Conference Paper

Discovering Alternative Treatments for Opioid Use Recovery Using Social Media

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Abstract

Opioid use disorder (OUD) poses substantial risks to personal well-being and public health. In online communities, users support those seeking recovery, in part by promoting clinically grounded treatments. However, some communities also promote clinically unverified OUD treatments, such as unregulated and untested drugs. Little research exists on which alternative treatments people use, whether these treatments are effective for recovery, or if they cause negative side effects. We provide the first large-scale social media study of clinically unverified, alternative treatments in OUD recovery on Reddit, partnering with an addiction research scientist. We adopt transfer learning across 63 subreddits to precisely identify posts related to opioid recovery. Then, we quantitatively discover potential alternative treatments and contextualize their effectiveness. Our work benefits health research and practice by identifying undiscovered recovery strategies. We also discuss the impacts to online communities dealing with stigmatized behavior and research ethics.

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... To determine if subreddits primarily catered to the undergraduate college experience, two members of the research team designed a rating task. To be included, subreddits had to meet three criteria, inspired by prior work [8,16,78]: 1) the discussions were about undergraduate college experience; 2) there was at least one post in the last month when we gathered our data (April 2020); 3) the size of the subreddit was larger than 2,500 subscribers. Two researchers independently annotated whether a subreddit met these criteria and met to resolve disagreements. ...
... For this threshold, we used cosine similarity (at a threshold of 0.85) for each discrete post to the LSSF embedding vector. The research team manually validated our threshold for plausible cosine similarity scores between 0.7 and 0.9 in 0.05 increments, motivated by prior work [16,73,76]. We found that a cosine similarity of 0.85 appropriately balanced filtering noise and identifying posts related to the post-college transition. ...
... 4.2.1 Building a Classification Approach. Inspired by past work in using social media to understand mental well-being [15,16,78], we used transfer learning to assess stress expressions in our dataset. Transfer learning is a popular approach in machine learning that trains and tunes a supervised model on a closely related but different dataset -the resulting model is then "transferred" to the new, target dataset [84]. ...
Article
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The post-college transition is a critical period where individuals experience unique challenges and stress before, during, and after graduation. Individuals often use social media to discuss and share information, advice, and support related to post-college challenges in online communities. These communities are important as they fill gaps in institutional support between college and post-college plans. We empirically study the challenges and stress expressed on social media around this transition as students graduate college and move into emerging adulthood. We assembled a dataset of about 299,000 Reddit posts between 2008 and 2020 about the post-college transition from 10 subreddits. We extracted top concerns, challenges, and conversation points using unsupervised Latent Dirichlet Allocation (LDA). Then, we combined the results of LDA with binary transfer learning to identify stress expressions in the dataset (classifier performance at F1=0.94). Finally, we explore temporal patterns in stress expressions, and the variance of per-topic stress levels throughout the year. Our work highlights more deliberate and focused understanding of the post-college transition, as well as useful research and design impacts to study transient cohorts in need of support.
... Efforts in the field of human-computer interaction (HCI) have attempted to understand both how mental health is experienced and the interactions that experience can have on technology use [37••]. This includes understanding and predicting how individuals will express mental illness in online and social contexts through analysis of social media and online mental health forums (as described above) [21,[38][39][40][41][42], as well as designing behavioral interventions to ease mental distress [43][44][45]. Work in HCI has involved use of online data to explore mental health states [46], and analysis of social media data to predict the onset of symptoms of mental illness. ...
... For example, analysis of data from Twitter showed over 70% accuracy in predicting onset of postpartum depression [40] and major depressive disorder [47]. Other studies have explored interactions between technology and mental health, including predicting when an individual would come to feel better within an online mental health forum [41], predicting shifts to suicidal ideation using data collected from Reddit [21], or examining linguistic signals of more complex mental disorders such as schizophrenia [39,48,49]. ...
... The clinical implications of this work are beginning to be seen, such as a greater understanding of alternative methods of addressing opioid addiction [39]o ram o r e nuanced understanding of pre-treatment factors that might predict outcome in the use of antidepressants [42]. Complementing these mainly quantitative analyses, work in HCI has also considered how people express their mental health in different social media contexts [38,50], and create behavioral interventions based on these understandings, including cognitive behavioral [44]a n d emotional regulation [45] targeted interventions. ...
Article
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Purpose Globally, individuals living with mental disorders are more likely to have access to a mobile phone than mental health care. In this commentary, we highlight opportunities for expanding access to and use of digital technologies to advance research and intervention in mental health, with emphasis on the potential impact in lower resource settings. Recent findings Drawing from empirical evidence, largely from higher income settings, we considered three emerging areas where digital technology will potentially play a prominent role: supporting methods in data science to further our understanding of mental health and inform interventions, task sharing for building workforce capacity by training and supervising non-specialist health workers, and facilitating new opportunities for early intervention for young people in lower resource settings. Challenges were identified related to inequities in access, threats of bias in big data analyses, risks to users, and need for user involvement to support engagement and sustained use of digital interventions. Summary For digital technology to achieve its potential to transform the ways we detect, treat, and prevent mental disorders, there is a clear need for continued research involving multiple stakeholders, and rigorous studies showing that these technologies can successfully drive measurable improvements in mental health outcomes.
... Social media platforms such as Twitter, Facebook, and Reddit have fostered communities that provide solidarity and support for people dealing with a multitude of issues such as eating disorder [1], suicidal ideation [2], and chronic illnesses such as rheumatoid arthritis, stroke [3], HIV/AIDS [4], and opioid use disorder (OUD) [5]. In these communities, users receive emotional support, information, and companionship while, in some cases, preserving anonymity. ...
... Past research has described how social media platforms are used to benefit (or, in some cases, harm) subpopulations with certain medical conditions. For instance, social media can allow harmful or misleading opinions to propagate to other users [1,2], or offer an avenue for subpopulations to seek support, advice, or information relevant to their condition [3][4][5]. Studies exploring discussion on stroke-and HIV-specific forums have shown that participants largely use such forums to request and share information or emotional support, or to share their own experiences [3,4]. ...
... A separate body of research has provided insight on the social structure of these communities finding that, similar to in-person support groups such as Alcoholics Anonymous and Narcotics Anonymous (AA/NA) [10], community cohesion is driven by a core of long-standing members [11] while those most engaged with the platform (i.e., posting most frequently) are currently withdrawing from or still using illicit opioids [12]. More recently, an analysis of Reddit forums found that there is also an abundance of medical advice from non-clinicians including unverified OUD treatment alternatives [5,13]. Buprenorphine-naloxone is one of the most effective tools for reducing overdoses [14,15]. ...
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Background: Online communities such as Reddit can provide social support for those recovering from opioid use disorder. However, it is unclear whether and how advice-seekers differ from other users. Our research addresses this gap by identifying key characteristics of r/suboxone users that predict advice-seeking behavior. Objective: The objective of this analysis is to identify and describe advice-seekers on Reddit for buprenorphine-naloxone use using text annotation, social network analysis, and statistical modeling techniques. Methods: We collected 5258 posts and their comments from Reddit between 2014 and 2019. Among 202 posts which met our inclusion criteria, we annotated each post to determine which were advice-seeking (n = 137) or not advice-seeking (n = 65). We also annotated each posting user's buprenorphine-naloxone use status (current versus formerly taking and, if currently taking, whether inducting or tapering versus other stages) and quantified their connectedness using social network analysis. To analyze the relationship between Reddit users' advice-seeking and their social connectivity and medication use status, we constructed four models which varied in their inclusion of explanatory variables for social connectedness and buprenorphine use status. Results: The stepwise model containing "total degree" (p = 0.002), "using: inducting/tapering" (p < 0.001), and "using: other" (p = 0.01) outperformed all other models. Reddit users with fewer connections and who are currently using buprenorphine-naloxone are more likely to seek advice than those who are well-connected and no longer using the medication, respectively. Importantly, advice-seeking behavior is most accurately predicted using a combination of network characteristics and medication use status, rather than either factor alone. Conclusions: Our findings provide insights for the clinical care of people recovering from opioid use disorder and the nature of online medical advice-seeking overall. Clinicians should be especially attentive (e.g., through frequent follow-up) to patients who are inducting or tapering buprenorphine-naloxone or signal limited social support.
... As of June 2021, the novel coronavirus disease (COVID- 19) pandemic has claimed the lives of over 590,000 individuals in the US and 3.8 million globally [1]. Public health experts suggest that the intersection of the COVID-19 pandemic and the ongoing drug overdose epidemic has had detrimental impacts on efforts to reduce deaths [2,3]. ...
... Kratom, which is unregulated, and iboga (ibogaine), a schedule-I controlled substance also showed increases in conversations. These substances have been of interest to people who use opioids as alternatives for opioid treatment or withdrawal in non-medical settings and have received widespread social media attention [19] despite concerns about safety [20,21]. Insights from our work may help inform population-level monitoring efforts for drug poisoning and overdose education by providers and public health practitioners, specifically in the context of COVID-19. ...
Article
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Background Timely data from official sources regarding the impact of the COVID-19 pandemic on people who use prescription and illegal opioids is lacking. We conducted a large-scale, natural language processing (NLP) analysis of conversations on opioid-related drug forums to better understand concerns among people who use opioids. Methods In this retrospective observational study, we analyzed posts from 14 opioid-related forums on the social network Reddit. We applied NLP to identify frequently mentioned substances and phrases, and grouped the phrases manually based on their contents into three broad key themes: (i) prescription and/or illegal opioid use ; (ii) substance use disorder treatment access and care ; and (iii) withdrawal . Phrases that were unmappable to any particular theme were discarded. We computed the frequencies of substance and theme mentions, and quantified their volumes over time. We compared changes in post volumes by key themes and substances between pre-COVID-19 (1/1/2019—2/29/2020) and COVID-19 (3/1/2020—11/30/2020) periods. Results Seventy-seven thousand six hundred fifty-two and 119,168 posts were collected for the pre-COVID-19 and COVID-19 periods, respectively. By theme, posts about treatment and access to care increased by 300%, from 0.631 to 2.526 per 1000 posts between the pre-COVID-19 and COVID-19 periods. Conversations about withdrawal increased by 812% between the same periods (0.026 to 0.235 per 1,000 posts). Posts about drug use did not increase (0.219 to 0.218 per 1,000 posts). By substance, among medications for opioid use disorder, methadone had the largest increase in conversations (20.751 to 56.313 per 1,000 posts; 171.4% increase). Among other medications, posts about diphenhydramine exhibited the largest increase (0.341 to 0.927 per 1,000 posts; 171.8% increase). Conclusions Conversations on opioid-related forums among people who use opioids revealed increased concerns about treatment and access to care along with withdrawal following the emergence of COVID-19. Greater attention to social media data may help inform timely responses to the needs of people who use opioids during COVID-19.
... 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. ...
... Lu et al. [22], used the cox regression model to identify transitions to addiction recovery subreddits. Chancellor et al. [23], studied recovery-related posts on Reddit to identify clinically unverified treatments for drug withdrawal popular amongst drug users on Reddit. Rubya et al. [24]., investigated how users in online recovery communities enact anonymity Finally, Tamersoy et al. [25], studied Reddit forums to characterize smoking and drinking abstinence and were able to predict long-term and short-term abstinence. ...
Article
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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.
... More recently, researchers have examined patterns of anonymity in web-based recovery communities [13]. Specific to OUD, previous studies have investigated the different types of web-based discourse associated with opioid use, including personal use, whether it is associated with legitimate use or abuse of opioids [14], or whether it involves the promotion of clinically unverified treatments [15]. Abuse discourse on social media platforms has been further broken down into stand-alone use and co-use of opioids with other opioids, illicit drugs, and alcohol [16]. ...
