior Changes](https://mlsanity.com/paper/2203.13769/) (2022)
- [Auditing of AI: Legal, Ethical and Technical Approaches](https://mlsanity.com/paper/2407.06235/) (2024)
- [Auditing YouTube's Recommendation Algorithm for Misinformation Filter Bubbles](https://mlsanity.com/paper/2210.10085/) (2022)
- [Weakly Supervised Learning for Analyzing Political Campaigns on Facebook](https://mlsanity.com/paper/2210.10669/) (2022)
- [Discovering Latent Themes in Social Media Messaging: A Machine-in-the-Loop Approach Integrating LLMs](https://mlsanity.com/paper/2403.10707/) (2024)
- [Post-hoc Study of Climate Microtargeting on Social Media Ads with LLMs: Thematic Insights and Fairness Evaluation](https://mlsanity.com/paper/2410.05401/) (2024)
- [P2NIA: Privacy-Preserving Non-Iterative Auditing](https://mlsanity.com/paper/2504.00874/) (2025)
- [Mitigating Leakage from Data Dependent Communications in Decentralized Computing using Differential Privacy](https://mlsanity.com/paper/2112.12411/) (2021)

## Abstract (arXiv)

The 2016 United States presidential election was marked by the abuse of targeted advertising on Facebook. Concerned with the risk of the same kind of abuse to happen in the 2018 Brazilian elections, we designed and deployed an independent auditing system to monitor political ads on Facebook in Brazil. To do that we first adapted a browser plugin to gather ads from the timeline of volunteers using Facebook. We managed to convince more than 2000 volunteers to help our project and install our tool. Then, we use a Convolution Neural Network (CNN) to detect political Facebook ads using word embeddings. To evaluate our approach, we manually label a data collection of 10k ads as political or non-political and then we provide an in-depth evaluation of proposed approach for identifying political ads by comparing it with classic supervised machine learning methods. Finally, we deployed a real system that shows the ads identified as related to politics. We noticed that not all political ads we detected were present in the Facebook Ad Library for political ads. Our results emphasize the importance of enforcement mechanisms for declaring political ads and the need for independent auditing platforms.