Deric R. Kenne

Background: Early substance use during adolescence increases the risk for later substance use disorders and mental health problems, yet the real-time emotional and contextual factors underlying these behaviors remain poorly understood. Methods: We analyzed approximately 23,000 substance use–related posts and an equal number of non–substance use posts from Reddit’s r/teenagers subreddit (2018–2022). Posts were annotated for discrete emotions (sadness, anger, joy, guilt, fear, shame, and disgust) and contextual influences (family, peers, and school) using large language models (LLMs). Group differences were assessed statistically, emotion–context associations were visualized using heatmaps, and an interpretable XGBoost model identified key predictors of substance-use discussions. In addition, LLM-based thematic analysis extracted latent psychosocial themes linking emotions with contexts, providing qualitative insight into adolescents’ lived experiences. Results: Negative emotions, particularly sadness, guilt, fear, and disgust, were more prevalent in substance use posts, while joy predominated in non-substance use discussions. Guilt and shame showed distinct patterns: guilt often signaled regret and reparative intent, whereas shame reinforced risky dynamics through peer identity performance. Peer influence emerged as the strongest contextual driver, closely associated with sadness, fear, and guilt. In contrast, family and school environments displayed dual roles, acting as both risk and protective contexts depending on relational quality and stressors. Conclusions: Adolescent substance use reflects the interplay of sadness, guilt, and peer influence as central risk factors, with family and school contexts can operate as sources of both risk and protection.

Background: Personality traits are known predictors of substance use (SU), but their expression and association with SU in digital discourse remain largely unexamined. During the COVID-19 pandemic, the online social engagement heightened and led to an amplification in SU rates, thereby creating a unique natural opportunity to investigate these dynamics through large-scale digital discourse data. In our study, we offer insights beyond traditional self-report methods, which are crucial for developing timely and targeted public health interventions. Objective: We aim to evaluate whether the associations between the Big Five personality traits and SU discourse shifted during the 2019‐2021 period, and to conduct a focused analysis of how these traits predict SU and relate to specific substance types, emotional expression, and demographic factors. Methods: We analyzed a corpus of several hundred million public posts from a major social media platform from 2019 to 2021. Using a pipeline of natural language processing and deep learning models, we identified SU-related posts and subsequently extracted scores for the Big Five personality traits, emotions, and user demographics. We used trend analysis to compare annual shifts in trait-SU associations, while detailed 2020 data underwent rigorous modeling using logistic regression, correlation analysis, and topic modeling to elucidate the core relationships. Results: Our analysis revealed that Extraversion (odds ratio [OR] 3.22, 95% CI 2.98-3.49) and, most strikingly, agreeableness (OR 4.04, 95% CI 3.71-4.41) were the strongest positive predictors of being a substance user. In stark contrast to the conventional self-medication hypothesis, neuroticism emerged as a robust protective factor against SU (OR 0.29, 95% CI 0.26-0.31). This counterintuitive finding was supported by a decreased association between neuroticism and SU posts at the pandemic’s onset in 2020 (Cohen d =−0.13, 95% CI) and a negative correlation with the expression of negative emotions online. Topic modeling further indicated that SU discourse was frequently embedded in social contexts (social drinking and friendly beverage choices) rather than themes of solitary coping. Conclusions: Our findings challenge traditional models by demonstrating that in large-scale online discourse, SU expression is more powerfully linked to social-affiliative traits than to negative emotionality. The paradoxical protective role of neuroticism suggests that established risk profiles may not apply uniformly to digital environments, particularly during a public health crisis. These insights are vital for refining computational methods for public health surveillance and developing interventions that recognize the potent social drivers of SU in the digital age.

