Effect of Feedback on Drug Consumption Disclosures on Social Media

Jangra, Hitkul; Shah, Rajiv Ratn; Kumaraguru, Ponnurangam

doi:10.1609/icwsm.v17i1.22158

Cited by 2 publications

(2 citation statements)

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“…Past research has suggested that Reddit is a popular social media platform well suited for substance-related research due to its large number of users with diverse backgrounds, which facilitates longitudinal data analysis and insights into the evolving nature of substance use and discussions [ 30 ]. Various studies have indicated that engaging in substance-related discussions can be a strong marker of an individual’s experience with substance use [ 26 , 28 , 31 ]. For our data set, we focus on Reddit, a prevalent social media platform where individuals can anonymously participate in topic-centered communities known as “subreddits.” Each subreddit operates under its own set of rules to moderate posts and comments, offering unfiltered discussions with less interference from overarching platform policies, distinguishing Reddit as an ideal platform for analysis of substance-related conversations [ 30 ].…”

Section: Methodsmentioning

confidence: 99%

“…In such cases, we used the strategy of breaking ties by selecting the highest risk level as the majority label. The rationale behind this tie-breaking approach lies in the assumption that when Reddit users begin engaging in discussions within higher risk level subreddits, they likely already possess some level of experience or familiarity with those particular drugs [ 26 , 28 , 31 ]. By assigning the higher risk level as the majority label in cases of ties, we aimed to capture the progression or transition of Reddit users toward subreddits associated with potentially greater risk.…”

Section: Methodsmentioning

confidence: 99%

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Examining the Gateway Hypothesis and Mapping Substance Use Pathways on Social Media: Machine Learning Approach

Yuan,

Kasson,

Taylor

et al. 2024

JMIR Form Res

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Background Substance misuse presents significant global public health challenges. Understanding transitions between substance types and the timing of shifts to polysubstance use is vital to developing effective prevention and recovery strategies. The gateway hypothesis suggests that high-risk substance use is preceded by lower-risk substance use. However, the source of this correlation is hotly contested. While some claim that low-risk substance use causes subsequent, riskier substance use, most people using low-risk substances also do not escalate to higher-risk substances. Social media data hold the potential to shed light on the factors contributing to substance use transitions. Objective By leveraging social media data, our study aimed to gain a better understanding of substance use pathways. By identifying and analyzing the transitions of individuals between different risk levels of substance use, our goal was to find specific linguistic cues in individuals’ social media posts that could indicate escalating or de-escalating patterns in substance use. Methods We conducted a large-scale analysis using data from Reddit, collected between 2015 and 2019, consisting of over 2.29 million posts and approximately 29.37 million comments by around 1.4 million users from subreddits. These data, derived from substance use subreddits, facilitated the creation of a risk transition data set reflecting the substance use behaviors of over 1.4 million users. We deployed deep learning and machine learning techniques to predict the escalation or de-escalation transitions in risk levels, based on initial transition phases documented in posts and comments. We conducted a linguistic analysis to analyze the language patterns associated with transitions in substance use, emphasizing the role of n-gram features in predicting future risk trajectories. Results Our results showed promise in predicting the escalation or de-escalation transition in risk levels, based on the historical data of Reddit users created on initial transition phases among drug-related subreddits, with an accuracy of 78.48% and an F1-score of 79.20%. We highlighted the vital predictive features, such as specific substance names and tools indicative of future risk escalations. Our linguistic analysis showed that terms linked with harm reduction strategies were instrumental in signaling de-escalation, whereas descriptors of frequent substance use were characteristic of escalating transitions. Conclusions This study sheds light on the complexities surrounding the gateway hypothesis of substance use through an examination of web-based behavior on Reddit. While certain findings validate the hypothesis, indicating a progression from lower-risk substances such as marijuana to higher-risk ones, a significant number of individuals did not show this transition. The research underscores the potential of using machine learning with social media analysis to predict substance use transitions. Our results point toward future directions for leveraging social media data in substance use research, underlining the importance of continued exploration before suggesting direct implications for interventions.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Examining the Gateway Hypothesis and Mapping Substance Use Pathways on Social Media: Machine Learning Approach

Yuan,

Kasson,

Taylor

et al. 2024

JMIR Form Res

View full text Add to dashboard Cite

show abstract

Examining the Gateway Hypothesis and Mapping Substance Use Pathways on Social Media: Machine Learning Approach (Preprint)

Yuan,

Kasson,

Taylor

et al. 2023

Preprint

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BACKGROUND Substance misuse presents significant global public health challenges. Understanding transitions between substance types and the timing of shifts to polysubstance use is vital for targeted prevention, harm reduction, and recovery strategies. The longstanding gateway hypothesis suggests high-risk substance use is preceded by lower-risk substance use. However, the source of this correlation is hotly contested. While some claim that low-risk substance use causes subsequent, riskier substance use, most users of low-risk substances also do not escalate to higher-risk substances. Social media data holds the potential to shed light on the factors contributing to substance use transitions. OBJECTIVE By leveraging social media data, our study aims to gain a better understanding of substance use pathways. By identifying and analyzing the transitions of individuals between different risk levels of substance use, our goal is to find specific linguistic cues in individuals' social media posts that could be indicative of escalating or de-escalating patterns in substance use. METHODS We conducted a large-scale analysis using data from Reddit, collected between 2015 and 2019, consisting of over 2.29 million posts and approximately 29.37 million comments by around 1.4 million users from subreddits. This data, derived from substance use subreddits, facilitated the creation of a risk transition dataset reflecting the substance use behaviors of over 1.4 million users. We deployed deep learning and machine learning techniques, including fine-tuned BERT and RoBERTa models, to predict the escalation or de-escalation in risk levels based on initial transition phases documented in posts and comments. Additionally, we conducted an extensive linguistic analysis to analyze the language patterns associated with transitions in substance use, emphasizing the role of n-gram features in predicting future risk trajectories. RESULTS Our results showed promise in predicting the escalation or de-escalation in risk levels based on the historical data of Reddit users created on initial transition phases among drug-related subreddits with an accuracy of 78.48% and an F1-score of 79.20%. We highlighted the vital predictive features, such as specific substance names and tools indicative of future risk escalations. Our linguistic analysis showed terms linked with harm reduction strategies were instrumental in signaling de-escalation, whereas descriptors of frequent substance use were characteristic of escalating transitions. CONCLUSIONS This study sheds light on the complexities surrounding the gateway hypothesis of substance use through an examination of online behavior on Reddit. While certain findings validate the hypothesis, indicating a progression from lower-risk substances like marijuana to higher-risk ones, a significant number of individuals did not showcase this transition. The research underscores the potential of using machine learning in conjunction with social media analysis for predicting substance use transitions. Our results emphasize the role of linguistic features as predictors and the importance of timely interventions.

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Effect of Feedback on Drug Consumption Disclosures on Social Media

Cited by 2 publications

References 45 publications

Examining the Gateway Hypothesis and Mapping Substance Use Pathways on Social Media: Machine Learning Approach

Examining the Gateway Hypothesis and Mapping Substance Use Pathways on Social Media: Machine Learning Approach

Examining the Gateway Hypothesis and Mapping Substance Use Pathways on Social Media: Machine Learning Approach (Preprint)

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