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1. DUI: the drug use insights web server

   https://doi.org/10.1093/bioinformatics/btab461  

   Prince, Zachary; Jha, Deeptanshu; Singh, Rahul (June 2021, Bioinformatics)

   Wren, Jonathan (Ed.)

   Abstract Motivation Substance abuse constitutes one of the major contemporary health epidemics. Recently, the use of social media platforms has garnered interest as a novel source of data for drug addiction epidemiology. Often however, the language used in such forums comprises slang and jargon. Currently, there are no publicly available resources to automatically analyse the esoteric language-use in the social media drug-use sub-culture. This lacunae introduces critical challenges for interpreting, sensemaking and modeling of addiction epidemiology using social media. Results Drug-Use Insights (DUI) is a public and open-source web application to address the aforementioned deficiency. DUI is underlined by a hierarchical taxonomy encompassing 108 different addiction related categories consisting of over 9,000 terms, where each category encompasses a set of semantically related terms. These categories and terms were established by utilizing thematic analysis in conjunction with term embeddings generated from 7,472,545 Reddit posts made by 1,402,017 redditors. Given post(s) from social media forums such as Reddit and Twitter, DUI can be used foremost to identify constituent terms related to drug use. Furthermore, the DUI categories and integrated visualization tools can be leveraged for semantic- and exploratory analysis. To the best of our knowledge, DUI utilizes the largest number of substance use and recovery social media posts used in a study and represents the first significant online taxonomy of drug abuse terminology. Availability The DUI web server and source code are available at: http://haddock9.sfsu.edu/insight/ Supplementary information Supplementary data are available at Bioinformatics online. 

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2. Analysis of associations between emotions and activities of drug users and their addiction recovery tendencies from social media posts using structural equation modeling

   https://doi.org/0.1186/s12859-020-03893-9  

   Jha, Deeptanshu; Singh, Rahul (January 2021, BMC bioinformatics)

   null (Ed.)

   Background: Addiction to drugs and alcohol constitutes one of the significant factors underlying the decline in life expectancy in the US. Several context-specific reasons influence drug use and recovery. In particular emotional distress, physical pain, relationships, and self-development efforts are known to be some of the factors associated with addiction recovery. Unfortunately, many of these factors are not directly observable and quantifying, and assessing their impact can be difficult. Based on social media posts of users engaged in substance use and recovery on the forum Reddit, we employed two psycholinguistic tools, Linguistic Inquiry and Word Count and Empath and activities of substance users on various Reddit sub-forums to analyze behavior underlining addiction recovery and relapse. We then employed a statistical analysis technique called structural equation modeling to assess the effects of these latent factors on recovery and relapse. Results: We found that both emotional distress and physical pain significantly influence addiction recovery behavior. Self-development activities and social relationships of the substance users were also found to enable recovery. Furthermore, within the context of self-development activities, those that were related to influencing the mental and physical well-being of substance users were found to be positively associated with addiction recovery. We also determined that lack of social activities and physical exercise can enable a relapse. Moreover, geography, especially life in rural areas, appears to have a greater correlation with addiction relapse. Conclusions: The paper describes how observable variables can be extracted from social media and then be used to model important latent constructs that impact addiction recovery and relapse. We also report factors that impact self-induced addiction recovery and relapse. To the best of our knowledge, this paper represents the first use of structural equation modeling of social media data with the goal of analyzing factors influencing addiction recovery. 

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3. Calculating prescription rates and addiction probabilities for the four most commonly prescribed opioids and evaluating their impact on addiction using compartment modelling

   https://doi.org/10.1093/imammb/dqab001  

   Rivas, Samantha R; Tessner, Alex C; Goldwyn, Eli E (February 2021, Mathematical Medicine and Biology: A Journal of the IMA)

   null (Ed.)