Background: User demographics are often hidden in social media data due to privacy concerns. However, demographic information on substance use (SU) can provide valuable insights, allowing public health policy makers to focus on specific cohorts and develop efficient prevention strategies, especially during global crises such as the COVID-19 pandemic. Objective: This study aimed to analyze SU trends at the user level across different demographic dimensions, such as age, gender, race, and ethnicity, with a focus on the COVID-19 pandemic. The study also establishes a baseline for SU trends using social media data. Methods: The study was conducted using large-scale English-language data from Twitter (now known as X) over a 3-year period (2019, 2020, and 2021), comprising 1.13 billion posts. Following preprocessing, the SU posts were identified using our custom-trained deep learning model (Robustly Optimized Bidirectional Encoder Representations From Transformers Pretraining Approach [RoBERTa]), which resulted in the identification of 9 million SU posts. Then, demographic attributes, such as user type, age, gender, race, and ethnicity, as well as sentiments and emotions associated with each post, were extracted via a collection of natural language processing modules. Finally, various qualitative analyses were performed to obtain insight into user behaviors based on demographics. Results: The highest level of user participation in SU discussions was observed in 2020, with a 22.18% increase compared to 2019 and a 25.24% increase compared to 2021. Throughout the study period, male users and teenagers increasingly dominated the SU discussions across all substance types. During the COVID-19 pandemic, user participation in prescription medication discussions was notably higher among female users compared to other substance types. In addition, alcohol use increased by 80% within 2 weeks after the global pandemic declaration in 2020. Conclusions: This study presents a large-scale, fine-grained analysis of SU on social media data, examining trends by age, gender, race, and ethnicity before, during, and after the COVID-19 pandemic. Our findings, contextualized with sociocultural and pandemic-specific factors, provide actionable insights for targeted public health interventions. This study establishes social media data (powered with artificial intelligence and natural language processing tools) as a valuable platform for real-time SU surveillance and prevention during crises.

BACKGROUND Personality traits are known predictors of substance use (SU), but their expression and association with SU during a global crisis remain largely unexamined at a population scale. The COVID-19 pandemic, which amplified both SU rates and online social engagement, created a unique natural experiment to investigate these dynamics through digital discourse. This approach offers insights beyond traditional self-report methods, which is crucial for developing timely and targeted public health interventions. OBJECTIVE To evaluate whether the associations between the Big Five personality traits and SU discourse shifted during the 2019–2021 period, and to conduct a focused analysis of how these traits predict SU and relate to specific substance types, emotional expression, and demographic factors. METHODS We analyzed a corpus of several hundred million public posts from a major social media platform from 2019 to 2021. Using a pipeline of natural language processing and deep learning models, we identified SU-related posts and subsequently extracted scores for the Big Five personality traits, emotions, and user demographics. We employed trend analysis to compare annual shifts in trait-SU associations, while detailed 2020 data underwent rigorous modeling using logistic regression, correlation analysis, and topic modeling to elucidate the core relationships. RESULTS Our analysis revealed that Extraversion (OR=3.22) and, most strikingly, Agreeableness (OR=4.04) were the strongest positive predictors of being a substance user. In stark contrast to the conventional self-medication hypothesis, Neuroticism emerged as a robust protective factor against SU (OR=0.29). This counterintuitive finding was supported by a decreased association between Neuroticism and SU posts at the pandemic's onset in 2020 (d=−0.13) and a negative correlation with the expression of negative emotions online. Topic modeling further indicated that SU discourse was frequently embedded in social contexts (Social Drinking, Friendly Beverage Choices) rather than themes of solitary coping. CONCLUSIONS Our findings challenge traditional models by demonstrating that in large-scale online discourse, SU expression is more powerfully linked to social-affiliative traits than to negative emotionality. The paradoxical protective role of Neuroticism suggests that established risk profiles may not apply uniformly to digital environments, particularly during a public health crisis. These insights are vital for refining computational methods for public health surveillance and developing interventions that recognize the potent social drivers of substance use in the digital age.

