Yunhao Yuan

Digital phenotyping leverages continuous data from smartphones and wearable devices for real-time mental health monitoring, offering opportunities for early detection and personalized care in mood disorders by enabling clinicians to proactively respond to significant changes before symptoms worsen. However, the heterogeneity of such data presents modeling challenges. This study evaluates the potential of large language models (LLMs) to detect changes in depression severity from digital phenotyping data among individuals experiencing major depressive episodes. We compare in-context learning and fine-tuning strategies and find that both few-shot prompted and fine-tuned LLMs outperform traditional baselines. Furthermore, embedding-only and QLoRA fine-tuning yield comparable results, with the former excelling on individual features and the latter performing better on combined inputs. These findings demonstrate the promise of LLMs in integrating heterogeneous behavioral data for mental health analysis, while underscoring the importance of clinical validation and ethical safeguards in real-world applications.

AI-powered companion chatbots (AICCs) such as Replika are increasingly popular, offering empathetic interactions, yet their psychosocial impacts remain unclear. We examined how engaging with AICCs shaped wellbeing and how users perceived these experiences. First, we conducted a large-scale quasi-experimental study of longitudinal Reddit data, applying stratified propensity score matching and Difference-in-Differences regression. Findings revealed mixed effects—greater grief expression and interpersonal focus, alongside increases in language about loneliness, depression, and suicidal ideation. Second, we complemented these results with 18 semi-structured interviews, which we thematically analyzed and contextualized using Knapp’s relationship development model. We identified trajectories of initiation, escalation, and bonding, wherein AICCs provided emotional validation and social rehearsal but also carried risks of over-reliance and withdrawal. Triangulating across methods, we offer design implications for AI companions that scaffold healthy boundaries, support mindful engagement, support disclosure without dependency, and surface relationship stages—maximizing psychosocial benefits while mitigating risks.

Digital phenotyping, which takes advantage of data continuously gathered from smartphones and wearable devices, offers promising avenues for real-time monitoring and mental health analysis. This approach holds promise for improving early detection and personalized care in mood disorders by enabling clinicians to proactively respond to significant changes before symptoms worsen. However, the complexity and heterogeneity of digital phenotyping data pose significant modeling challenges. Recent advances in large language models (LLMs) suggest their potential to generalize across diverse tasks with minimal labeled data, making them a promising alternative for analyzing data from digital phenotyping studies. However, the extent of usability of these methods for digital phenotyping studies is not yet well understood. In this study, we evaluate the potential of LLMs in analyzing digital phenotyping data to predict changes in depression severity among individuals experiencing major depressive episodes. We evaluate several in-context learning and fine-tuning strategies, and find that both few-shot prompted LLMs and fine-tuned models outperform traditional machine learning baselines trained on the same set of input features. Moreover, we compare two fine-tuning approaches (fine-tuning only the embedding layer versus parameter-efficient fine-tuning using QLoRA) and find that fine-tuning only the embedding layer significantly improves performance compared to QLoRA fine-tuning. These results highlight the capability of LLMs to process and integrate heterogeneous behavioral data, promising their application for digital phenotyping and mental health research. While our findings highlight the potential in using LLMs in mental health monitoring, their black-box nature and risk of replicating data biases highlight the need for clinical oversight and validation in real-world practice.