   Abstract In 2016, more than 11 million Americans abused prescription opioids. The National Institute on Drug Abuse considers the opioid crisis a national addiction epidemic, as an increasing number of people are affected each year. Using the framework developed in mathematical modelling of infectious diseases, we create and analyse a compartmental opioid-abuse model consisting of a system of ordinary differential equations. Since $$40\%$$ of opioid overdoses are caused by prescription opioids, our model includes prescription compartments for the four most commonly prescribed opioids, as well as for the susceptible, addicted and recovered populations. While existing research has focused on drug abuse models in general and opioid models with one prescription compartment, no previous work has been done comparing the roles that the most commonly prescribed opioids have had on the crisis. By combining data from the Substance Abuse and Mental Health Services Administration (which tracked the proportion of people who used or misused one of the four individual opioids) with data from the Centers of Disease Control and Prevention (which counted the total number of prescriptions), we estimate prescription rates and probabilities of addiction for the four most commonly prescribed opioids. Additionally, we perform a sensitivity analysis and reallocate prescriptions to determine which opioid has the largest impact on the epidemic. Our results indicate that oxycodone prescriptions are both the most likely to lead to addiction and have the largest impact on the size of the epidemic, while hydrocodone prescriptions had the smallest impact. 

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4. Cyberbullying and Mental Health in Adults: The Moderating Role of Social Media Use and Gender

   https://doi.org/10.3389/fpsyt.2021.674298  

   Schodt, Kaitlyn B.; Quiroz, Selena I.; Wheeler, Brittany; Hall, Deborah L.; Silva, Yasin N. (July 2021, Frontiers in Psychiatry)

   null (Ed.)

   Previous research has identified a link between mental health and cyberbullying, primarily in studies of youth. Fewer studies have examined cyberbullying in adults or how the relation between mental health and cyberbullying might vary based on an individual's social media use. The present research examined how three indicators of mental health—depression, anxiety, and substance use—interact with social media use and gender to predict cyberbullying in adults. In Study 1, U.S. adults recruited through Amazon Mechanical Turk ( N = 525) completed an online survey that included measures of mental health and cyberbullying. Multiple regression analyses revealed significant three-way interactions between mental health, degree of social media use, and gender in models predicting cyberbullying victimization and perpetration. Specifically, for men, depression and anxiety predicted greater cyberbullying victimization and perpetration, particularly among men with relatively higher levels of social media use. In contrast, depression and anxiety were uncorrelated with cyberbullying for women, regardless of level of social media use. Study 2 largely replicated these findings using well-validated measures of mental health (e.g., Center for Epidemiological Studies-Depression scale, Beck Anxiety Inventory, Global Appraisal of Individual Needs Substance Use scale) in U.S. adults recruited through Prolific.co ( N = 482). Together, these results underscore the importance of examining mental health correlates of cyberbullying within the context of social media use and gender and shed light on conditions in which indicators of mental health may be especially beneficial for predicting cyberbullying in adults. 

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5. Social Identity, Social Media Use, and Mental Health in Adults: Investigating the Mediating Role of Cyberbullying Experiences and the Moderating Effects of Gender and Age

   https://doi.org/10.2147/PRBM.S466965  

   Barragan, Natali; Batista, Amanda; Hall, Deborah; Silva, Yasin (November 2024, Psychology Research and Behavior Management)

   Purpose: Previous research points to a complex relation between social media use and mental health, with open questions remaining with respect to mediation pathways and potential sociodemographic moderators. The present research investigated the extent to which experiences of cyberbullying victimization mediate the link between greater social media use and poorer mental health in adults and whether such indirect effects are moderated by gender or age. Participants and methods: As part of a larger study, US adults (N = 502) completed an online survey that included measures of degree of social media use, cyberbullying victimization, depression, anxiety, substance use, and sociodemographic characteristics including gender and age. Results: A series of moderated mediation models revealed a robust indirect effect of cyberbullying victimization on the relation between degree of social media use and mental health, such that greater social media use was associated with higher levels of cyberbullying victimization and greater cyberbullying victimization was associated with increased depression, anxiety, and likelihood of substance use. There was no evidence that the mediation effects varied between men and women. Age did, however, moderate the mediation effects for anxiety and likelihood of substance use, with stronger mediation effects emerging for younger compared to older adults. Conclusion: Our findings underscore the importance of empirical investigations that shed a more nuanced light on the complex relation between social media and mental health. 

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