Background: The COVID-19 pandemic intensified the challenges associated with mental health and substance use (SU), with societal and economic upheavals leading to heightened stress and increased reliance on drugs as a coping mechanism. Centers for Disease Control and Prevention data from June 2020 showed that 13% of Americans used substances more frequently due to pandemic-related stress, accompanied by an 18% rise in drug overdoses early in the year. Simultaneously, a significant increase in social media engagement provided unique insights into these trends. Our study analyzed social media data from January 2019 to December 2021 to identify changes in SU patterns across the pandemic timeline, aiming to inform effective public health interventions. Objective: This study aims to analyze SU from large-scale social media data during the COVID-19 pandemic, including the prepandemic and postpandemic periods as baseline and consequence periods. The objective was to examine the patterns related to a broader spectrum of drug types with underlying themes, aiming to provide a more comprehensive understanding of SU trends during the COVID-19 pandemic. Methods: We leveraged a deep learning model, Robustly Optimized Bidirectional Encoder Representations from Transformers Pretraining Approach (RoBERTa), to analyze 1.13 billion Twitter (subsequently rebranded X) posts from January 2019 to December 2021, aiming to identify SU posts. The model’s performance was enhanced by a human-in-the-loop strategy that subsequently enriched the annotated data used during the fine-tuning phase. To gain insights into SU trends over the study period, we applied a range of statistical techniques, including trend analysis, k-means clustering, topic modeling, and thematic analysis. In addition, we integrated the system into a real-time application designed for monitoring and preventing SU within specific geographic locations. Results: Our research identified 9 million SU posts in the studied period. Compared to 2019 and 2021, the most substantial display of SU-related posts occurred in 2020, with a sharp 21% increase within 3 days of the global COVID-19 pandemic declaration. Alcohol and cannabinoids remained the most discussed substances throughout the research period. The pandemic particularly influenced the rise in nonillicit substances, such as alcohol, prescription medication, and cannabinoids. In addition, thematic analysis highlighted COVID-19, mental health, and economic stress as the leading issues that contributed to the influx of substance-related posts during the study period. Conclusions: This study demonstrates the potential of leveraging social media data for real-time detection of SU trends during global crises. By uncovering how factors such as mental health and economic stress drive SU spikes, particularly in alcohol and prescription medication, we offer crucial insights for public health strategies. Our approach paves the way for proactive, data-driven interventions that will help mitigate the impact of future crises on vulnerable populations.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: Recent advancements in Large Language Models (LLMs) have generated significant interest in their potential for assessing psychological constructs, particularly personality traits. While prior research has explored LLMs’ capabilities in zero-shot or few-shot personality inference, few studies have systematically evaluated LLM embeddings within a psychometric validity framework or examined their correlations with linguistic and emotional markers. Additionally, the comparative efficacy of LLM embeddings against traditional feature engineering methods remains underexplored, leaving gaps in understanding their scalability and interpretability for computational personality assessment. Objective: This study evaluates LLM embeddings for personality trait prediction through four key analyses: (1) performance comparison with zero-shot methods on PANDORA Reddit data, (2) psychometric validation and correlation with LIWC and emotion features, (3) benchmarking against traditional feature engineering approaches, and (4) assessment of model size effects (OpenAI vs BERT vs. RoBERTa). We aim to establish LLM embeddings as a psychometrically valid and efficient alternative for personality assessment. Methods: We conducted a multi-stage analysis using 1 million Reddit posts from the PANDORA Big Five Personality dataset. First, we generated text embeddings using three LLM architectures (RoBERTa, BERT, and OpenAI) and trained a custom BiLSTM model for personality prediction. We compared this approach against zero-shot inference using prompt-based methods. Second, we extracted psycholinguistic features (LIWC categories and NRC emotions) and performed feature engineering to evaluate potential performance enhancements. Third, we assessed the psychometric validity of LLM embeddings; reliability validity using Cronbach's alpha, and convergent validity analysis by examining correlations between embeddings and established linguistic markers. Finally, we performed traditional feature engineering on static psycholinguistic features to assess performance under different settings. Results: LLM embeddings trained using simple deep learning techniques significantly outperform zero-shot approaches on average by 45% across all personality traits. Although, psychometric validation tests indicate moderate reliability, with an average Cronbach’s alpha of 0.63, correlation analyses spark a strong association with key linguistic/emotional markers; such as openness correlates highly with social (r=0.53), conscientiousness with linguistic (r=0.46), extraversion with social (r=0.41), agreeableness with pronoun usage (r=0.40), and neuroticism with politics-related text (r=0.63). Despite adding advanced feature engineering on linguistic features, the performance didn’t improve, suggesting that LLM embeddings inherently capture key linguistic features. Furthermore, our analyses demonstrated efficacy on larger model size with computational cost tradeoff. Conclusions: Our findings demonstrate that LLM embeddings offer a robust alternative to zero-shot methods in personality trait analysis, capturing key linguistic patterns without requiring extensive feature engineering. The correlation with established psycholinguistic markers and the performance tradeoff with computational cost provides a hint for future computational linguistic work who are targeting LLM for personality assessment. Further research should explore fine-tuning strategies to enhance psychometric validity.

