skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


  • NSF Public Access
  • Search Results
  • Analysis of associations between emotions and activities of drug users and their addiction recovery tendencies from social media posts using structural equation modeling
Title: Analysis of associations between emotions and activities of drug users and their addiction recovery tendencies from social media posts using structural equation modeling
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.  more » « less
Award ID(s):
1817239 2401096
PAR ID:
10286123
Author(s) / Creator(s):
;
Date Published:
Journal Name:
BMC bioinformatics
Volume:
21
Issue:
554
ISSN:
1471-2105
Page Range / eLocation ID:
1-38
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; (, 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. 
    more » « less
  2. ; ; ; (, Proceedings of the AAAI Conference on Human Computation and Crowdsourcing)
    Voluntary sharing of personal information is at the heart of user engagement on social media and central to platforms' business models. From the users' perspective, so-called self-disclosure is closely connected with both privacy risks and social rewards. Prior work has studied contextual influences on self-disclosure, from platform affordances and interface design to user demographics and perceived social capital. Our work takes a mixed-methods approach to understand the contextual information which might be integrated in the development of privacy-enhancing technologies. Through observational study of several Reddit communities, we explore the ways in which topic of discussion, group norms, peer effects, and audience size are correlated with personal information sharing. We then build and test a prototype privacy-enhancing tool that exposes these contextual factors. Our work culminates in a browser extension that automatically detects instances of self-disclosure in Reddit posts at the time of posting and provides additional context to users before they post to support enhanced privacy decision-making. We share this prototype with social media users, solicit their feedback, and outline a path forward for privacy-enhancing technologies in this space. 
    more » « less
  3. ; ; ; (, International Journal of Environmental Research and Public Health)
    Although several theories posit that information seeking is related to better psychological health, this logic may not apply to a pandemic like COVID-19. Given uncertainty inherent to the novel virus, we expect that information seeking about COVID-19 will be positively associated with emotional distress. Additionally, we consider the type of news media from which individuals receive information—television, newspapers, and social media—when examining relationships with emotional distress. Using a U.S. national survey, we examine: (1) the link between information seeking about COVID-19 and emotional distress, (2) the relationship between reliance on television, newspapers, and social media as sources for news and emotional distress, and (3) the interaction between information seeking and use of these news media sources on emotional distress. Our findings show that seeking information about COVID-19 was significantly related to emotional distress. Moreover, even after accounting for COVID-19 information seeking, consuming news via television and social media was tied to increased distress, whereas consuming newspapers was not significantly related to greater distress. Emotional distress was most pronounced among individuals high in information seeking and television news use, whereas the association between information seeking and emotional distress was not moderated by newspapers or social media news use. 
    more » « less
  4. ; ; (, Proceedings IEEE International Conference on Bioinformatics and Biomedicine Workshops)
    Opioid addiction constitutes a significant contemporary health crisis that is multifarious in its complexity. Modeling the epidemiology of any addiction is challenging in its own right. For opioid addiction, the challenge is exacerbated due to the difficulties in collecting real-time data and the circumscribed nature of information opioid users may disclose owing to stigma associated with prescription misuse. Given this context, identifying the progression of individuals through the stages of (opioid) addiction is one of the more acute problems in epidemiological modeling whose solution is crucial for designing specific interventions at both personal and population levels. We describe a computational approach for determining and characterizing addiction stages of opioid users from their social media posts. The proposed approach combines recurrent neural network learning with information-theoretic analysis of word-associations and context-based word embedding to determine addiction stage-specific language usage. Users who have a high likelihood for relapsing back to drug-use are identified and characterized using propensity score matching and logistic regression. Experimental evaluations indicate that the proposed approach can distinguish between various addiction stages and identify users prone to relapse with high accuracy as evidenced by F1 scores of 0.88 and 0.79 respectively 
    more » « less
  5. ; ; ; ; (, Journal of Medical Internet Research)
    BackgroundThe COVID-19 pandemic has resulted in heightened levels of depression, anxiety, and other mental health issues due to sudden changes in daily life, such as economic stress, social isolation, and educational irregularity. Accurately assessing emotional and behavioral changes in response to the pandemic can be challenging, but it is essential to understand the evolving emotions, themes, and discussions surrounding the impact of COVID-19 on mental health. ObjectiveThis study aims to understand the evolving emotions and themes associated with the impact of COVID-19 on mental health support groups (eg, r/Depression and r/Anxiety) on Reddit (Reddit Inc) during the initial phase and after the peak of the pandemic using natural language processing techniques and statistical methods. MethodsThis study used data from the r/Depression and r/Anxiety Reddit communities, which consisted of posts contributed by 351,409 distinct users over a period spanning from 2019 to 2022. Topic modeling and Word2Vec embedding models were used to identify key terms associated with the targeted themes within the data set. A range of trend and thematic analysis techniques, including time-to-event analysis, heat map analysis, factor analysis, regression analysis, and k-means clustering analysis, were used to analyze the data. ResultsThe time-to-event analysis revealed that the first 28 days following a major event could be considered a critical window for mental health concerns to become more prominent. The theme trend analysis revealed key themes such as economic stress, social stress, suicide, and substance use, with varying trends and impacts in each community. The factor analysis highlighted pandemic-related stress, economic concerns, and social factors as primary themes during the analyzed period. Regression analysis showed that economic stress consistently demonstrated the strongest association with the suicide theme, whereas the substance theme had a notable association in both data sets. Finally, the k-means clustering analysis showed that in r/Depression, the number of posts related to the “depression, anxiety, and medication” cluster decreased after 2020, whereas the “social relationships and friendship” cluster showed a steady decrease. In r/Anxiety, the “general anxiety and feelings of unease” cluster peaked in April 2020 and remained high, whereas the “physical symptoms of anxiety” cluster showed a slight increase. ConclusionsThis study sheds light on the impact of COVID-19 on mental health and the related themes discussed in 2 web-based communities during the pandemic. The results offer valuable insights for developing targeted interventions and policies to support individuals and communities in similar crises. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email