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

Abstract

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.

Investigating Substance Use via Reddit: Systematic Scoping Review

“…The second common study objective was to explore individual characteristics of Reddit users who discuss substance use (31/60, 52%). Studies with this aim have explored individual characteristics, including demographic information [32,47,50,52], emotions [46,50], motivations for substance use [24,68,73], motivations for treatment [29], and perceptions of substance use [45,62]. In addition, some studies have conducted surveys to better investigate Reddit users' characteristics.…”

“…Among all the 60 included articles, 51 (85%) took advantage of the forum-like subreddit feature for data collection, and 38 (63%) reported the names of the subreddits that had been used. The top 3 most adopted drug-related subreddits were r/opiates (14/60, 23%) [27,[30][31][32]36,38,39,50,51,[54][55][56]58,59], r/OpiatesRecovery (12/60, 20%) [10,[30][31][32]36,38,39,46,50,51,54,58], and r/Drugs (6/60, 10%) [27,28,51,52,58,75]. The top 3 subreddits adopted for tobacco-related studies were r/stopsmoking (4/60, 7%) [19,45,60,63], r/electronic_cigarettes (4/60, 7%) [18,45,60,69], and r/vaping (3/60, 5%) [18,60,69].…”

“…Some articles adopted multiple annotation approaches. [17,28,36,37,39,50,61,64,66,71,72] 11 (100) Keyword matching or dictionary based a The numbers 34, 7, and 11 do not total 60 because not all of the 60 included articles used annotation approaches. Additionally, some articles might have used multiple annotation approaches.…”

“…For example, Yao et al [21] trained a series of traditional and neural network text classifiers to identify suicidal ideation among opioid users and found that the convolutional neural network model performed the best. Similarly, Jha and Singh [50] used several machine learning models to identify misinformation about medication for opioid use disorder, with a logistic regression classifier combined with term frequency-inverse document frequency achieving the best performance.…”

“I Will Not Drink With You Today”: A Topic-Guided Thematic Analysis of Addiction Recovery on Reddit

“…Further, much of the work that seeks to understand these communities is focused on the perspective of clinicians, such as looking for themes from diagnostic tools [33,37], clinician expertisebased recommendations [47], analyzing discussions to find addiction mechanisms and treatment methods that can be assessed for clinical validity [23,51], and categorizing users into clinical diagnoses [57,95,107]. While valuable, clinician perspectives may not always align with community members' values [13].…”