Background: Antidepressants are crucial for managing major depressive disorders; however, nonadherence remains a widespread challenge, driven by concerns over side effects, fear of dependency, and doubts about efficacy. Understanding patients’ experiences is essential for improving patient-centered care and enhancing adherence, which prioritizes individual needs in treatment. Objective: This study aims to gain a deeper understanding of patient experiences with antidepressants, providing insights that health care providers, families, and communities can develop into personalized treatment strategies. By integrating patient-centered care, these processes may improve satisfaction and adherence with antidepressants. Methods: Data were collected from AskaPatient and Reddit, analyzed using natural language processing and large language models. Analytical techniques included sentiment analysis, emotion detection, personality profiling, and topic modeling. Furthermore, demographic variations in patient experiences were also examined to offer a comprehensive understanding of discussions around antidepressants. Results: Sentiment and emotion analysis revealed that the majority of discussions (21,499/36,253, 59.3%) expressed neutral sentiments, with negative sentiments following closely (13,922/36,253, 38.4%). The most common emotions were fear (16,196/36,253, 44.66%) and sadness (12,507/36,253, 34.49%). The largest topic, “Mental Health and Relationships,” accounted for 11.69% (3755/36,253) of the discussions, which indicated a significant focus on managing mental health conditions. Discussions around nonadherence were marked by fear, followed by sadness, while self-care discussions showed a notable trend of sadness. Conclusions: These psychological insights into public perceptions of antidepressants provide a foundation for developing tailored, patient-centered treatment approaches that align with individual needs, enhancing both effectiveness and empathy of care.

Background: Adolescent substance use disorder is a pressing public health issue, with increasing prevalence as individuals age. Social media platforms like Reddit (Reddit Inc) serve as significant venues for teenagers to discuss and navigate substance use. Social media platforms, such as Reddit, serve as increasingly important spaces where teenagers discuss, share, and navigate their experiences with substance use, presenting unique opportunities and challenges for understanding and addressing this issue. Objective: This study aims to explore how teenagers frame substance-use discussions on the r/teenagers subreddit, focusing on their personal interpretations, causal attributions, and the social and psychological contexts that shape these online support groups. By identifying these interpretive frames, we aimed to better understand the complex drivers of adolescent substance use behavior and their potential interventions. Methods: Using natural language processing techniques, we analyzed 32,674 substance use–related posts from 2018 to 2022. A framing approach was used to identify and categorize prevalent themes, supplemented by emotional profiling using the EmoLLaMA-chat-13B model developed by Liu and colleagues. Results: In total, 7 primary frames emerged: normalization, risk awareness, social integration, autonomy and rebellion, coping mechanisms, media influence, and stigmatization. These frames varied in prevalence and were associated with distinct emotional profiles, highlighting the complex interplay between substance use and adolescent experiences. We observed that, for example, the normalization frame was often associated with a mix of sadness and anxiety, while the coping frame exhibited elevated levels of anger, sadness, and anxiety. These distinctive emotional landscapes associated with each frame reveal unique insights into the mental state of adolescents navigating substance use. Conclusions: The findings underscore the multifaceted nature of adolescent substance-use discussions on social media. Interventions must address underlying emotional and social factors as well as identity to effectively mitigate substance use disorder among adolescents. By understanding the frames teenagers use to interpret substance use, we can pave the way for more effective and personalized public health campaigns, and support services designed to resonate with adolescents’ unique lived experiences.

