A Computational Framework for Auditing Targeted Advertising

• Márcio Silva UFMS
• Fabrício Benevenuto UFMG

DOI: https://doi.org/10.5753/webmedia_estendido.2025.16433

Resumo

Political sponsored content has become a powerful yet potentially harmful campaigning tool on Online Social Networks (OSNs). Concerned about its misuse during the 2018 Brazilian elections, we developed a computational framework to audit targeted political advertising. Using a browser extension, we collected ads from over 2,000 Facebook volunteers and trained a Convolutional Neural Network with word embeddings, evaluated against classical machine learning methods on a labeled dataset of 10,000 ads. Our findings reveal gaps in Facebook’s Ad Library and show that coordinated posts on Facebook and Twitter can amplify political messaging, underscoring the need for independent auditing systems.