... Despite the positive benefits of social media, existing attempts of individuals with OUD are often challenged because of the pervasiveness of inaccurate and potentially harmful health misinformation on social media platforms [15]. Health misinformation is defined as a health-related claim of a fact that is currently false because of a lack of scientific evidence [22]. ...
Article
Background Expanding access to and use of medication for opioid use disorder (MOUD) is a key component of overdose prevention. An important barrier to the uptake of MOUD is exposure to inaccurate and potentially harmful health misinformation on social media or web-based forums where individuals commonly seek information. There is a significant need to devise computational techniques to describe the prevalence of web-based health misinformation related to MOUD to facilitate mitigation efforts. Objective By adopting a multidisciplinary, mixed methods strategy, this paper aims to present machine learning and natural language analysis approaches to identify the characteristics and prevalence of web-based misinformation related to MOUD to inform future prevention, treatment, and response efforts. Methods The team harnessed public social media posts and comments in the English language from Twitter (6,365,245 posts), YouTube (99,386 posts), Reddit (13,483,419 posts), and Drugs-Forum (5549 posts). Leveraging public health expert annotations on a sample of 2400 of these social media posts that were found to be semantically most similar to a variety of prevailing opioid use disorder–related myths based on representational learning, the team developed a supervised machine learning classifier. This classifier identified whether a post’s language promoted one of the leading myths challenging addiction treatment: that the use of agonist therapy for MOUD is simply replacing one drug with another. Platform-level prevalence was calculated thereafter by machine labeling all unannotated posts with the classifier and noting the proportion of myth-indicative posts over all posts. Results Our results demonstrate promise in identifying social media postings that center on treatment myths about opioid use disorder with an accuracy of 91% and an area under the curve of 0.9, including how these discussions vary across platforms in terms of prevalence and linguistic characteristics, with the lowest prevalence on web-based health communities such as Reddit and Drugs-Forum and the highest on Twitter. Specifically, the prevalence of the stated MOUD myth ranged from 0.4% on web-based health communities to 0.9% on Twitter. Conclusions This work provides one of the first large-scale assessments of a key MOUD-related myth across multiple social media platforms and highlights the feasibility and importance of ongoing assessment of health misinformation related to addiction treatment.
... We work on detecting illicit and prescription DA-related tweets. We focus on opioid drugs that include both prescription drugs and illicit drugs as well [8] and collect related tweets. A list of commonly abused illegal schedule 1 drugs like heroin and schedule 2 prescription drugs like fentanyl, oxycontin, etc., are used to collect tweets on Twitter. ...
... A list of commonly abused illegal schedule 1 drugs like heroin and schedule 2 prescription drugs like fentanyl, oxycontin, etc., are used to collect tweets on Twitter. [8]. ...
... For PWUO, in particular, the anonymity of online forums, such as Reddit, has the potential to reduce stigma and social exclusion and can be an important factor for seeking support online [8,9]. While these communities can provide much-needed support for those recovering from OUD, there is also an abundance of medical advice from non-clinicians (e.g., unverified OUD treatment alternatives [10]). Given the potential benefits and risks of seeking support for OUD online, it is important to identify and characterize which people recovering from OUD are likely to seek advice online. ...
... Despite the importance of characterizing advice-seekers on online recovery platforms, there is currently limited research on this topic. Research has analyzed these platforms to predict PWUO's transition to OUD [11], discover alternative treatments for opioid use recovery [10], and determine the prevalence of polydrug use [12]. Other work focuses on the social aspect of OUD recovery such as the social connectedness of online communities. ...
Preprint
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Background: Online communities can provide social support for those recovering from opioid use disorder. However, advice-seekers on these platforms risk exposure to uncurated medical advice, potentially harming their health or recovery efforts. The objective of this analysis is to combine text annotation, social network analysis, and statistical modeling to identify advice-seekers on online social media for buprenorphine-naloxone use and study their characteristics. Methods: We collected 5,258 posts and their comments from Reddit between 2014 and 2019. Among 202 posts which met our inclusion criteria, we annotated each post to determine which were advice-seeking (n=137) and not advice-seeking (n=65). We also annotated each posting user's medication use stage and quantified their connectedness using social network analysis. In order to analyze the relationship between advice-seeking with a user's social connectivity and medication use stage, we constructed four models which varied in explanatory variables. Results: The stepwise model (containing "total degree" (P=0.002), "using: inducting/tapering" (P<0.001), and "using: other" (P=0.01) as significant explanatory variables) outperformed all other models. We found that users with fewer connections and who are currently using buprenorphine-naloxone are more likely to seek advice than users who are well-connected and no longer using the medication, respectively. Importantly, advice-seeking behavior is most accurately predicted using a combination of network characteristics and medication use status, rather than either factor alone. Conclusions: Our findings provide insights for the clinical care of people recovering from opioid use disorder and the nature of online medical advice-seeking overall. Clinicians should be especially attentive (e.g., through frequent follow-up) to patients who are inducting or tapering buprenorphine-naloxone or signal limited social support.
... Twitter is one of the most popular microblogging and social network sites. The use of Twitter data in the field of biomedical knowledge mining [11], [12], (i.e., discovering alternative treatments for opioid abusers [6]) to explore the patterns and trends to understand opioid abuser has become essential. Moreover, Twitter has become a valuable source during the COVID-19 pandemic. ...
... This dictionary has three types of entities: 1. MAT drug with street term 2. Drug use indicating term 3. Personal pronoun. The entities except 'Personal pronoun' of this dictionary is collected from online resources such as 1 NIDA, 2 WHO, 3 DEA, 4 DRUGBANK, and also from the different related articles [6], [7], [9], [18], [19], [33], [34]. Moreover, this dictionary is reviewed and approved by a medical expert. ...
... Alongside traditional medical, pharmacological, and public health studies on the nonmedical adoption of prescription opioids [6][7][8][9][10][11][12][13][14], several phenomena related to the opioid epidemic have recently been successfully tackled through a digital epidemiology [15][16][17][18] approach. Researchers have used digital and social media data to perform various tasks, including detecting drug abuse [19,20], forecasting opioid overdose [21], studying transition into drug addiction [22], predicting opioid relapse [23], and discovering previously unknown treatments for opioid addiction [24]. A few recent studies investigated the temporal unfolding of the opioid epidemic in the United States by leveraging complementary data sources different from the official US Centers for Disease Control and Prevention data [2,[25][26][27][28] and using social media like Reddit [29,30]. ...
... Due to fair guarantees of anonymity, no limits on the number of characters in a post, and a large variety of debated topics, this platform is often used to uninhibitedly discuss personal experiences [42]. Reddit constitutes a nonintrusive and privileged data source to study a variety of issues [43,44], including sensitive topics such as mental health [45], weight loss [46], gender issues [47], and substance abuse [22,24]. ...
Article
Background The complex unfolding of the US opioid epidemic in the last 20 years has been the subject of a large body of medical and pharmacological research, and it has sparked a multidisciplinary discussion on how to implement interventions and policies to effectively control its impact on public health. Objective This study leverages Reddit, a social media platform, as the primary data source to investigate the opioid crisis. We aimed to find a large cohort of Reddit users interested in discussing the use of opioids, trace the temporal evolution of their interest, and extensively characterize patterns of the nonmedical consumption of opioids, with a focus on routes of administration and drug tampering. Methods We used a semiautomatic information retrieval algorithm to identify subreddits discussing nonmedical opioid consumption and developed a methodology based on word embedding to find alternative colloquial and nonmedical terms referring to opioid substances, routes of administration, and drug-tampering methods. We modeled the preferences of adoption of substances and routes of administration, estimating their prevalence and temporal unfolding. Ultimately, through the evaluation of odds ratios based on co-mentions, we measured the strength of association between opioid substances, routes of administration, and drug tampering. Results We identified 32 subreddits discussing nonmedical opioid usage from 2014 to 2018 and observed the evolution of interest among over 86,000 Reddit users potentially involved in firsthand opioid usage. We learned the language model of opioid consumption and provided alternative vocabularies for opioid substances, routes of administration, and drug tampering. A data-driven taxonomy of nonmedical routes of administration was proposed. We modeled the temporal evolution of interest in opioid consumption by ranking the popularity of the adoption of opioid substances and routes of administration, observing relevant trends, such as the surge in synthetic opioids like fentanyl and an increasing interest in rectal administration. In addition, we measured the strength of association between drug tampering, routes of administration, and substance consumption, finding evidence of understudied abusive behaviors, like chewing fentanyl patches and dissolving buprenorphine sublingually. Conclusions This work investigated some important consumption-related aspects of the opioid epidemic using Reddit data. We believe that our approach may provide a novel perspective for a more comprehensive understanding of nonmedical abuse of opioids substances and inform the prevention, treatment, and control of the public health effects.
... Reddit has the potential for designers of health communication, aimed at persuasive opioid messaging, to test their messaging (Silberman & Record, 2021). Additionally, the text mining of Reddit data may better inform providers of health prevention services, community veri ed alternative addition treatment (15). ...
... These query terms are commonly used as query terms to gather data related to opioids. Additionally, these query terms are representative of the language that is used in the subreddit search (15,16). From the RedditExtraction package, the function get_reddit was performed on the opiates subreddit (r/opiates) to extract and compiled the dataset for this analysis. ...
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Background: The opioid crisis has fuelled dramatic increases in fatal drug overdoses, with illicitly manufactured fentanyl and fentanyl analogues driving the opioid-related overdose death rates among all groups. One way to address these overdose deaths is through increasing public awareness about opioid overdoses and encouraging people who use drugs to make safe choices about opioids. The ease, convenience, and privacy of social media sites provide an inside look into the world of opioid users. This study seeks to understand the nature and significance of Reddit discussions regarding opioid overdoses and safe choices. Methods: We systematically searched Reddit during the month of August (2019). We collected 4,844 posts, across 25 distinct r/opiates subreddit forums using the search terms “opioids,” “drugs,” and “fentanyl”. We then used qualitative thematic analysis methods to code 49 unique original posts. Results: The posts from these Reddit discussions provide insight into this online opioid community and how they are sharing and normalizing risk-mitigating strategies to avoid opioid-related overdoses through (1) recognizing the dangers and avoiding fentanyl; (2) knowing the signs and symptoms of opioid overdoses; and (3) having and using naloxone to treat opioid overdoses. Conclusions: These informal and social interactions provide insight into the complexity the opioid epidemic crisis and can inform future strategies and interventions to address the opioid crisis.
... Alongside traditional medical, pharmacological, and public health studies on the nonmedical adoption of prescription opioids [6][7][8][9][10][11][12][13][14], several phenomena related to the opioid epidemic have recently been successfully tackled through a digital epidemiology [15][16][17][18] approach. Researchers have used digital and social media data to perform various tasks, including detecting drug abuse [19,20], forecasting opioid overdose [21], studying transition into drug addiction [22], predicting opioid relapse [23], and discovering previously unknown treatments for opioid addiction [24]. A few recent studies investigated the temporal unfolding of the opioid epidemic in the United States by leveraging complementary data sources different from the official US Centers for Disease Control and Prevention data [2,[25][26][27][28] and using social media like Reddit [29,30]. ...
... Due to fair guarantees of anonymity, no limits on the number of characters in a post, and a large variety of debated topics, this platform is often used to uninhibitedly discuss personal experiences [42]. Reddit constitutes a nonintrusive and privileged data source to study a variety of issues [43,44], including sensitive topics such as mental health [45], weight loss [46], gender issues [47], and substance abuse [22,24]. This study's contributions are manifold. ...
