Social media data has been mined over the years to predict individual sensitive attributes such as political and religious beliefs. Indeed, mining such data can improve the user experience with personalization and freemium services. Still, it can also be harmful and discriminative when used to make critical decisions, such as employment. In this work, we investigate social media privacy protection against attribute inference attacks using machine learning explainability and adversarial defense strategies. More precisely, we propose FOX (FOoling with eXplanations), an adversarial attack framework to explain and fool sensitive attribute inference models by generating effective adversarial reactions. We evaluate the performance of FOX with other SOTA baselines in a black-box setting by attacking five gender attribute classifiers trained on Facebook pictures reactions, specifically (i) comments generated by Facebook users excluding the picture owner, and (ii) textual tags (i.e., alttext) generated by Facebook. Our experiments show that FOX successfully fools (about 99.7% and 93.2% of the time) the classifiers, outperforms the SOTA baselines and gives a good transferability of adversarial features.