Background: Substance misuse presents significant global public health challenges. Understanding transitions between substance types and the timing of shifts to polysubstance use is vital to developing effective prevention and recovery strategies. The gateway hypothesis suggests that high-risk substance use is preceded by lower-risk substance use. However, the source of this correlation is hotly contested. While some claim that low-risk substance use causes subsequent, riskier substance use, most people using low-risk substances also do not escalate to higher-risk substances. Social media data hold the potential to shed light on the factors contributing to substance use transitions. Objective: By leveraging social media data, our study aimed to gain a better understanding of substance use pathways. By identifying and analyzing the transitions of individuals between different risk levels of substance use, our goal was to find specific linguistic cues in individuals’ social media posts that could indicate escalating or de-escalating patterns in substance use. Methods: We conducted a large-scale analysis using data from Reddit, collected between 2015 and 2019, consisting of over 2.29 million posts and approximately 29.37 million comments by around 1.4 million users from subreddits. These data, derived from substance use subreddits, facilitated the creation of a risk transition data set reflecting the substance use behaviors of over 1.4 million users. We deployed deep learning and machine learning techniques to predict the escalation or de-escalation transitions in risk levels, based on initial transition phases documented in posts and comments. We conducted a linguistic analysis to analyze the language patterns associated with transitions in substance use, emphasizing the role of n-gram features in predicting future risk trajectories. Results: Our results showed promise in predicting the escalation or de-escalation transition in risk levels, based on the historical data of Reddit users created on initial transition phases among drug-related subreddits, with an accuracy of 78.48% and an F1-score of 79.20%. We highlighted the vital predictive features, such as specific substance names and tools indicative of future risk escalations. Our linguistic analysis showed that terms linked with harm reduction strategies were instrumental in signaling de-escalation, whereas descriptors of frequent substance use were characteristic of escalating transitions. Conclusions: This study sheds light on the complexities surrounding the gateway hypothesis of substance use through an examination of web-based behavior on Reddit. While certain findings validate the hypothesis, indicating a progression from lower-risk substances such as marijuana to higher-risk ones, a significant number of individuals did not show this transition. The research underscores the potential of using machine learning with social media analysis to predict substance use transitions. Our results point toward future directions for leveraging social media data in substance use research, underlining the importance of continued exploration before suggesting direct implications for interventions.

BACKGROUND Substance misuse presents significant global public health challenges. Understanding transitions between substance types and the timing of shifts to polysubstance use is vital to developing effective prevention and recovery strategies. The gateway hypothesis suggests that high-risk substance use is preceded by lower-risk substance use. However, the source of this correlation is hotly contested. While some claim that low-risk substance use causes subsequent, riskier substance use, most people using low-risk substances also do not escalate to higher-risk substances. Social media data hold the potential to shed light on the factors contributing to substance use transitions. OBJECTIVE By leveraging social media data, our study aimed to gain a better understanding of substance use pathways. By identifying and analyzing the transitions of individuals between different risk levels of substance use, our goal was to find specific linguistic cues in individuals’ social media posts that could indicate escalating or de-escalating patterns in substance use. METHODS We conducted a large-scale analysis using data from Reddit, collected between 2015 and 2019, consisting of over 2.29 million posts and approximately 29.37 million comments by around 1.4 million users from subreddits. These data, derived from substance use subreddits, facilitated the creation of a risk transition data set reflecting the substance use behaviors of over 1.4 million users. We deployed deep learning and machine learning techniques to predict the escalation or de-escalation transitions in risk levels, based on initial transition phases documented in posts and comments. We conducted a linguistic analysis to analyze the language patterns associated with transitions in substance use, emphasizing the role of n-gram features in predicting future risk trajectories. RESULTS Our results showed promise in predicting the escalation or de-escalation transition in risk levels, based on the historical data of Reddit users created on initial transition phases among drug-related subreddits, with an accuracy of 78.48% and an F1-score of 79.20%. We highlighted the vital predictive features, such as specific substance names and tools indicative of future risk escalations. Our linguistic analysis showed that terms linked with harm reduction strategies were instrumental in signaling de-escalation, whereas descriptors of frequent substance use were characteristic of escalating transitions. CONCLUSIONS This study sheds light on the complexities surrounding the gateway hypothesis of substance use through an examination of web-based behavior on Reddit. While certain findings validate the hypothesis, indicating a progression from lower-risk substances such as marijuana to higher-risk ones, a significant number of individuals did not show this transition. The research underscores the potential of using machine learning with social media analysis to predict substance use transitions. Our results point toward future directions for leveraging social media data in substance use research, underlining the importance of continued exploration before suggesting direct implications for interventions.