Preprint
Full-text available
The complex unfolding of the US opioid epidemic in the last 20 years has been the subject of a large body of medical and pharmacological research, and it has sparked a multidisciplinary discussion on how to implement interventions and policies to effectively control its impact on public health. This study leverages Reddit as the primary data source to investigate the opioid crisis. We aimed to find a large cohort of Reddit users interested in discussing the use of opioids, trace the temporal evolution of their interest, and extensively characterize patterns of the nonmedical consumption of opioids, with a focus on routes of administration and drug tampering. We used a semiautomatic information retrieval algorithm to identify subreddits discussing nonmedical opioid consumption, finding over 86,000 Reddit users potentially involved in firsthand opioid usage. We developed a methodology based on word embedding to select alternative colloquial and nonmedical terms referring to opioid substances, routes of administration, and drug-tampering methods. We modeled the preferences of adoption of substances and routes of administration, estimating their prevalence and temporal unfolding, observing relevant trends such as the surge in synthetic opioids like fentanyl and an increasing interest in rectal administration. Ultimately, through the evaluation of odds ratios based on co-mentions, we measured the strength of association between opioid substances, routes of administration, and drug tampering, finding evidence of understudied abusive behaviors like chewing fentanyl patches and dissolving buprenorphine sublingually. We believe that our approach may provide a novel perspective for a more comprehensive understanding of nonmedical abuse of opioids substances and inform the prevention, treatment, and control of the public health effects.
... 8 User posts on Reddit related to opioid addiction were analyzed and alternative unapproved treatments were found to be promoted by the patients on other online communities. 9 For pharmaceutical companies that are the MAH of a medication, these posts present an opportunity to comprehensively evaluate the drug's benefit-risk profile. Because the posts in OHCs are in the form of health discussions, they contain requisite information such as the drug name and indication but clinical details regarding the case such as the medical history of the patient may not be available. ...
Article
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Objective Outcomes mentioned on online health communities (OHCs) by patients can serve as a source of evidence for off-label drug usage evaluation, but identifying these outcomes manually is tedious work. We have built a natural language processing model to identify off-label usage of drugs mentioned in these patient posts. Materials and Methods Single patient posts from 4 major OHCs were considered for this study. A text classification model was built to classify the posts as either relevant or not relevant based on patient experience. The relevant posts were passed through a spelling correction tool, CSpell, and then medications and indications from these posts were identified using cTAKES (clinical Text Analysis and Knowledge Extraction System), a named entity recognition tool. Drug and indication pairs were identified using a dependency parser. Finally, if the paired indication was not mentioned on the label of the drug approved by U.S. Food and Drug Administration, it was tagged as off-label use of that drug. Results Using this algorithm, we identified 289 off-label indications, achieving a recall of 76%. Conclusions The method designed in this study identifies and extracts the semantic relationship between drugs and indications from demotic posts in OHCs. The results demonstrate the feasibility of using natural language processing techniques in identifying off-label drug usage across online health forums for a variety of drugs. Understanding patients’ off-label use of drugs may be able to help manufacturers innovate to better address patients’ needs and assist doctors’ prescribing decisions.
... Studies that specifically use Reddit data include a study by Pandrekar et al. that investigated the opioid discussion on Reddit by analyzing 51,537 opioid-related posts, performing topic modelling, and finding the psychological categories of the opioid posts 15 . Chancellor et al. used Reddit posts about opioid recovery, and discovered potential alternative treatments in opioid recovery posts 16 . ...
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The opioid epidemic persists in the United States; in 2019, annual drug overdose deaths increased by 4.6% to 70,980, including 50,042 opioid-related deaths. The widespread abuse of opioids across geographies and demographics and the rapidly changing dynamics of abuse require reliable and timely information to monitor and address the crisis. Social media platforms include petabytes of participant-generated data, some of which, offers a window into the relationship between individuals and their use of drugs. We assessed the utility of Reddit data for public health surveillance, with a focus on the opioid epidemic. We built a natural language processing pipeline to identify opioid-related comments and created a cohort of 1,689,039 geo-located Reddit users, each assigned to a city and state. We followed these users over a period of 10+ years and measured their opioid-related activity over time. We benchmarked the activity of this cohort against CDC overdose death rates for different drug classes and NFLIS drug report rates. Our Reddit-derived rates of opioid discussion strongly correlated with external benchmarks on the national, regional, and city level. During the period of our study, kratom emerged as an active discussion topic; we analyzed mentions of kratom to understand the dynamics of its use. We also examined changes in opioid discussions during the COVID-19 pandemic; in 2020, many opioid classes showed marked increases in discussion patterns. Our work suggests the complementary utility of social media as a part of public health surveillance activities.
... Literature on online presentation has examined how people share difcult content, including but not limited to death of a loved one [10], job loss [11], and relationship breakups [36]. A growing body of research has examined social media use in sharing negatively perceived and stigmatized events, such as pregnancy loss [2], sexual abuse [3], and addiction [13]. Sharing difcult content can lead to positive outcomes such as resources and support [1,14], and negative outcomes such as rejection and further stigmatization [8]. ...
Article
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People often strive to present themselves authentically on social media, but this may not be possible for everyone. To understand how people view online authenticity, how it relates to social media sharing behaviors, and whether it is achievable, we interviewed 28 social media users who had recently experienced major life transitions. We found that to many participants, online authenticity required presenting a consistent, positive, and “true” self across online and offline contexts. Though most stated that they considered online authenticity achievable, their social media self-disclosure behaviors around life transitions revealed what we call the online authenticity paradox: people strive to achieve online authenticity, yet because doing so requires sharing negative experiences on social media, online authenticity is often unreachable, or is possible only at great personal cost – especially for those with marginalized identities and difficult life experiences.
... Such techniques include both supervised classification [17]- [20] as well as unsupervised methods [15], [21] for identifying DA tweets from the tweet stream. Complementary to the problem of DA detection, works based on identifying alternative opioid recovery treatments on social media also exist [22]. Such data can be leveraged to gain insights about the microscopic behavior of the corresponding users and their role in spreading DA tweets in the network although only a few works have attempted to do so [2], [5]. ...
Preprint
In this article, we perform a large-scale study of the Twitter follower network, involving around 0.42 million users who justify DA, to characterize the spreading of DA tweets across the network. Our observations reveal the existence of a very large giant component involving 99% of these users with dense local connectivity that facilitates the spreading of such messages. We further identify active cascades over the network and observe that the cascades of DA tweets get spread over a long distance through the engagement of several closely connected groups of users. Moreover, our observations also reveal a collective phenomenon, involving a large set of active fringe nodes (with a small number of follower and following) along with a small set of well-connected nonfringe nodes that work together toward such spread, thus potentially complicating the process of arresting such cascades. Furthermore, we discovered that the engagement of the users with respect to certain drugs, such as Vicodin, Percocet, and OxyContin, that were observed to be most mentioned in Twitter is instantaneous. On the other hand, for drugs, such as Lortab, that found lesser mentions, the engagement probability becomes high with increasing exposure to such tweets, thereby indicating that drug abusers engaged on Twitter remain vulnerable to adopting newer drugs, aggravating the problem further.
... Both datasets were then used to train and develop the final deep learning model. Some studies have used social media sources other than Twitter; Chancellor et al. [25], for example, employed machine learning methods (LR, SVM, and RF) to determine whether a Reddit post was about opioid use disorder recovery. Despite the potential application of supervised classification approaches, our recent review on the topic [9] showed that significant improvements in the performances of current systems were needed to effectively utilize social media data for PM abuse monitoring. ...
Article
Full-text available
Background Prescription medication (PM) misuse/abuse has emerged as a national crisis in the United States, and social media has been suggested as a potential resource for performing active monitoring. However, automating a social media-based monitoring system is challenging—requiring advanced natural language processing (NLP) and machine learning methods. In this paper, we describe the development and evaluation of automatic text classification models for detecting self-reports of PM abuse from Twitter. Methods We experimented with state-of-the-art bi-directional transformer-based language models, which utilize tweet-level representations that enable transfer learning (e.g., BERT, RoBERTa, XLNet, AlBERT, and DistilBERT), proposed fusion-based approaches, and compared the developed models with several traditional machine learning, including deep learning, approaches. Using a public dataset, we evaluated the performances of the classifiers on their abilities to classify the non-majority “abuse/misuse” class. Results Our proposed fusion-based model performs significantly better than the best traditional model (F 1 -score [95% CI]: 0.67 [0.64–0.69] vs. 0.45 [0.42–0.48]). We illustrate, via experimentation using varying training set sizes, that the transformer-based models are more stable and require less annotated data compared to the other models. The significant improvements achieved by our best-performing classification model over past approaches makes it suitable for automated continuous monitoring of nonmedical PM use from Twitter. Conclusions BERT, BERT-like and fusion-based models outperform traditional machine learning and deep learning models, achieving substantial improvements over many years of past research on the topic of prescription medication misuse/abuse classification from social media, which had been shown to be a complex task due to the unique ways in which information about nonmedical use is presented. Several challenges associated with the lack of context and the nature of social media language need to be overcome to further improve BERT and BERT-like models. These experimental driven challenges are represented as potential future research directions.
... Both datasets were then used to train and develop the nal deep learning model. Some studies have used social media sources other than Twitter; Chancellor et al. (25), for example, employed machine learning methods (LR, SVM, and RF) to determine whether a Reddit post was about opioid use disorder recovery. Despite the potential application of supervised classi cation approaches, our recent review on the topic (9) showed that signi cant improvements in the performances of current systems were needed to effectively utilize social media data for PM abuse monitoring. ...
Preprint
Full-text available
Background Prescription medication (PM) misuse/abuse has emerged as a national crisis in the United States, and social media has been suggested as a potential resource for performing active monitoring. However, automating a social media-based monitoring system is challenging—requiring advanced natural language processing (NLP) and machine learning methods. In this paper, we describe the development and evaluation of automatic text classification models for detecting self-reports of PM abuse from Twitter. Methods We experimented with state-of-the-art bi-directional transformer-based language models, which utilize tweet-level representations that enable transfer learning (e.g., BERT, RoBERTa, XLNet, AlBERT, and DistilBERT), proposed fusion-based approaches, and compared the developed models with several traditional machine learning, including deep learning, approaches. Using a public dataset, we evaluated the performances of the classifiers on their abilities to classify the non-majority “abuse/misuse” class. Results Our proposed fusion-based model performs significantly better than the best traditional model (F1-score [95% CI]: 0.67 [0.64-0.69] vs. 0.45 [0.42-0.48]). We illustrate, via experimentation using differing training set sizes, that the transformer-based models are more stable and require less annotated data compared to the other models. The significant improvements achieved by our best-performing classification model over past approaches makes it suitable for automated continuous monitoring of nonmedical PM use from Twitter. Conclusions BERT, BERT-like and fusion-based models not only outperform traditional machine learning and deep learning models, but also show substantial improvements over many years of past research on the topic of prescription medication misuse/abuse classification from social media, which had been shown to be a complex task due to the unique ways in which information about nonmedical use is presented. Several challenges, such as lack of complete context and the nature of social media language, need to be overcome to further improve BERT and BERT-like models. These experimental driven challenges are represented as potential future research directions.
... In fact, these consequences are compounded because OMHCs cater to sensitive population of individuals (ones possibly struggling with mental health challenges). For instance, diagnosing, suggesting, and adopting drugs and alternative treatments without clinical corroboration can adversely affect individuals [14,45,67]. Further, since OMHCs are largely peer-driven platforms, it is essential to ensure the quality, credibility, and supportiveness of content being shared, so that these communities facilitate positive health and behavior change [12]. ...
