Adolescence is a critical developmental period marked by heightened emotional sensitivity, social stress, and vulnerability to substance use. However, traditional research methods provide limited access to adolescents' authentic experiences, hindering efforts to develop evidence-based prevention and intervention strategies. Social media provides a unique opportunity to observe adolescents' naturally occurring discussions about substance use, offering valuable insights into their opinions, emotions, and lived experiences that can inform early prevention and intervention strategies. In this study, we analyze large-scale Reddit discussions related to substance use among adolescents between 2018 and 2023. Leveraging hour-by-day temporal analysis, sentiment and emotion classification, and transformer-based topic modeling (BERTopic), we examine the interaction between time, emotion, and semantic content in adolescent substance use discourse. Our findings reveal pronounced weekend and late-night peaks in substance-related discussions, a dominance of negative emotions such as sadness and fear, and distinct semantic topics centered on peer relationships, family conflict, emotional distress, and substance-specific experiences. These findings advance our understanding of adolescent substance use in naturalistic online settings and provide empirical evidence to support the development of more timely, targeted, and evidence-based prevention and intervention strategies.
Time series foundation models (TS-FMs) aim to learn generalizable temporal representations that can be adapted to a wide range of downstream tasks. In real-world multimodal settings, time series are frequently affected by temporal misalignment and partial modality missingness, where different modalities are observed at heterogeneous time scales or are partially absent. Existing approaches typically rely on naive imputation or masking strategies, which fail to account for cross-modal dependencies and often lead to misaligned or degraded representations. We propose TRACE, a conditional estimation paradigm for multimodal time series foundation model pipelines under missingness and irregular sampling, allowing incomplete target modalities to be systematically inferred from available auxiliary modalities. We evaluate TRACE on diverse multimodal benchmarks spanning healthcare and affective computing, including the MIMIC-IV clinical dataset and the CMU-MOSI and CMU-MOSEI benchmarks for multimodal sentiment analysis. Across a range of downstream prediction tasks and missing-modality settings, TRACE consistently outperforms prior multimodal fusion approaches, demonstrating improved robustness to severe modality missingness and more reliable cross-modal representations.