The COVID-19 pandemic has disproportionately impacted the lives of minorities, such as members of the LGBTQ community (lesbian, gay, bisexual, transgender, and queer) due to pre-existing social disadvantages and health disparities. Although extensive research has been carried out on the impact of the COVID-19 pandemic on different aspects of the general population's lives, few studies are focused on the LGBTQ population. In this paper, we develop and evaluate two sets of machine learning classifiers using a pre-pandemic and a during-pandemic dataset to identify Twitter posts exhibiting minority stress, which is a unique pressure faced by the members of the LGBTQ population due to their sexual and gender identities. We demonstrate that our best pre- and during-pandemic models show strong and stable performance for detecting posts that contain minority stress. We investigate the linguistic differences in minority stress posts across pre- and during-pandemic periods. We find that anger words are strongly associated with minority stress during the COVID-19 pandemic. We explore the impact of the pandemic on the emotional states of the LGBTQ population by adopting propensity score-based matching to perform a causal analysis. The results show that the LGBTQ population have a greater increase in the usage of cognitive words and worsened observable attribute in the usage of positive emotion words than the group of the general population with similar pre-pandemic behavioral attributes. Our findings have implications for the public health domain and policy-makers to provide adequate support, especially with respect to mental health, to the LGBTQ population during future crises.

The COVID-19 pandemic has disproportionately impacted the lives of minorities, such as members of the LGBTQ community (lesbian, gay, bisexual, transgender, and queer) due to pre-existing social disadvantages and health disparities. Although extensive research has been carried out on the impact of the COVID-19 pandemic on different aspects of the general population's lives, few studies are focused on the LGBTQ population. In this paper, we develop and evaluate two sets of machine learning classifiers using a pre-pandemic and a during-pandemic dataset to identify Twitter posts exhibiting minority stress, which is a unique pressure faced by the members of the LGBTQ population due to their sexual and gender identities. We demonstrate that our best pre- and during-pandemic models show strong and stable performance for detecting posts that contain minority stress. We investigate the linguistic differences in minority stress posts across pre- and during-pandemic periods. We find that anger words are strongly associated with minority stress during the COVID-19 pandemic. We explore the impact of the pandemic on the emotional states of the LGBTQ population by adopting propensity score-based matching to perform a causal analysis. The results show that the LGBTQ population have a greater increase in the usage of cognitive words and worsened observable attribute in the usage of positive emotion words than the group of the general population with similar pre-pandemic behavioral attributes. Our findings have implications for the public health domain and policy-makers to provide adequate support, especially with respect to mental health, to the LGBTQ population during future crises.

The Papageno effect concerns how media can play a positive role in preventing and mitigating suicidal ideation and behaviors. With the increasing ubiquity and widespread use of social media, individuals often express and share lived experiences and struggles with mental health. However, there is a gap in our understanding about the existence and effectiveness of the Papageno effect in social media, which we study in this paper. In particular, we adopt a causal-inference framework to examine the impact of exposure to mental health coping stories on individuals on Twitter. We obtain a Twitter dataset with ∼ 2M posts by ∼ 10K individuals. We consider engaging with coping stories as the Treatment intervention, and adopt a stratified propensity score approach to find matched cohorts of Treatment and Control individuals. We measure the psychosocial shifts in affective, behavioral, and cognitive outcomes in longitudinal Twitter data before and after engaging with the coping stories. Our findings reveal that, engaging with coping stories leads to decreased stress and depression, and improved expressive writing, diversity, and interactivity. Our work discusses the practical and platform design implications in supporting mental wellbeing.

The Papageno effect concerns how media can play a positive role in preventing and mitigating suicidal ideation and behaviors. With the increasing ubiquity and widespread use of social media, individuals often express and share lived experiences and struggles with mental health. However, there is a gap in our understanding about the existence and effectiveness of the Papageno effect in social media, which we study in this paper. In particular, we adopt a causal-inference framework to examine the impact of exposure to mental health coping stories on individuals on Twitter. We obtain a Twitter dataset with $\sim$2M posts by $\sim$10K individuals. We consider engaging with coping stories as the Treatment intervention, and adopt a stratified propensity score approach to find matched cohorts of Treatment and Control individuals. We measure the psychosocial shifts in affective, behavioral, and cognitive outcomes in longitudinal Twitter data before and after engaging with the coping stories. Our findings reveal that, engaging with coping stories leads to decreased stress and depression, and improved expressive writing, diversity, and interactivity. Our work discusses the practical and platform design implications in supporting mental wellbeing.