Chapter
Full-text available
Online Mental Health Communities (OMHCs) enable individuals to seek and provide support, and serve as a safe haven to disclose and share stigmatizing and sensitive experiences. Like other online communities, OMHCs are not immune to bad behavior and antisocial activities such as trolling, spamming, and harassment. Therefore, these communities are oftentimes guided by strict norms against such behavior, and moderated to ensure the quality and credibility of the content being shared. However, moderation within these communities is not only limited to ensuring content quality. It is far more complex—providing supportive spaces for disclosure, ensuring individuals’ privacy, etc.—because of the sensitive population that they cater to. By interviewing 19 moderators across 12 such OMHCs on Reddit, this paper studies the practices and structure of moderation in these communities to better understand their functioning and effectiveness. Our research questions primarily revolve around three major themes—moderation, support, and self-disclosure. We find practices of moderation hierarchy, and several distinctions in motivations and responsibilities of the moderators individually and as a group. We also notice that these communities predominantly encourage emotional support, and provide supportive spaces that encourage self-disclosure on stigmatized concerns. Our findings highlight the necessity of awareness corresponding to (currently lacking) privacy concerns, and raises the importance of the presence of mental health experts (counselors and psychiatrists) in these communities. On the basis of the insights drawn from this work, we discuss the implications and considerations for designing OMHCs.
... 15,16 While a large number of patients use online health forums to discuss disease and treatment options, 12,17 social media is still underutilized as a source of information about off-label drugs. In a recent study, Chancellor et al. 18 analyzed user posts on Reddit related to Opioid addiction and found related alternative treatments discussed and promoted by users in online communities. In prior related research, Frost et al. 19 analyzed structured information entered by users of PatientsLikeMe, about two drugs with known off-label usage, and suggested that patient reported data can be used as a new source of information about off-label prescriptions. ...
Article
Full-text available
Objectives To investigate using patient posts in social media as a resource to profile off-label prescriptions of cancer drugs. Methods We analyzed patient posts from the Inspire health forums (www.inspire.com) and extracted mentions of cancer drugs from the 14 most active cancer-type specific support groups. To quantify drug-disease associations, we calculated information component scores from the frequency of posts in each cancer-specific group with mentions of a given drug. We evaluated the results against three sources: manual review, Wolters-Kluwer Medi-span, and Truven MarketScan insurance claims. Results We identified 279 frequently discussed and therefore highly associated drug-disease pairs from Inspire posts. Of these, 96 are FDA approved, 9 are known off-label uses, and 174 do not have records of known usage (potentially novel off-label uses). We achieved a mean average precision of 74.9% in identifying drug-disease pairs with a true indication association from patient posts and found consistent evidence in medical claims records. We achieved a recall of 69.2% in identifying known off-label drug uses (based on Wolters-Kluwer Medi-span) from patient posts.
... Literature on online presentation has examined how people share difcult content, including but not limited to death of a loved one [10], job loss [11], and relationship breakups [36]. A growing body of research has examined social media use in sharing negatively perceived and stigmatized events, such as pregnancy loss [2], sexual abuse [3], and addiction [13]. Sharing difcult content can lead to positive outcomes such as resources and support [1,14], and negative outcomes such as rejection and further stigmatization [8]. ...
... A thematic analysis of posts to popular opioid-related subreddits during the early stages of the COVID-19 pandemic found evidence of robust mutual aid and social support [31]. In a computational text analysis across 1.4 million Reddit posts in 63 subreddits, previous work identified common terms used to describe alternative treatments for opioid use recovery, the most common of which was kratom [32]. Through a word embedding analysis, that study found that cannabis had the highest cosine similarity with kratom (indicating that the word "cannabis" was the most contextually similar word to "kratom" in these opioid recovery posts). ...
Article
Full-text available
A growing body of research has reported on the potential opioid-sparing effects of cannabis and cannabinoids, but less is known about specific mechanisms. The present research examines cannabis-related posts in two large online communities on the Reddit platform ("subreddits") to compare mentions of naturalistic cannabis use by persons self-identifying as actively using opioids versus persons in recovery. We extracted all posts mentioning cannabis-related keywords (e.g., "weed", "cannabis", "marijuana") from December 2015 through August 2019 from an opioid use subreddit and an opioid recovery subreddit. To investigate how cannabis is discussed at-scale, we identified and compared the most frequent phrases in cannabis-related posts in each subreddit using term-frequency-inverse document frequency (TF-IDF) weighting. To contextualize these findings, we also conducted a qualitative content analysis of 200 random posts (100 from each subreddit). Cannabis-related posts were about twice as prevalent in the recovery subreddit (n = 908; 5.4% of 16,791 posts) than in the active opioid use subreddit (n = 4,224; 2.6% of 159,994 posts, p < .001). The most frequent phrases from the recovery subreddit referred to time without using opioids and the possibility of using cannabis as a "treatment." The most frequent phrases from the opioid subreddit referred to concurrent use of cannabis and opioids. The most common motivations for using cannabis were to manage opioid withdrawal symptoms in the recovery subreddit, often in conjunction with anti-anxiety and GI-distress "comfort meds," and to enhance the "high" when used in combination with opioids in the opioid subreddit. Despite limitations in generalizability from pseudonymous online posts, this examination of reports of naturalistic cannabis use in relation to opioid use identified withdrawal symptom management as a common motivation. Future research is warranted with more structured assessments that examines the role of cannabis and cannabinoids in addressing both somatic and affective symptoms of opioid withdrawal.
... In fact, these consequences are compounded because OMHCs cater to sensitive population of individuals (ones possibly struggling with mental health challenges). For instance, diagnosing, suggesting, and adopting drugs and alternative treatments without clinical corroboration can adversely affect individuals [14,45,67]. Further, since OMHCs are largely peer-driven platforms, it is essential to ensure the quality, credibility, and supportiveness of content being shared, so that these communities facilitate positive health and behavior change [12]. ...
Conference Paper
Full-text available
Online Mental Health Communities (OMHCs) enable individuals to seek and provide support, and serve as a safe haven to disclose and share stigmatizing and sensitive experiences. Like other online communities, OMHCs are not immune to bad behavior and antisocial activities such as trolling, spamming, and harassment. Therefore, these communities are oftentimes guided by strict norms against such behavior , and moderated to ensure the quality and credibility of the content being shared. However, moderation within these communities is not only limited to ensuring content quality. It is far more complex-providing supportive spaces for disclosure, ensuring individuals' privacy, etc.-because of the sensitive population that they cater to. By interviewing 19 moderators across 12 such OMHCs on Reddit, this paper studies the practices and structure of moderation in these communities to better understand their functioning and effectiveness. Our research questions primarily revolve around three major themes-moderation, support, and self-disclosure. We find practices of moderation hierarchy, and several distinctions in motivations and responsibilities of the moderators individually and as a group. We also notice that these communities predominantly encourage emotional support, and provide supportive spaces that encourage self-disclosure on stigmatized concerns. Our findings highlight the necessity of awareness corresponding to (currently lacking) privacy concerns, and raises the importance of the presence of mental health experts (counselors and psychiatrists) in these communities. On the basis of the insights drawn from this work, we discuss the implications and considerations for designing OMHCs.
... Complementary to the problem of drug-abuse detection, works based on identifying alternative opioid recovery treatments on social media also exist [22]. Such data can be leveraged to gain insights about the microscopic behavior of the corresponding users and their role in spreading drug-abuse tweets in the network, although only a few works have attempted to do so [2], [5]. ...
Article
In this article, we perform a large-scale study of the Twitter follower network, involving around 0.42 million users who justify DA, to characterize the spreading of DA tweets across the network. Our observations reveal the existence of a very large giant component involving 99% of these users with dense local connectivity that facilitates the spreading of such messages. We further identify active cascades over the network and observe that the cascades of DA tweets get spread over a long distance through the engagement of several closely connected groups of users. Moreover, our observations also reveal a collective phenomenon, involving a large set of active fringe nodes (with a small number of follower and following) along with a small set of well-connected nonfringe nodes that work together toward such spread, thus potentially complicating the process of arresting such cascades. Furthermore, we discovered that the engagement of the users with respect to certain drugs, such as Vicodin, Percocet, and OxyContin, that were observed to be most mentioned in Twitter is instantaneous. On the other hand, for drugs, such as Lortab, that found lesser mentions, the engagement probability becomes high with increasing exposure to such tweets, thereby indicating that drug abusers engaged on Twitter remain vulnerable to adopting newer drugs, aggravating the problem further.
... In another study by Park and Conway (2018), the authors focused on the way URLs which contained some kind of opioid drug promotion were shared on Reddit. Several other studies examined the opioid crisis on Reddit by focusing on alternative treatments and recovery communities (Chancellor et al., 2019). ...
Article
Fentanyl sale is often promoted on social media, but there is very little empirical and systematic research on this issue. This study attempts to fill a gap in the literature by utilizing big data and computational as well as qualitative analyses to better understand this phenomenon. The study’s sample includes a unique dataset of over 6 million tweets and Instagram posts referencing fentanyl including 5687 messages (856 tweets and 4831 Instagram posts) that promoted fentanyl sale and provided some instructions on where and how to purchase it. From a theoretical point of view, it discusses the concept of Dark Social Networking Sites (DSNS) by building and expanding on the previous literature on dark social media. Second, it offers an assessment of the content posted and major strategies used by opioid drug dealers on social media which mostly involve exchanging contact information of mobile communication apps in order to arrange for money transfers and deliveries.
... We chose Reddit over other social networks or web-based forums such as Twitter [20], Bluelight [21], and Discord [22] for several reasons. While all these sources contain information about substance use, the substance use community of Reddit is much larger and has been extensively used in peerreviewed research related to substance use and emerging substance use trends [23][24][25]. Reddit content is also moderated, and posts that do not adhere to the rules of a subreddit are removed by its moderators. Consequently, while these rules restrict some types of information from being posted, they also ensure that the data are reflective of the topical areas and the volume of spam, posts from bots, or irrelevant content is thereby lower. ...
Article
Full-text available
Background Despite recent rises in fatal overdoses involving multiple substances, there is a paucity of knowledge about stimulant co-use patterns among people who use opioids (PWUO) or people being treated with medications for opioid use disorder (PTMOUD). A better understanding of the timing and patterns in stimulant co-use among PWUO based on mentions of these substances on social media can help inform prevention programs, policy, and future research directions. This study examines stimulant co-mention trends among PWUO/PTMOUD on social media over multiple years. Methods We collected publicly available data from 14 forums on Reddit (subreddits) that focused on prescription and illicit opioids, and medications for opioid use disorder (MOUD). Collected data ranged from 2011 to 2020, and we also collected timelines comprising past posts from a sample of Reddit users (Redditors) on these forums. We applied natural language processing to generate lexical variants of all included prescription and illicit opioids and stimulants and detect mentions of them on the chosen subreddits. Finally, we analyzed and described trends and patterns in co-mentions. Results Posts collected for 13,812 Redditors showed that 12,306 (89.1%) mentioned at least 1 opioid, opioid-related medication, or stimulant. Analyses revealed that the number and proportion of Redditors mentioning both opioids and/or opioid-related medications and stimulants steadily increased over time. Relative rates of co-mentions by the same Redditor of heroin and methamphetamine, the substances most commonly co-mentioned, decreased in recent years, while co-mentions of both fentanyl and MOUD with methamphetamine increased. Conclusion Our analyses reflect increasing mentions of stimulants, particularly methamphetamine, among PWUO/PTMOUD, which closely resembles the growth in overdose deaths involving both opioids and stimulants. These findings are consistent with recent reports suggesting increasing stimulant use among people receiving treatment for opioid use disorder. These data offer insights on emerging trends in the overdose epidemic and underscore the importance of scaling efforts to address co-occurring opioid and stimulant use including harm reduction and comprehensive healthcare access spanning mental-health services and substance use disorder treatment.
... In this study, we empirically assess the value of a particularly prominent type of online volunteer work-Reddit volunteer moderation. Reddit is one of the most visited websites in the U.S., with fifty-two million daily active users (Patel, 2020), and the website actively plays a role in the public's news consumption (Stoddard, 2015), topical discussions (Gilbert, 2020), and social support for mental health (Chancellor et al., 2019(Chancellor et al., , 2016bChoudhury and Kiciman, 2017). Reddit is organized into thousands of topical communities, called subreddits. ...
