Social Media Text Mining Framework for Drug Abuse: An Opioid Crisis Case Analysis

Social media is considered as a promising and viable source of data for gaining insights into various disease conditions, patients’ attitudes and behaviors, and medications. The daily use of social media provides new opportunities for analyzing several aspects of communication. Social media as a big data source can be used to recognize communication and behavioral themes of problematic use of prescription drugs. Mining and analyzing such media have challenges and limitations with respect to topic deduction and data quality. There is a need for a structured approach to efficiently and effectively analyze social media content related to drug abuse in a manner that can mitigate the challenges surrounding the use of this data source. Following a design science research methodology, the research aims at developing and evaluating a framework for mining and analyzing social media content related to drug abuse in a manner that will mitigate challenges and limitations related to topic deduction and data quality. The framework consists of four phases: Topic Discovery and Detection; Data Collection; Data Preparation and Quality; and Analysis and Results. The topic discovery and detection phase consists of a topic expansion stage for the drug abuse related topics that address the research domain and objectives. The topic expansion is based on different terms related to keywords, categories, and characteristics of the topic of interest and the objective of monitoring. To formalize the process and supporting artifacts, we create an ontology for drug abuse that captures the different categories that exist in the topic expansion and the literature. The data collection phase is characterized by the date range, social media platforms, search keywords, and a set of inclusion/exclusion criteria. The data preparation and quality phase is mainly concerned with obtaining high-quality data to mitigate problems with data veracity. In this phase, we pre-process the collected data then we evaluate the quality of the data, with respect to the terms and objectives of the research topic phase, using a data quality evaluation matrix. Finally, in the data analysis phase, the researcher can choose the suitable analysis approach. We used a combination of unsupervised and supervised machine learning approaches, including opinion and content analysis modeling. We demonstrate and evaluate the applicability of the proposed framework to identify common concerns toward opioid crisis from two perspectives; the addicted users’ perspective and the public’s (non-addicted users) perspective. In both cases, data is collected from twitter using Crimson Hexagon, a social media analytics tool for data collection and analysis. Natural language processing is used for data preparation and pre-processing. Different data visualization techniques such as, word clouds and clustering visualization, are used to form a deeper understanding of the relationships among the identified themes for the selected communities. The results help in understanding concerns of the public and opioid addicts towards the opioid crisis in the United States. Results of this study could help in understanding the problem aspects and provide key input when it comes to defining and implementing innovative solutions/strategies to face the opioid epidemic. From a theoretical perspective, this study highlights the importance of developing and adapting text mining techniques to social media for drug abuse. This study proposes a social media text mining framework for drug abuse research which lead to a good quality of datasets. Emphasis is placed on developing methods for improving the discovery and identification of topics in social media domains characterized by a plethora of highly diverse terms and a lack of commonly available dictionary/language by the community such as in the opioid and drug abuse case. From a practical perspective, automatically analyzing social media users’ posts using machine learning tools can help in understanding the public themes and topics that exist in the recent discussions of online users of social media networks. This could help in developing proper mitigation strategies. Examples of such strategies can be gaining insights from the discussion topics to make the opioid media campaigns more effective in preventing opioid misuse. Finally, the study helps address some of the U.S. Department of Health and Human Services (HHS) five-point strategy by providing a systematic approach that could support conducting better research on addiction and drug abuse and strengthening public health data reporting and collection using social media data

Discovering Barriers to Opioid Addiction Treatment from Social Media: A Similarity Network-Based Deep Learning Approach

Opioid use disorder (OUD) refers to the physical and psychological reliance on opioids. OUD costs the US healthcare systems $504 billion annually and poses significant mortality risk for patients. Understanding and mitigating the barriers to OUD treatment is a high-priority area. Current OUD treatment studies rely on surveys with low response rate because of social stigma. In this paper, we explore social media as a new data source to study OUD treatments. We develop the SImilarity Network-based DEep Learning (SINDEL) to discover barriers to OUD treatment from the patient narratives and address the challenge of morphs. SINDEL reaches an F1 score of 76.79%. Thirteen types of OUD treatment barriers were identified and verified by domain experts. This study contributes to IS literature by proposing a novel deep-learning-based analytical approach with impactful implications for health practitioners

