There are various privacy-related functionalities in social media apps. For example, users of TikTok can upload videos that record their daily activities and specify which users can view these videos. Ensuring the correctness of these functionalities is crucial. Otherwise, it may threaten the users’ privacy or frustrate users. Due to the absence of appropriate automated testing techniques, manual testing remains the primary approach for validating these functionalities in the industrial setting, which is cumbersome, error-prone, and inadequate due to its small-scale validation. To this end, we adapt property-based testing to validate app behaviors against the properties described by the given privacy specifications. Our key idea is that privacy specifications maintained by testers written in natural language can be transformed into the Büchi automata, which can be used to determine whether the app has reached unexpected states as well as guide the test case generation. To support the application of our approach, we implemented an automated GUI testing tool, PDTDroid, which can detect the app behavior that is inconsistent with the checked privacy specifications. Our evaluation on TikTok, involving 125 real privacy specifications, shows that PDTDroid can efficiently validate privacy-related functionality and reduce manual effort by an average of 95.2% before each app release. Our further experiments on six popular social media apps show the generability and applicability of PDTDroid. During the evaluation, PDTDroid also found 22 previously unknown inconsistencies between the specification and implementation in these extensively tested apps (including four privacy leakage bugs, nine privacy-related functional bugs, and nine specification issues).