Preprint
Full-text available
Online volunteers are a crucial labor force that keeps many for-profit systems afloat (e.g. social media platforms and online review sites). Despite their substantial role in upholding highly valuable technological systems, online volunteers have no way of knowing the value of their work. This paper uses content moderation as a case study and measures its monetary value to make apparent volunteer labor's value. Using a novel dataset of private logs generated by moderators, we use linear mixed-effect regression and estimate that Reddit moderators worked a minimum of 466 hours per day in 2020. These hours amount to 3.4 million USD a year based on the median hourly wage for comparable content moderation services in the U.S. We discuss how this information may inform pathways to alleviate the one-sided relationship between technology companies and online volunteers.
... In the same survey, 14%, 12%, and 4% of Hispanic, White, and Black Americans respectively and 9%, 10%, and 15% of those with annual incomes less than $30,000, $30,000 to $74,999, and greater than $75,000 respectively reported using Reddit. Previous studies have used machine learning and natural language processing methods to analyze Reddit posts regarding casual drug discussions, opioid addiction, and alternative treatments for opioid use recovery (Park and Conway, 2018;Chancellor et al., 2019;Lu et al., 2019;Alambo et al., 2021). However, no known studies focus on fentanyl discussions in particular. ...
Article
Full-text available
Introduction Opioid misuse is a public health crisis in the US, and misuse of synthetic opioids such as fentanyl have driven the most recent waves of opioid-related deaths. Because those who misuse fentanyl are often a hidden and high-risk group, innovative methods for identifying individuals at risk for fentanyl misuse are needed. Machine learning has been used in the past to investigate discussions surrounding substance use on Reddit, and this study leverages similar techniques to identify risky content from discussions of fentanyl on this platform. Methods A codebook was developed by clinical domain experts with 12 categories indicative of fentanyl misuse risk, and this was used to manually label 391 Reddit posts and comments. Using this data, we built machine learning classification models to identify fentanyl risk. Results Our machine learning risk model was able to detect posts or comments labeled as risky by our clinical experts with 76% accuracy and 76% sensitivity. Furthermore, we provide a vocabulary of community-specific, colloquial words for fentanyl and its analogues. Discussion This study uses an interdisciplinary approach leveraging machine learning techniques and clinical domain expertise to automatically detect risky discourse, which may elicit and benefit from timely intervention. Moreover, our vocabulary of online terms for fentanyl and its analogues expands our understanding of online “street” nomenclature for opiates. Through an improved understanding of substance misuse risk factors, these findings allow for identification of risk concepts among those misusing fentanyl to inform outreach and intervention strategies tailored to this at-risk group.
Conference Paper
Textual comments from peers with informational and emotional support are beneficial to members of online mental health communities (OMHCs). However, many comments are not of high quality in reality. Writing support technologies that assess (AS) the text or recommend (RE) writing examples on the fly could potentially help support providers to improve the quality of their comments. However, how providers perceive and work with such technologies are under-investigated. In this paper, we present a technological prototype MepsBot which offers providers in-situ writing assistance in either AS or RE mode. Results of a mixed-design study with 30 participants show that both types of MepsBots improve users’ confidence in and satisfaction with their comments. The AS-mode MepsBot encourages users to refine expressions and is deemed easier to use, while the RE-mode one stimulates more support-related content re-editions. We report concerns on MepsBot and propose design considerations for writing support technologies in OMHCs.
Preprint
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Prescription medication (PM) misuse/abuse has emerged as a national crisis in the United States, and social media has been suggested as a potential resource for performing active monitoring. However, automating a social media-based monitoring system is challenging--requiring advanced natural language processing (NLP) and machine learning methods. In this paper, we describe the development and evaluation of automatic text classification models for detecting self-reports of PM abuse from Twitter. We experimented with state-of-the-art bi-directional transformer-based language models, which utilize tweet-level representations that enable transfer learning (e.g., BERT, RoBERTa, XLNet, AlBERT, and DistilBERT), proposed fusion-based approaches, and compared the developed models with several traditional machine learning, including deep learning, approaches. Using a public dataset, we evaluated the performances of the classifiers on their abilities to classify the non-majority "abuse/misuse" class. Our proposed fusion-based model performs significantly better than the best traditional model (F1-score [95% CI]: 0.67 [0.64-0.69] vs. 0.45 [0.42-0.48]). We illustrate, via experimentation using differing training set sizes, that the transformer-based models are more stable and require less annotated data compared to the other models. The significant improvements achieved by our best-performing classification model over past approaches makes it suitable for automated continuous monitoring of nonmedical PM use from Twitter.
Preprint
Substance Use Disorders (SUDs) involve the misuse of any or several of a wide array of substances, such as alcohol, opioids, marijuana, and methamphetamine. SUDs are characterized by an inability to decrease use despite severe social, economic, and health-related consequences to the individual. A 2017 national survey identified that 1 in 12 US adults have or have had a substance use disorder. The National Institute on Drug Abuse estimates that SUDs relating to alcohol, prescription opioids, and illicit drug use cost the United States over $520 billion annually due to crime, lost work productivity, and health care expenses. Most recently, the US Department of Health and Human Services has declared the national opioid crisis a public health emergency to address the growing number of opioid overdose deaths in the United States. In this interdisciplinary workshop, we explored how computational support - digital systems, algorithms, and sociotechnical approaches (which consider how technology and people interact as complex systems) - may enhance and enable innovative interventions for prevention, detection, treatment, and long-term recovery from SUDs. The Computing Community Consortium (CCC) sponsored a two-day workshop titled "Computational Support for Substance Use Disorder Prevention, Detection, Treatment, and Recovery" on November 14-15, 2019 in Washington, DC. As outcomes from this visioning process, we identified three broad opportunity areas for computational support in the SUD context: 1. Detecting and mitigating risk of SUD relapse, 2. Establishing and empowering social support networks, and 3. Collecting and sharing data meaningfully across ecologies of formal and informal care.
Preprint
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Background Prescription medication (PM) misuse/abuse has emerged as a national crisis in the United States, and social media has been suggested as a potential resource for performing active monitoring. However, automating a social media-based monitoring system is challenging—requiring advanced natural language processing (NLP) and machine learning methods. In this paper, we describe the development and evaluation of automatic text classification models for detecting self-reports of PM abuse from Twitter. Methods We experimented with state-of-the-art bi-directional transformer-based language models, which utilize tweet-level representations that enable transfer learning (e.g., BERT, RoBERTa, XLNet, AlBERT, and DistilBERT), proposed fusion-based approaches, and compared the developed models with several traditional machine learning, including deep learning, approaches. Using a public dataset, we evaluated the performances of the classifiers on their abilities to classify the non-majority “abuse/misuse” class. Results Our proposed fusion-based model performs significantly better than the best traditional model (F1-score [95% CI]: 0.67 [0.64–0.69] vs. 0.45 [0.42–0.48]). We illustrate, via experimentation using differing training set sizes, that the transformer-based models are more stable and require less annotated data compared to the other models. The significant improvements achieved by our best-performing classification model over past approaches makes it suitable for automated continuous monitoring of nonmedical PM use from Twitter. Conclusions BERT, BERT-like and fusion-based models not only outperform traditional machine learning and deep learning models, but also show substantial improvements over many years of past research on the topic of prescription medication misuse/abuse classification from social media, which had been shown to be a complex task due to the unique ways in which information about nonmedical use is presented. However, several challenges, such as lack of complete context and the nature of social media language, must be overcome to further improve BERT and BERT-like models despite their advantages over other approaches. These experimental driven challenges are represented as potential future research directions.
Article
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Objective: Prescription medication (PM) misuse and abuse is a major health problem globally, and a number of recent studies have focused on exploring social media as a resource for monitoring nonmedical PM use. Our objectives are to present a methodological review of social media-based PM abuse or misuse monitoring studies, and to propose a potential generalizable, data-centric processing pipeline for the curation of data from this resource. Materials and methods: We identified studies involving social media, PMs, and misuse or abuse (inclusion criteria) from Medline, Embase, Scopus, Web of Science, and Google Scholar. We categorized studies based on multiple characteristics including but not limited to data size; social media source(s); medications studied; and primary objectives, methods, and findings. Results: A total of 39 studies met our inclusion criteria, with 31 (∼79.5%) published since 2015. Twitter has been the most popular resource, with Reddit and Instagram gaining popularity recently. Early studies focused mostly on manual, qualitative analyses, with a growing trend toward the use of data-centric methods involving natural language processing and machine learning. Discussion: There is a paucity of standardized, data-centric frameworks for curating social media data for task-specific analyses and near real-time surveillance of nonmedical PM use. Many existing studies do not quantify human agreements for manual annotation tasks or take into account the presence of noise in data. Conclusion: The development of reproducible and standardized data-centric frameworks that build on the current state-of-the-art methods in data and text mining may enable effective utilization of social media data for understanding and monitoring nonmedical PM use.
Article
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Background The current opioid crisis in the United States impacts broad population groups, including pregnant women. Opioid use during pregnancy can affect the health and wellness of both mothers and their infants. Understanding women’s efforts to self-manage opioid use or misuse in pregnancy is needed to identify intervention points for improving maternal outcomes. Objective This study aims to identify the characteristics of women in an online health community (OHC) with opioid use or misuse during pregnancy and the self-management support needs of these mothers. MethodsA total of 200 web posts by pregnant women with opioid use participating in an OHC were double coded. Concepts and their thematic connections were identified through an inductive process until theoretical saturation was reached. Statistical tests were performed to identify patterns. ResultsThe majority of pregnant women (150/200, 75.0%) in the OHC exhibited signs of misuse, and 62.5% (125/200) of the participants were either contemplating or pursuing dosage reduction. Self-managed withdrawal was more common (P
Article
Background Mental illness is a growing concern within many college campuses. Limited access to therapy resources, along with the fear of stigma, often prevents students from seeking help. Introducing supportive interventions, coping strategies, and mitigation programs might decrease the negative effects of mental illness among college students. Objective Many college students find social support for a variety of needs through social media platforms. With the pervasive adoption of social media sites in college populations, in this study, we examine whether and how these platforms may help meet college students’ mental health needs. Methods We first conducted a survey among 101 students, followed by semistructured interviews (n=11), of a large public university in the southeast region of the United States to understand whether, to what extent, and how students appropriate social media platforms to suit their struggle with mental health concerns. The interviews were intended to provide comprehensive information on students’ attitudes and their perceived benefits and limitations of social media as platforms for mental health support. Results Our survey revealed that a large number of participating students (71/101, 70.3%) had recently experienced some form of stress, anxiety, or other mental health challenges related to college life. Half of them (52/101, 51.5%) also reported having appropriated some social media platforms for self-disclosure or help, indicating the pervasiveness of this practice. Through our interviews, we obtained deeper insights into these initial observations. We identified specific academic, personal, and social life stressors; motivations behind social media use for mental health needs; and specific platform affordances that helped or hindered this use. Conclusions Students recognized the benefits of social media in helping connect with peers on campus and promoting informal and candid disclosures. However, they argued against complete anonymity in platforms for mental health help and advocated the need for privacy and boundary regulation mechanisms in social media platforms supporting this use. Our findings bear implications for informing campus counseling efforts and in designing social media–based mental health support tools for college students.
