This research explores the potential of augmented Data Download Packages (aDDPs) as a novel approach to analyze digital trace data, using TikTok as a use case to demonstrate the broader applicability of the method. The study demonstrates how these data packages can be used in social science research to understand better user behavior, content consumption patterns, and the relationship between self-reported preferences and actual digital behavior.We introduce the concept of aDDPs, which extend the conventional Data Download Packages (DDPs) by augmenting the collected data with survey data, metadata, content data, and multimodal content embeddings, among other possibilities - rendering aDDPs an unprecedentedly rich data source for social science research. This work provides an overview and guidance on collecting, augmenting DDPs, and analyzing the resulting aDDPs.In a pilot study on 18 aDDPs, we use the combination of data components in aDDPs to facilitate research on user engagement behavior and content classification. We showcase the potential of the information breadth and depth that aDDPs depict by exploiting the combination of multimodal content embeddings, the users’ watch history, and survey data. To do so, we train and compare uni- and multimodal classifiers, classify the 18 aDDPs’ videos, and investigate the extent to which user engagement behavior impacts future content suggestions. Furthermore, we compare the users retrieved content with the users’ self-reported content consumption.