Facebook Ads Monitor: An Independent Auditing System for Political Ads on Facebook
This addresses the risk of targeted advertising abuse in elections for policymakers and the public, though it is incremental as it applies existing methods to a new context.
The authors tackled the problem of monitoring political ads on Facebook to prevent abuse, as seen in the 2016 U.S. election, by deploying an independent auditing system in Brazil for the 2018 elections, using a CNN to detect political ads from over 2000 volunteers' data and identifying discrepancies with Facebook's own library.
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.