Article
Background The coronavirus disease (COVID-19) pandemic has impacted patients receiving methadone maintenance treatment (MMT) through opioid treatment programs (OTPs), especially because of the unique challenges of the care delivery model. Previously, documentation of patient experiences during emergencies often comes years after the fact, in part because there is a substantial data void in real-time. Methods: We extracted 308 posts that mention COVID-19 keywords on r/methadone, an online community for patients receiving MMT to share information, on Reddit occurring between January 31, 2020 and September 30, 2020. 215 of these posts self-report an impact to their MMT. Using qualitative content analysis, we characterized the impacts described in these posts and identified four emergent themes describing patients’ experience of impacts to MMT during COVID-19. Results: The themes included (1) 54.4% of posts reporting impediments to accessing their methadone, (2) 28.4% reporting impediments to accessing physicial OTPs, (3) 19.5% reporting having to self-manage their care, and (4) 4.7% reporting impediments to accessing OTP providers and staff. Conclusions: Patients described unanticipated consequences to one-size-fits-all policies that are unevenly applied resulting in suboptimal dosing, increased perceived risk of acquiring COVID-19 at OTPs, and reduced interaction with OTP providers and staff. While preliminary, these results are formative for follow-up surveillance metrics for patients of OTPs as well as digitally-mediated resource needs for this online community. This study serves as a model of how social media can be employed during and after emergencies to hear the lived experiences of patients for informed emergency preparedness and response.
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Background Stigma associated with substance use can have severe negative consequences for physical and mental health and serve as a barrier to treatment. Yet, research on stigma processes and stigma reduction interventions is limited. Aim We use a social media dataset to examine: 1) the nature of stigma-related experience related to substance use; and 2) affective and temporal factors that are salient in the use of three substances: alcohol, cannabis, and opioids. Methods We harvested several years of data pertaining to three substances – alcohol, cannabis, and opioids – from Reddit, a popular social networking platform. For Part I, we selected posts based on stigma-related keywords, performed content analysis, and rendered word clouds to examine the nature of stigma associated with these substances. In Part II, we employed natural language processing in conjunction with hierarchical clustering, and visualization, to explore the salience of temporal and affective factors. Results In Part I, internalized stigma was most commonly exhibited. Anticipated and enacted stigma were less common in posts relating to cannabis compared to the other two substances. Work, home, and school were important contexts in which stigma was observed. Part II showed that temporal markers were prominent; post authors shared stories of substance use journeys, and timelines of their experience with quitting and withdrawals. Shame, sadness, anxiety, and fear were common, with shame being more prominent in alcohol-related posts. Conclusion Our findings highlight the importance of contextual factors in substance use recovery and stigma reduction and offer directions for future interventions.
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Background: Deaths due to opioid overdose continue to rise in the United States. Despite availability of effective treatment for opioid use disorder, uptake is low among those who misuse opioids. Methods: This paper explores the role of misconception, stigma, and misinformation in influencing decisions to initiate medications for opioid use disorder among patients and providers. Conclusion: Misinformation about opioids has been prevalent among future healthcare providers and first responders as well as pharmaceutical companies, which may have implications for treatment. Among individuals with opioid use disorder, treatment uptake and adherence have been negatively affected by misconceptions about treatment efficacy and side effects, as well as stigma. We discuss the role of social media, education, and the community, in mitigating misinformation and addressing misconceptions about opioids and treatment options.
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Substances involved in overdose deaths have shifted over time and continue to undergo transition. Early detection of emerging drugs involved in overdose is a major challenge for traditional public health data systems. While novel social media data have shown promise, there is a continued need for robust natural language processing approaches that can identify emerging substances. Consequently, we developed a new metric, the relative similarity ratio, based on diachronic word embeddings to measure movement in the semantic proximity of individual substance words to ‘overdose’ over time. Our analysis of 64,420,376 drug-related posts made between January 2011 and December 2018 on Reddit, the largest online forum site, reveals that this approach successfully identified fentanyl, the most significant emerging substance in the overdose epidemic, >1 year earlier than traditional public health data systems. Use of diachronic word embeddings may enable improved identification of emerging substances involved in drug overdose, thereby improving the timeliness of prevention and treatment activities.
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Objective: We aim to understand (1) the frequency of URL sharing and (2) types of shared URLs among opioid related discussions that take place in the social media platform called Reddit.Introduction: Nearly 100 people per day die from opioid overdose in the United States. Further, prescription opioid abuse is assumed to be responsible for a 15-year increase in opioid overdose deaths1. However, with increasing use of social media comes increasing opportunity to seek and share information. For instance, 80% of Internet users obtain health information online2, including popular social interaction sites like Reddit (http://www.reddit.com), which had more than 82.5 billion page views in 20153. In Reddit, members often share information, and include URLs to supplement the information. Understanding the frequency of URL sharing and types of shared URLs can improve our knowledge of information seeking/sharing behaviors as well as domains of shared information on social media. Such knowledge has the potential to provide opportunities to improve public health surveillance practice. We use Reddit to track opioid related discussions and then investigate types of shared URLs among Reddit members in those discussions.Methods: First, we use a dataset4—made available on Reddit—that has been used in several informatics studies5,6. The dataset is comprised of 13,213,173 unique member IDs, 114,320,798 posts, and 1,659,361,605 associated comments that are made on 239,772 (including active and inactive) subreddits (i.e., sub-communities) from October 2007 to May 2015. Second, we identified 9 terms that are associated with opioids. The terms are 'opioid', 'opium', 'morphine', 'opiate', 'hydrocodone', 'oxycodone', 'fentanyl', 'heroin', and 'methadone'. Third, we preprocessed the entire dataset (i.e., converting text to lower cases and removing punctuation) and extracted discussions with opioid terms and their metadata (e.g., user ID, post ID) via a lexicon-based approach. Fourth, we extracted URLs using Python from these discussions, categorized the URLs by domain, and then visualized the results in a bubble chart7.Results: We extracted 1,121,187 posts/comments that were made by 328,179 unique member IDs from 8,892 subreddits. Of the 1,121,187 posts/comments, 82,639 posts/comments contained URLs (7.37%), and these posts consisted of 272,551 individual URLs and 138,206 unique URLs. The types of shared URLs in these opioid related discussions are summarized in Figure 1. The color and size represent the type and size respectively of shared URLs. The ‘.com’ is in blue; ‘.org’ is in orange; and ‘.gov’ is in green.Conclusions: We present preliminary findings concerning the types of shared URLs in opioid-related discussions among Reddit members. Our initial results suggest that Reddit members openly discuss opioid related issues and URL sharing is a part of information sharing. Although members share many URLs from reliable information sources (e.g., ‘ncbi.nlm.nih.gov’, ‘wikipedia.org, ‘nytimes.com’, ‘sciencedirect.com’), further investigation is needed concerning many of the ‘.com’ URLs, which have the potential to contain high and/or low quality information (e.g., ‘youtube.com’, ‘reddit.com’, ‘google.com’, ‘amazon.com’) to fully understand information seeking/sharing behaviors on social media and to identify opportunities, such as misinformation dissemination for improving public health surveillance practice.
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We investigated if participants in social media surveillance studies could be reverse identified by reviewing all articles published on PubMed in 2015 or 2016 with the words “Twitter” and either “read,” “coded,” or “content” in the title or abstract. Seventy-two percent (95% CI: 63–80) of articles quoted at least one participant’s tweet and searching for the quoted content led to the participant 84% (95% CI: 74–91) of the time. Twenty-one percent (95% CI: 13–29) of articles disclosed a participant’s Twitter username thereby making the participant immediately identifiable. Only one article reported obtaining consent to disclose identifying information and institutional review board (IRB) involvement was mentioned in only 40% (95% CI: 31–50) of articles, of which 17% (95% CI: 10–25) received IRB-approval and 23% (95% CI:16–32) were deemed exempt. Biomedical publications are routinely including identifiable information by quoting tweets or revealing usernames which, in turn, violates ICMJE ethical standards governing scientific ethics, even though said content is scientifically unnecessary. We propose that authors convey aggregate findings without revealing participants’ identities, editors refuse to publish reports that reveal a participant’s identity, and IRBs attend to these privacy issues when reviewing studies involving social media data. These strategies together will ensure participants are protected going forward.
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Background: Online discussion forums allow those in addiction recovery to seek help through text-based messages, including when facing triggers to drink or use drugs. Trained staff (or "moderators") may participate within these forums to offer guidance and support when participants are struggling but must expend considerable effort to continually review new content. Demands on moderators limit the scalability of evidence-based digital health interventions. Objective: Automated identification of recovery problems could allow moderators to engage in more timely and efficient ways with participants who are struggling. This paper aimed to investigate whether computational linguistics and supervised machine learning can be applied to successfully flag, in real time, those discussion forum messages that moderators find most concerning. Methods: Training data came from a trial of a mobile phone-based health intervention for individuals in recovery from alcohol use disorder, with human coders labeling discussion forum messages according to whether or not authors mentioned problems in their recovery process. Linguistic features of these messages were extracted via several computational techniques: (1) a Bag-of-Words approach, (2) the dictionary-based Linguistic Inquiry and Word Count program, and (3) a hybrid approach combining the most important features from both Bag-of-Words and Linguistic Inquiry and Word Count. These features were applied within binary classifiers leveraging several methods of supervised machine learning: support vector machines, decision trees, and boosted decision trees. Classifiers were evaluated in data from a later deployment of the recovery support intervention. Results: To distinguish recovery problem disclosures, the Bag-of-Words approach relied on domain-specific language, including words explicitly linked to substance use and mental health ("drink," "relapse," "depression," and so on), whereas the Linguistic Inquiry and Word Count approach relied on language characteristics such as tone, affect, insight, and presence of quantifiers and time references, as well as pronouns. A boosted decision tree classifier, utilizing features from both Bag-of-Words and Linguistic Inquiry and Word Count performed best in identifying problems disclosed within the discussion forum, achieving 88% sensitivity and 82% specificity in a separate cohort of patients in recovery. Conclusions: Differences in language use can distinguish messages disclosing recovery problems from other message types. Incorporating machine learning models based on language use allows real-time flagging of concerning content such that trained staff may engage more efficiently and focus their attention on time-sensitive issues.
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Background: Legalization of medical and recreational cannabis has coincided with an increase in novel forms of cannabis use and a burgeoning cannabis product industry. This research seeks to understand the occurrence of discussions about these emerging and traditional forms of use in an online social media discussion forum. Methods: We analyzed posts to a cannabis-specific forum on the Reddit social media platform posted from January 2010-December 2016. For each of various keywords describing smoking, vaping, edibles, dabbing, and butane hash oil (BHO) concentrate use, we analyzed (1) relative prevalence of posts mentioning these cannabis forms of use; (2) user-reported subjective ratings of "highness" on a scale of 1-10; (3) the ten most common words mentioned in posts; and (4) the frequency of adverse health effect terms. Results: Form of use was mentioned in approximately 17.7% of 2.26 million posts; smoking was the most commonly mentioned form of cannabis use. From 2010-2016, relative post volume increased significantly for posts mentioning dabbing (3.63/1000 additional posts per year, p < .001), butane hash oil terms (3.16/1000, p < .001), and edible terms (2.84/1000, p = .002). Mean subjective highness was significantly greater for posts mentioning dabbing (mean = 7.8, p < .001), butane hash oil terms (mean = 7.5, p < .001), and edible terms (mean = 7.2, p < .001) but not significantly different for vaping (mean = 6.7, p = .19), when compared to smoking (mean = 6.8). Conclusions: Despite limitations in representativeness, findings indicate a significant increase in online discussion of emerging cannabis forms of use over time and greater subjective effects of dabbing, butane hash oil, and edible use.