Theme-driven Keyphrase Extraction to Analyze Social Media Discourse

Social media platforms are vital resources for sharing self-reported health experiences, offering rich data on various health topics. Despite advancements in Natural Language Processing (NLP) enabling large-scale social media data analysis, a gap remains in applying keyphrase extraction to health-related content. Keyphrase extraction is used to identify salient concepts in social media discourse without being constrained by predefined entity classes. This paper introduces a theme-driven keyphrase extraction framework tailored for social media, a pioneering approach designed to capture clinically relevant keyphrases from user-generated health texts. Themes are defined as broad categories determined by the objectives of the extraction task. We formulate this novel task of theme-driven keyphrase extraction and demonstrate its potential for efficiently mining social media text for the use case of treatment for opioid use disorder. This paper leverages qualitative and quantitative analysis to demonstrate the feasibility of extracting actionable insights from social media data and efficiently extracting keyphrases using minimally supervised NLP models. Our contributions include the development of a novel data collection and curation framework for theme-driven keyphrase extraction and the creation of MOUD-Keyphrase, the first dataset of its kind comprising human-annotated keyphrases from a Reddit community. We also identify the scope of minimally supervised NLP models to extract keyphrases from social media data efficiently. Lastly, we found that a large language model (ChatGPT) outperforms unsupervised keyphrase extraction models, and we evaluate its efficacy in this task.Comment: 11 pages, 2 figures, submitted to ICWSM. This version represents a substantial expansion and refocus of the previous manuscript, including new experiments, expanded data analysis, and comprehensive discussion

Patterns of Routes of Administration and Drug Tampering for Nonmedical Opioid Consumption: Data Mining and Content Analysis of Reddit Discussions

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

Analysis of Deviant Opioid Addiction Treatment Communities on Reddit

As the opioid epidemic in the US continues, many addicts turn to clinically unverified, non-mainstream, deviant recovery methods to ameliorate the symptoms of withdrawal. In this study, we analyze discussion on the social media site Reddit surrounding these treatments. We apply transfer learning methods to train a classifier highly sensitive to recovery-related posts. Based on network analysis of Reddit communities (known as “subreddits”), we generate a list of subreddits where discussion of deviant addiction treatment methods is taking place. Using word embeddings and the testimony of a practicing opioid addiction clinician, we identify potential alternative opioid addiction treatment methods. Applying the classifier to subreddit post data, we generate a dataset consisting of recovery-related discourse. When applied to these posts, topic modeling methods, such as Latent Dirichlet Allocation (LDA), reveal topics discussed within the context of recovery, such as the lifestyle changes associated with kratom use.Undergraduat

Interpretable Subgroup Discovery in Treatment Effect Estimation with Application to Opioid Prescribing Guidelines

The dearth of prescribing guidelines for physicians is one key driver of the current opioid epidemic in the United States. In this work, we analyze medical and pharmaceutical claims data to draw insights on characteristics of patients who are more prone to adverse outcomes after an initial synthetic opioid prescription. Toward this end, we propose a generative model that allows discovery from observational data of subgroups that demonstrate an enhanced or diminished causal effect due to treatment. Our approach models these sub-populations as a mixture distribution, using sparsity to enhance interpretability, while jointly learning nonlinear predictors of the potential outcomes to better adjust for confounding. The approach leads to human-interpretable insights on discovered subgroups, improving the practical utility for decision suppor

The Opioid Epidemic: How Our Society is Contributing to the Stigma

The ongoing opioid crisis in the United States is continuing to worsen as prevention strategies for opioid abuse and addiction are ineffective or improperly regulated. Behaviors of the providers, the public, and the patients must all be addressed to create effective prevention strategies because the attitude toward opioid addiction is a significant factor in the treatment of the disease. Many people view substance abuse as a choice and while this may be an accurate assumption in the beginning, citizens across America are not comprehending the full impact these substances have on a person’s body over time, specifically their brain. In recent years, medical scientists and researchers have termed addiction as a disease but there are still significant barriers for those seeking treatment for it, especially for those in rural communities. The opioid epidemic is exacerbated by a lack of compassion as well as the stigma surrounding addiction, which has clouded the judgment of our health care providers to the point that they have become desensitized while treating a patient struggling with substance abuse. There are several factors contributing to this stigma such as the understanding of addiction as a disease instead of as a willful choice, negative language surrounding substance use, confusing the symptoms of mental and/or physical health issues with symptoms of substance abuse disorders, and limited access to rehabilitation and treatment, which all affect the user’s willingness to seek health care. Keywords: opioid epidemic, addiction, disease, stigma, treatment, compassion, health care providers, mental healt

Addicts Speak: An Exploratory Ethnographic Study of Opioid Addiction

This thesis explores the experiences of people in recovery from opioid addiction in order to better understand the many process of recovery. Employing both participant observation and focused life history interview, and utilizing a grounded theory approach to data analysis, this research emphasizes data-driven conclusions. The research provides numerous insights into the process of recovery from opioid addiction, as well as factors that help to facilitate and sustain the process, the role that services play, and how services can be developed to better meet the needs of those in recovery