Auditing Algorithmic Explanations of Social Media Feeds: A Case Study of TikTok Video Explanations - Mousavi, Sepehr; Gummadi, Krishna P.; Zannettou, Savvas (Proceedings of the International AAAI Conference on Web and Social Media, 2024)
In recent years, user feeds on social media platforms have shifted from simple, chronologically ordered content posted by their network connections (i.e., friends) to opaque, algorithmically ordered and curated content. This shift has led to regulations that require platforms to offer end users greater transparency and control over their algorithmic recommendation-based feeds. In response, social media platforms such as TikTok have recently started explaining why specific videos are recommended to end users. However, we still lack a good understanding of how these explanations are generated and whether they offer the desired transparency to end users. In this work, we audit explanations provided on short-format videos on TikTok. We collect a large dataset of short-format videos and explanations provided by TikTok (when available) using automated sockpuppet accounts. Then, we systematically characterize the explanations, focusing on their accuracy and comprehensiveness. For our assessments, we compare the provided explanations with video metadata and the behavior of our sockpuppet accounts. Our analysis shows that some generic (non-personalized) reasons are always included in explanations (e.g., "This video is popular in your country"), while at the same time, we find that a large number of provided explanations are incompatible with the behavior of our sockpuppet accounts; (e.g., an account that made zero comments on the platform, was presented with the explanation "You commented on similar videos" in 34% of all recommended videos.) Overall, our audit of TikTok video explanations highlights the need for more accurate, fine-grained, and useful explanations for the end users. We will make our code and dataset available to assist the research community.