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Opioid (e.g., heroin and morphine) addiction has become one of the largest and deadliest epidemics in the United States. To combat such deadly epidemic, there is an urgent need for novel tools and methodologies to gain new insights into the behavioral processes of opioid abuse and addiction. The role of social media in biomedical knowledge mining has turned into increasingly significant in recent years. In this paper, we propose a novel framework named AutoDOA to automatically detect the opioid addicts from Twitter, which can potentially assist in sharpening our understanding toward the behavioral process of opioid abuse and addiction. In AutoDOA, to model the users and posted tweets as well as their rich relationships, a structured heterogeneous information network (HIN) is first constructed. Then meta-path based approach is used to formulate similarity measures over users and different similarities are aggregated using Laplacian scores. Based on HIN and the combined meta-path, to reduce the cost of acquiring labeled examples for supervised learning, a transductive classification model is built for automatic opioid addict detection. To the best of our knowledge, this is the first work to apply transductive classification in HIN into drug-addiction domain. Comprehensive experiments on real sample collections from Twitter are conducted to validate the effectiveness of our developed system AutoDOA in opioid addict detection by comparisons with other alternate methods. The results and case studies also demonstrate that knowledge from daily-life social media data mining could support a better practice of opioid addiction prevention and treatment.
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Stress constitutes a persistent wellbeing challenge to college students, impacting their personal, social, and academic life. However, violent events on campuses may aggravate student stress, due to the induced fear and trauma. In this paper, leveraging social media as a passive sensor of stress, we propose novel computational techniques to quantify and examine stress responses after gun violence on college campuses. We first present a machine learning classifier for inferring stress expression in Reddit posts, which achieves an accuracy of 82%. Next, focusing on 12 incidents of campus gun violence in the past five years, and social media data gathered from college Reddit communities, our methods reveal amplified stress levels following the violent incidents, which deviate from usual stress patterns on the campuses. Further, distinctive temporal and linguistic changes characterize the campus populations, such as reduced cognition, higher self pre-occupation and death-related conversations. We discuss the implications of our work in improving mental wellbeing and rehabilitation efforts around crisis events in college student populations.
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Objective We aim to develop an automated method to track opium relateddiscussions that are made in the social media platform calledReddit.As a first step towards this goal, we use a keyword-based approach totrack how often Reddit members discuss opium related issues.IntroductionIn recent years, the use of social media has increased at anunprecedented rate. For example, the popular social media platformReddit (http://www.reddit.com) had 83 billion page views from over88,000 active sub-communities (subreddits) in 2015. Members ofReddit made over 73 million individual posts and over 725 millionassociated comments in the same year [1].We use Reddit to track opium related discussions, because Redditallows for throwaway and unidentifiable accounts that are suitable forstigmatized discussions that may not be appropriate for identifiableaccounts. Reddit members exchange conversation via a forum likeplatform, and members who have achieved a certain status withinthe community are able to create new topically focused group calledsubreddits.Methods First, we use a dataset archived by one of Reddit members who usedReddit’s official Application Programming Interface (API) to collectthe data (https://www.reddit.com/r/datasets/comments/3bxlg7/i_have_every_publicly_available_reddit_comment/). The dataset iscomprised of 239,772 (including both active and inactive) subreddits,13,213,173 unique user IDs, 114,320,798 posts, and 1,659,361,605associated comments that are made from Oct of 2007 to May of 2015.Second, we identify 10 terms that are associated with opium. Theterms are ‘opium’, ‘opioid’, ‘morphine’, ‘opiate’,’ hydrocodone’,‘oxycodone’, ‘fentanyl’, ‘oxy’, ‘heroin’, ‘methadone’. Third, wepreprocess the entire dataset, which includes structuring the data intomonthly time frame, converting text to lower cases, and stemmingkeywords and text. Fourth, we employed a dictionary approachto count and extract timestamps, user IDs, posts, and commentscontaining opium related terms. Fifth, we normalized the frequencycount by dividing the frequency count by the overall number of therespective variable for that period.ResultsAccording to our dataset, Reddit members discuss opium relatedtopics in social media. The normalized frequency count of postersshows that less than one percent members, on average, talk aboutopium related topics (Figure 1). Although the community as a wholedoes not frequently talk about opium related issues, this still amountsto more than 10,000 members in 2015 (Figure 2). Moreover, membersof Reddit created a number of subreddits, such as ‘oxycontin’,‘opioid’, ‘heroin’, ‘oxycodon’, that explicitly focus on opioids.Conclusions We present preliminary findings on developing an automatedmethod to track opium related discussions in Reddit. Our initialresults suggest that on the basis of our analysis of Reddit, members ofthe Reddit community discuss opium related issues in social media,although the discussions are contributed by a small fraction of themembers.We provide several interesting directions to future work to bettertrack opium related discussions in Reddit. First, the automated methodneeds to be further developed to employ more sophisticated methodslike knowledge-based and corpus-based approaches to better extractopium related discussions. Second, the automated method needs tobe thoroughly evaluated and measure precision, recall, accuracy, andF1-score of the system. Third, given how many members use socialmedia to discuss these issues, it will be helpful to investigate thespecifics of their discussions.Line Graphs of normalized frequency counts for posters, comments, and poststhat contained opium related termsLine Graphs of raw frequency counts for posters, comments, and posts thatcontained opium related terms
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The consumption of cannabis has substantial implications for medicine, popular culture, and technology use, yet discussion of it is almost entirely absent in the HCI literature. Taking advantage of CHI 2017's location in one of the first jurisdictions to legalize recreational use of marijuana in the U.S., this panel will discuss its socio-technical implications, identify HCI research themes relevant to policy and public health debates, and outline a research agenda.
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Background The nonmedical use of pharmaceutical products has become a significant public health concern. Traditionally, the evaluation of nonmedical use has focused on controlled substances with addiction risk. Currently, there is no effective means of evaluating the nonmedical use of noncontrolled antidepressants. Objective Social listening, in the context of public health sometimes called infodemiology or infoveillance, is the process of identifying and assessing what is being said about a company, product, brand, or individual, within forms of electronic interactive media. The objectives of this study were (1) to determine whether content analysis of social listening data could be utilized to identify posts discussing potential misuse or nonmedical use of bupropion and two comparators, amitriptyline and venlafaxine, and (2) to describe and characterize these posts. Methods Social listening was performed on all publicly available posts cumulative through July 29, 2015, from two harm-reduction Web forums, Bluelight and Opiophile, which mentioned the study drugs. The acquired data were stripped of personally identifiable identification (PII). A set of generic, brand, and vernacular product names was used to identify product references in posts. Posts were obtained using natural language processing tools to identify vernacular references to drug misuse-related Preferred Terms from the English Medical Dictionary for Regulatory Activities (MedDRA) version 18 terminology. Posts were reviewed manually by coders, who extracted relevant details. Results A total of 7756 references to at least one of the study antidepressants were identified within posts gathered for this study. Of these posts, 668 (8.61%, 668/7756) referenced misuse or nonmedical use of the drug, with bupropion accounting for 438 (65.6%, 438/668). Of the 668 posts, nonmedical use was discouraged by 40.6% (178/438), 22% (22/100), and 18.5% (24/130) and encouraged by 12.3% (54/438), 10% (10/100), and 10.8% (14/130) for bupropion, amitriptyline, and venlafaxine, respectively. The most commonly reported desired effects were similar to stimulants with bupropion, sedatives with amitriptyline, and dissociatives with venlafaxine. The nasal route of administration was most frequently reported for bupropion, whereas the oral route was most frequently reported for amitriptyline and venlafaxine. Bupropion and venlafaxine were most commonly procured from health care providers, whereas amitriptyline was most commonly obtained or stolen from a third party. The Fleiss kappa for interrater agreement among 20 items with 7 categorical response options evaluated by all 11 raters was 0.448 (95% CI 0.421-0.457). Conclusions Social listening, conducted in collaboration with harm-reduction Web forums, offers a valuable new data source that can be used for monitoring nonmedical use of antidepressants. Additional work on the capabilities of social listening will help further delineate the benefits and limitations of this rapidly evolving data source.
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Support seeking in stigmatized contexts is useful when the discloser receives the desired response, but it also entails social risks. Thus, people do not always disclose or seek support when they need it. One such stigmatized context for support seeking is sexual abuse. In this paper, we use mixed methods to understand abuse-related posts on reddit. First, we take a qualitative approach to understand post content. Then we use quantitative methods to investigate the use of "throwaway" accounts, which provide greater anonymity, and report on factors associated with support seeking and first-time disclosures. In addition to significant linguistic differences between throwaway and identified accounts, we find that those using throwaway accounts are significantly more likely to engage in seeking support. We also find that men are significantly more likely to use throwaway accounts when posting about sexual abuse. Results suggest that subreddit moderators and members who wish to provide support pay attention to throwaway accounts, and we discuss the importance of context-specific anonymity in support seeking.
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This study investigates the public health intelligence utility of Yik Yak, a social media platform that allows users to anonymously post and view messages within precise geographic locations. Our dataset contains 122,179 " yaks " collected from 120 college campuses across the United States during 2015. We first present an exploratory analysis of the topics commonly discussed in Yik Yak, clarifying the health issues for which this may serve as a source of information. We then present an in-depth content analysis of data describing substance use, an important public health issue that is not often discussed in public social media, but commonly discussed on Yik Yak under the cloak of anonymity.
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Introduction Prescription medication overdose is the fastest growing drug-related problem in the USA. The growing nature of this problem necessitates the implementation of improved monitoring strategies for investigating the prevalence and patterns of abuse of specific medications. Objectives Our primary aims were to assess the possibility of utilizing social media as a resource for automatic monitoring of prescription medication abuse and to devise an automatic classification technique that can identify potentially abuse-indicating user posts. Methods We collected Twitter user posts (tweets) associated with three commonly abused medications (Adderall®, oxycodone, and quetiapine). We manually annotated 6400 tweets mentioning these three medications and a control medication (metformin) that is not the subject of abuse due to its mechanism of action. We performed quantitative and qualitative analyses of the annotated data to determine whether posts on Twitter contain signals of prescription medication abuse. Finally, we designed an automatic supervised classification technique to distinguish posts containing signals of medication abuse from those that do not and assessed the utility of Twitter in investigating patterns of abuse over time. Results Our analyses show that clear signals of medication abuse can be drawn from Twitter posts and the percentage of tweets containing abuse signals are significantly higher for the three case medications (Adderall®: 23 %, quetiapine: 5.0 %, oxycodone: 12 %) than the proportion for the control medication (metformin: 0.3 %). Our automatic classification approach achieves 82 % accuracy overall (medication abuse class recall: 0.51, precision: 0.41, F measure: 0.46). To illustrate the utility of automatic classification, we show how the classification data can be used to analyze abuse patterns over time. Conclusion Our study indicates that social media can be a crucial resource for obtaining abuse-related information for medications, and that automatic approaches involving supervised classification and natural language processing hold promises for essential future monitoring and intervention tasks.
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Recent research has shown that Twitter data analytics can have broad implications on public health research. However, its value for pharmacovigilance has been scantly studied - with health related forums and community support groups preferred for the task. We present a systematic study of tweets collected for 74 drugs to assess their value as sources of potential signals for adverse drug reactions (ADRs). We created an annotated corpus of 10,822 tweets. Each tweet was annotated for the presence or absence of ADR mentions, with the span and Unified Medical Language System (UMLS) concept ID noted for each ADR present. Using Cohen's kappa1, we calculated the inter-annotator agreement (IAA) for the binary annotations to be 0.69. To demonstrate the utility of the corpus, we attempted a lexicon-based approach for concept extraction, with promising success (54.1% precision, 62.1% recall, and 57.8% F-measure). A subset of the corpus is freely available at: http://diego.asu.edu/downloads.