Background: The pervasiveness of drug culture has become evident in popular music and social media. Previous research has examined drug abuse content in both social media and popular music; however, to our knowledge, the intersection of drug abuse content in these 2 domains has not been explored. To address the ongoing drug epidemic, we analyzed drug-related content on Twitter (subsequently rebranded X), with a specific focus on lyrics. Our study provides a novel finding on the prevalence of drug abuse by defining a new subcategory of X content: “tweets that reference established drug lyrics.” Objective: We aim to investigate drug trends in popular music on X, identify and classify popular drugs, and analyze related artists’ gender, genre, and popularity. Based on the collected data, our goal is to create a prediction model for future drug trends and gain a deeper understanding of the characteristics of users who cite drug lyrics on X. Methods: X data were collected from 2015 to 2017 through the X streaming application programming interface (API). Drug lyrics were obtained from the Genius lyrics database using the Genius API based on drug keywords. The Smith-Waterman text-matching algorithm is used to detect the drug lyrics in posts. We identified famous drugs in lyrics that were posted. Consequently, the analysis was extended to related artists, songs, genres, and popularity on X. The frequency of drug-related lyrics on X was aggregated into a time-series, which was then used to create prediction models using linear regression, Facebook Prophet, and NIXTLA TimeGPT-1. In addition, we analyzed the number of followers of users posting drug-related lyrics to explore user characteristics. Results: We analyzed over 1.97 billion publicly available posts from 2015 to 2017, identifying more than 157 million that matched drug-related keywords. Of these, 150,746 posts referenced drug-related lyrics. Cannabinoids, opioids, stimulants, and hallucinogens were the most cited drugs in lyrics on X. Rap and hip-hop dominated, with 91.98% of drug-related lyrics from these genres and 84.21% performed by male artists. Predictions from all 3 models, linear regression, Facebook Prophet, and NIXTLA TimeGPT-1, indicate a slight decline in the prevalence of drug-related lyrics on X over time. Conclusions: Our study revealed 2 significant findings. First, we identified a previously unexamined subset of drug-related content on X: drug lyrics, which could play a critical role in models predicting the surge in drug-related incidents. Second, we demonstrated the use of cutting-edge time-series forecasting tools, including Facebook Prophet and NIXTLA TimeGPT-1, in accurately predicting these trends. These insights contribute to our understanding of how social media shapes public behavior and sentiment toward drug use.

BACKGROUND User demographics are often hidden in social media data due to privacy concerns. However, demographic information on substance use (SU) can provide valuable insights, allowing public health policy makers to focus on specific cohorts and develop efficient prevention strategies, especially during global crises such as the COVID-19 pandemic. OBJECTIVE This study aimed to analyze SU trends at the user level across different demographic dimensions, such as age, gender, race, and ethnicity, with a focus on the COVID-19 pandemic. The study also establishes a baseline for SU trends using social media data. METHODS The study was conducted using large-scale English-language data from Twitter (now known as X) over a 3-year period (2019, 2020, and 2021), comprising 1.13 billion posts. Following preprocessing, the SU posts were identified using our custom-trained deep learning model (Robustly Optimized Bidirectional Encoder Representations From Transformers Pretraining Approach [RoBERTa]), which resulted in the identification of 9 million SU posts. Then, demographic attributes, such as user type, age, gender, race, and ethnicity, as well as sentiments and emotions associated with each post, were extracted via a collection of natural language processing modules. Finally, various qualitative analyses were performed to obtain insight into user behaviors based on demographics. RESULTS The highest level of user participation in SU discussions was observed in 2020, with a 22.18% increase compared to 2019 and a 25.24% increase compared to 2021. Throughout the study period, male users and teenagers increasingly dominated the SU discussions across all substance types. During the COVID-19 pandemic, user participation in prescription medication discussions was notably higher among female users compared to other substance types. In addition, alcohol use increased by 80% within 2 weeks after the global pandemic declaration in 2020. CONCLUSIONS This study presents a large-scale, fine-grained analysis of SU on social media data, examining trends by age, gender, race, and ethnicity before, during, and after the COVID-19 pandemic. Our findings, contextualized with sociocultural and pandemic-specific factors, provide actionable insights for targeted public health interventions. This study establishes social media data (powered with artificial intelligence and natural language processing tools) as a valuable platform for real-time SU surveillance and prevention during crises.