Identifying individuals amenable to drug recovery interventions through computational analysis of addiction content in social media

Eshleman, Ryan; Jha, Deeptanshu; Singh, Rahul

doi:10.1109/bibm.2017.8217766

Cited by 20 publications

(20 citation statements)

References 13 publications

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“…Drug addiction recovery has been the focus of far fewer works. Among the latter, in our previous work Eshleman et al [20], random forests were used with subreddit activity as features to identify users open to addiction recovery interventions in a predictive setting. The Gini impurity criterion, which measures how often a random element from a set would be labeled incorrectly if labeled according to the distribution of labels in the set, was used to rank the different subreddits on the basis of their importance.…”

Section: Prior Workmentioning

confidence: 99%

“…This analysis found correlations amongst subreddit categories, such as, mental health, spirituality, and relationships with addiction recovery behavior. The SEM model in the current work was developed using two latent variables-"relationships" and "mental and physical well-being", both of which were directly inspired by findings reported in [20]. In particular, we used user activity in the following subreddits: "relationships", "rela-tionship_advice", "parenting", and "childfree" to reflect the latent variable "relationships".…”

Section: Prior Workmentioning

confidence: 99%

“…We used a set of 117 recreational drug use (RDU) subreddits, and 29 drug addiction recovery (DAR) subreddits reported in our prior works to identify users discussing drug use and recovery on Reddit [20,45]. In [20] we had utilized the word2vec algorithm [46] to create a term embedding space. In this space related terms were grouped using an iterative set expansion technique to construct drug-use and addiction-recovery lexicons.…”

Section: Data Source and Participantsmentioning

confidence: 99%

“…In this paper, we have proposed the use of structural equation modeling (SEM)-a multivariate latent variable modeling technique to estimate critical latent constructs (italicized hereafter) such as emotional distress, physical pain, self-development, and relationships by analyzing social media activities of substance users. Social media has generated recent interest as a novel source of information in drug abuse epidemiology [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25]. Being semi-anonymous, social media consists of unfiltered and self-reported conversations and activities of an individual.…”

Section: Introductionmentioning

confidence: 99%

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

Jha

Singh

2020

BMC Bioinformatics

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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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Section: Prior Workmentioning

confidence: 99%

Section: Prior Workmentioning

confidence: 99%

Section: Data Source and Participantsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Jha

Singh

2020

BMC Bioinformatics

Self Cite

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“…Indeed, social media, such as Twitter, can serve as data sources for approaches that automatically detect opioid addicts and support a better practice of opioid addiction, prevention, and treatment [ 12 ]. Several studies have used social media as sources of input data to identify individuals amenable to drug recovery interventions [ 13 ] and used text mining to examine and compare discussion topics on social media communities to discover the thematic similarity, difference, and membership in online mental health communities [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

Social Media Text Mining Framework for Drug Abuse: Development and Validation Study With an Opioid Crisis Case Analysis

Nasralah¹,

El‐Gayar²,

Wang³

2020

J Med Internet Res

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Background Social media are considered promising and viable sources of data for gaining insights into various disease conditions and patients’ attitudes, behaviors, and medications. They can be used to recognize communication and behavioral themes of problematic use of prescription drugs. However, mining and analyzing social media data have challenges and limitations related to topic deduction and data quality. As a result, we need a structured approach to analyze social media content related to drug abuse in a manner that can mitigate the challenges and limitations surrounding the use of such data. Objective This study aimed to develop and evaluate a framework for mining and analyzing social media content related to drug abuse. The framework is designed to mitigate challenges and limitations related to topic deduction and data quality in social media data analytics for drug abuse. Methods The proposed framework started with defining different terms related to the keywords, categories, and characteristics of the topic of interest. We then used the Crimson Hexagon platform to collect data based on a search query informed by a drug abuse ontology developed using the identified terms. We subsequently preprocessed the data and examined the quality using an evaluation matrix. Finally, a suitable data analysis approach could be used to analyze the collected data. Results The framework was evaluated using the opioid epidemic as a drug abuse case analysis. We demonstrated the applicability of the proposed framework to identify public concerns toward the opioid epidemic and the most discussed topics on social media related to opioids. The results from the case analysis showed that the framework could improve the discovery and identification of topics in social media domains characterized by a plethora of highly diverse terms and lack of a commonly available dictionary or language by the community, such as in the case of opioid and drug abuse. Conclusions The proposed framework addressed the challenges related to topic detection and data quality. We demonstrated the applicability of the proposed framework to identify the common concerns toward the opioid epidemic and the most discussed topics on social media related to opioids.

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Detection of Facebook Addiction Using Machine Learning

Islam

Jannat

Habib

et al. 2022

Third International Conference on Image Processing and Capsule Networks

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Identifying individuals amenable to drug recovery interventions through computational analysis of addiction content in social media

Cited by 20 publications

References 13 publications

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

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

Social Media Text Mining Framework for Drug Abuse: Development and Validation Study With an Opioid Crisis Case Analysis

Detection of Facebook Addiction Using Machine Learning

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