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Substance use disorders, such as alcoholism and drug addiction, are a widespread and hazardous public health issue. Technology designed for the needs and values of people in recovery may be able to supplement traditional treatment options, enhance long-term abstinence maintenance, and create new opportunities for social support. We conducted a series of participatory design workshops with women in recovery from substance use disorders to identify design opportunities for supportive technologies that align with the specific values, practices and traditions of the recovery community. Through a data-driven inductive qualitative analysis, we identify five major themes that may be addressed with technology: 1) supporting twelve-step traditions and practices, 2) management of restlessness and moments of crisis, 3) agency and control over privacy and personal safety, 4) tracking progress and maintaining motivation, and 5) constructing a new normal. We connect these themes to specific implications for design.
Article
Social media's unfettered access has made it an important venue for health discussion and a resource for patients and their loved ones. However, the quality of information available, as well as the motivations of its posters, has been questioned. This work examines the individuals on social media that are posting questionable health-related information, and in particular promoting cancer treatments which have been shown to be ineffective (making it a kind of misinformation, willful or not). Using a multi-stage user selection process, we study 4,212 Twitter users who have posted about one of 139 such "treatments", and compare them to a baseline of users generally interested in cancer. Considering features capturing user attributes, writing style, and sentiment, we build a classifier which is able to identify users prone to propagate such misinformation at an accuracy of over 90%, providing a potential tool for public health officials to identify such individuals for preventive intervention.
Article
Background: For a number of fiscal and practical reasons, data on heroin use have been of poor quality, which has hampered the ability to halt the growing epidemic. Internet search data, such as those made available by Google Trends, have been used as a low-cost, real-time data source for monitoring and predicting a variety of public health outcomes. We aimed to determine whether data on opioid-related internet searches might predict future heroin-related admissions to emergency departments (ED). Methods: Across nine metropolitan statistical areas (MSAs) in the United States, we obtained data on Google searches for prescription and non-prescription opioids, as well as Substance Abuse and Mental Health Services Administration (SAMHSA) data on heroin-related ED visits from 2004 to 2011. A linear mixed model assessed the relationship between opioid-related Internet searches and following year heroin-related visits, controlling for MSA GINI index and total number of ED visits. Results: The best-fitting model explained 72% of the variance in heroin-related ED visits. The final model included the search keywords "Avinza," "Brown Sugar," "China White," "Codeine," "Kadian," "Methadone," and "Oxymorphone." We found regional differences in where and how people searched for opioid-related information. Conclusions: Internet search-based modeling should be explored as a new source of insights for predicting heroin-related admissions. In geographic regions where no current heroin-related data exist, Internet search modeling might be a particularly valuable and inexpensive tool for estimating changing heroin use trends. We discuss the immediate implications for using this approach to assist in managing opioid-related morbidity and mortality in the United States.
Conference Paper
Online health communities (OHCs) provide support across conditions; for weight loss, OHCs offer support to foster positive behavior change. However, weight loss behaviors can also be subverted on OHCs to promote disordered eating practices. Using comments as proxies for support, we use computational linguistic methods to juxtapose similarities and differences in two Reddit weight loss communities, r/proED and r/loseit. We employ language modeling and find that word use in both communities is largely similar. Then, by building a word embedding model, specifically a deep neural network on comment words, we contrast the context of word use and find differences that imply different behavior change goals in these OHCs. Finally, these content and context norms predict whether a comment comes from r/proED or r/loseit. We show that norms matter in understanding how different OHCs provision support to promote behavior change and discuss the implications for design and moderation of OHCs.
Conference Paper
Many online communities cater to the critical and unmet needs of individuals challenged with mental illnesses. Generally, communities engender characteristic linguistic practices, known as norms. Conformance to these norms, or linguistic accommodation, encourages social approval and acceptance. This paper investigates whether linguistic accommodation impacts a specific social feedback: the support received by an individual in an online mental health community. We first quantitatively derive two measures for each post in these communities: 1) the linguistic accommodation it exhibits, and 2) the level of support it receives. Thereafter, we build a statistical framework to examine the relationship between these measures. Although the extent to which accommodation is associated with support varies, we find a positive link between the two, consistent across 55 Reddit communities serving various psychological needs. We discuss how our work surfaces a tension in the functioning of these sensitive communities, and present design implications for improving their support provisioning mechanisms.
Article
Background: The rise in opioid use and overdose has increased the importance of improving data collection methods for the purpose of targeting resources to high-need populations and responding rapidly to emerging trends. Objective: To determine whether Twitter data could be used to identify geographic differences in opioid-related discussion and whether opioid topics were significantly correlated with opioid overdose death rate. Methods: We filtered approximately 10 billion tweets for keywords related to opioids between July 2009 and October 2015. The content of the messages was summarized into 50 topics generated using Latent Dirchlet Allocation, a machine learning analytic tool. The correlation between topic distribution and census region, census division, and opioid overdose death rate were quantified. Results: We evaluated a tweet cohort of 84,023 tweets from 72,211 unique users across the US. Unique opioid-related topics were significantly correlated with different Census Bureau divisions and with opioid overdose death rates at the state and county level. Drug-related crime, language of use, and online drug purchasing emerged as themes in various Census Bureau divisions. Drug-related crime, opioid-related news, and pop culture themes were significantly correlated with county-level opioid overdose death rates, and online drug purchasing was significantly correlated with state-level opioid overdoses. Conclusions: Regional differences in opioid-related topics reflect geographic variation in the content of Twitter discussion about opioids. Analysis of Twitter data also produced topics significantly correlated with opioid overdose death rates. Ongoing analysis of Twitter data could provide a means of identifying emerging trends related to opioids.
Article
Background: Prescription opioid misuse has doubled over the past 10 years and is now a public health epidemic. Analysis of social media data may provide additional insights into opioid misuse to supplement the traditional approaches of data collection (eg, self-report on surveys). Objective: The aim of this study was to characterize representations of codeine misuse through analysis of public posts on Instagram to understand text phrases related to misuse. Methods: We identified hashtags and searchable text phrases associated with codeine misuse by analyzing 1156 sequential Instagram posts over the course of 2 weeks from May 2016 to July 2016. Content analysis of posts associated with these hashtags identified the most common themes arising in images, as well as culture around misuse, including how misuse is happening and being perpetuated through social media. Results: A majority of images (50/100; 50.0%) depicted codeine in its commonly misused form, combined with soda (lean). Codeine misuse was commonly represented with the ingestion of alcohol, cannabis, and benzodiazepines. Some images highlighted the previously noted affinity between codeine misuse and hip-hop culture or mainstream popular culture images. Conclusions: The prevalence of codeine misuse images, glamorizing of ingestion with soda and alcohol, and their integration with mainstream, popular culture imagery holds the potential to normalize and increase codeine misuse and overdose. To reduce harm and prevent misuse, immediate public health efforts are needed to better understand the relationship between the potential normalization, ritualization, and commercialization of codeine misuse.
Article
Background: With the rapid development of new psychoactive substances (NPS) and changes in the use of more traditional drugs, it is increasingly difficult for researchers and public health practitioners to keep up with emerging drugs and drug terms. Substance use surveys and diagnostic tools need to be able to ask about substances using the terms that drug users themselves are likely to be using. Analyses of social media may offer new ways for researchers to uncover and track changes in drug terms in near real time. This study describes the initial results from an innovative collaboration between substance use epidemiologists and linguistic scientists employing techniques from the field of natural language processing to examine drug-related terms in a sample of tweets from the United States. Objective: The objective of this study was to assess the feasibility of using distributed word-vector embeddings trained on social media data to uncover previously unknown (to researchers) drug terms. Methods: In this pilot study, we trained a continuous bag of words (CBOW) model of distributed word-vector embeddings on a Twitter dataset collected during July 2016 (roughly 884.2 million tokens). We queried the trained word embeddings for terms with high cosine similarity (a proxy for semantic relatedness) to well-known slang terms for marijuana to produce a list of candidate terms likely to function as slang terms for this substance. This candidate list was then compared with an expert-generated list of marijuana terms to assess the accuracy and efficacy of using word-vector embeddings to search for novel drug terminology. Results: The method described here produced a list of 200 candidate terms for the target substance (marijuana). Of these 200 candidates, 115 were determined to in fact relate to marijuana (65 terms for the substance itself, 50 terms related to paraphernalia). This included 30 terms which were used to refer to the target substance in the corpus yet did not appear on the expert-generated list and were therefore considered to be successful cases of uncovering novel drug terminology. Several of these novel terms appear to have been introduced as recently as 1 or 2 months before the corpus time slice used to train the word embeddings. Conclusions: Though the precision of the method described here is low enough as to still necessitate human review of any candidate term lists generated in such a manner, the fact that this process was able to detect 30 novel terms for the target substance based only on one month's worth of Twitter data is highly promising. We see this pilot study as an important proof of concept and a first step toward producing a fully automated drug term discovery system capable of tracking emerging NPS terms in real time.
Article
How do individuals in twelve-step fellowships like Alcoholics Anonymous (AA) and Narcotics Anonymous (NA) interpret and enact "anonymity?" In this paper, we answer this question through a mixed-methods investigation. Through secondary analysis of interview data from 26 participants and an online questionnaire (N=285) we found three major interpretations of anonymity among AA and NA members: "unidentifiability," "social contract," and "program over individual." While unidentifiability has been the focus of computing investigations, the other interpretations provide a significant and novel lens on anonymity. To understand how and when the unidentifiability interpretation was most likely to be enacted, we conducted a quantitative analysis of traces of activity in a large online recovery community. We observed that members were less likely to enact "unidentifiability" if they were more connected to the particular community and had more time in recovery. We provide implications for future research on context-specific anonymity and implications for design in online recovery spaces and similar sensitive contexts.
Article
In 2015, Reddit closed several subreddits-foremost among them r/fatpeoplehate and r/CoonTown-due to violations of Reddit's anti-harassment policy. However, the effectiveness of banning as a moderation approach remains unclear: banning might diminish hateful behavior, or it may relocate such behavior to different parts of the site. We study the ban of r/fatpeoplehate and r/CoonTown in terms of its effect on both participating users and affected subreddits. Working from over 100M Reddit posts and comments, we generate hate speech lexicons to examine variations in hate speech usage via causal inference methods. We find that the ban worked for Reddit. More accounts than expected discontinued using the site; those that stayed drastically decreased their hate speech usage-by at least 80%. Though many subreddits saw an influx of r/fatpeoplehate and r/CoonTown "migrants," those subreddits saw no significant changes in hate speech usage. In other words, other subreddits did not inherit the problem. We conclude by reflecting on the apparent success of the ban, discussing implications for online moderation, Reddit and internet communities more broadly.
Article
Background Social media has increasingly become a venue for health discourse and support, particularly for vulnerable individuals. This study examines user-generated content of an online Reddit community targeting individuals recovering from opiate addiction. Methods 100 Reddit posts and their comments were collected from the online community on August 19, 2016. Posts were qualitatively coded for opioid use disorder (OUD) criteria as outlined by the Diagnostic and Statistical Manual of Mental Disorders (DSM-5), as well as other common themes. Comments were coded for expression of distinct therapeutic factors (i.e., instillation of hope, universality, imparting information, and altruism). All posts and comments were coded for addiction phase of the author (i.e., using, withdrawing, recovering). Results 73 unique usernames authored the 100 posts. Among the 73 usernames, 33% (24/73) described enough symptoms in their posts to meet DSM-V criteria for OUD (16/73 or 22% mild severity, 7/73 or 10% moderate severity, and 1/73 or 1% high severity. Among the 100 posts, advice was requested in 43% (43/100) of the posts and support was sought in 24% (24/100) of the posts. There were 511 comments made on the 100 posts, nearly all of which contained at least one distinct therapeutic factor (486/511, 95%) with altruism being the most common (341/511, 67%). Conclusions This research provides validity to the supportive content generated on an online recovery-oriented community, while also revealing discussions of self-reported struggles with OUD among group members. Future research should explore the feasibility of incorporating social media-based peer support into traditional addiction treatments.