  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.
