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

Text Mining for Social Harm and Criminal Justice Applications

Indiana University-Purdue University Indianapolis (IUPUI)Increasing rates of social harm events and plethora of text data demands the need of employing text mining techniques not only to better understand their causes but also to develop optimal prevention strategies. In this work, we study three social harm issues: crime topic models, transitions into drug addiction and homicide investigation chronologies. Topic modeling for the categorization and analysis of crime report text allows for more nuanced categories of crime compared to official UCR categorizations. This study has important implications in hotspot policing. We investigate the extent to which topic models that improve coherence lead to higher levels of crime concentration. We further explore the transitions into drug addiction using Reddit data. We proposed a prediction model to classify the users’ transition from casual drug discussion forum to recovery drug discussion forum and the likelihood of such transitions. Through this study we offer insights into modern drug culture and provide tools with potential applications in combating opioid crises. Lastly, we present a knowledge graph based framework for homicide investigation chronologies that may aid investigators in analyzing homicide case data and also allow for post hoc analysis of key features that determine whether a homicide is ultimately solved. For this purpose we perform named entity recognition to determine witnesses, detectives and suspects from chronology, use keyword expansion to identify various evidence types and finally link these entities and evidence to construct a homicide investigation knowledge graph. We compare the performance over several choice of methodologies for these sub-tasks and analyze the association between network statistics of knowledge graph and homicide solvability

Identifying Self-Disclosures of Use, Misuse and Addiction in Community-based Social Media Posts

In the last decade, the United States has lost more than 500,000 people from an overdose involving prescription and illicit opioids (https://www.cdc.gov/drugoverdose/epidemic/index.html) making it a national public health emergency (USDHHS, 2017). To more effectively prevent unintentional opioid overdoses, medical practitioners require robust and timely tools that can effectively identify at-risk patients. Community-based social media platforms such as Reddit allow self-disclosure for users to discuss otherwise sensitive drug-related behaviors, often acting as indicators for opioid use disorder. Towards this, we present a moderate size corpus of 2500 opioid-related posts from various subreddits spanning 6 different phases of opioid use: Medical Use, Misuse, Addiction, Recovery, Relapse, Not Using. For every post, we annotate span-level extractive explanations and crucially study their role both in annotation quality and model development. We evaluate several state-of-the-art models in a supervised, few-shot, or zero-shot setting. Experimental results and error analysis show that identifying the phases of opioid use disorder is highly contextual and challenging. However, we find that using explanations during modeling leads to a significant boost in classification accuracy demonstrating their beneficial role in a high-stakes domain such as studying the opioid use disorder continuum. The dataset will be made available for research on Github in the formal version.Comment: Work in progres

Do Group Memberships Online Protect Addicts in Recovery against Relapse? Testing the Social Identity Model of Recovery in the Online World

This is the author accepted manuscript. The final version is available from ACM via the DOI in this recordCSCW 2021: 24th ACM Conference on Computer-Supported Cooperative Work and Social Computing, 23 - 27 October 2021. OnlineThe Social Identity Model of Recovery (SIMOR) suggests that addiction recovery is a journey through time where membership in various groups facilitates success. With the help of computational approaches, we now have access to new resources to study whether a wide variety of different online communities can be part of the addiction recovery journey. In this work, we study the effects of two main social factors on recovery success: first, multiple group membership defined in terms of richness of online community engagement;second, active participation operationalized as the evenness in engagement with these groups. We then model recovery from addiction by applying the extended Cox regression model which accounts for the effect of these two factors on time to relapse. We applied our analysis to a dataset of 457 recovering opioid addicts that self-announced the date of their recovery, indicating that at least 219 (48%) addicts relapsed during the recovery period. We find that multiple group membership – in terms of the number of other forums that a subject had posted in - as well as active participation - in terms of how evenly their posts were spread amongst the different forums - reduced the risk of relapse. We discuss our findings with regards to the opportunity, but also risk, that online group membership poses for recovering opioid addicts, as well as the possible contribution that computational social science methods can make to the study of addiction and recovery.Engineering and Physical Sciences Research Council (EPSRC

Psilocybin-Assisted Therapy: A Double-Blind Randomized Controlled Trial

Alcohol use disorder is one of the most prevalent mental health disorders worldwide, treatment options are limited, and relapse rates are high. There remains an urgent need for effective treatments. Research involving the therapeutic potential of classic hallucinogens has reemerged in recent decades and the hallucinogen psilocybin has shown some promise in early studies. We aim to assess the efficacy of psilocybin in treating alcohol use disorder with a 10-week double blind randomized double dummy crossover control trial comparing psilocybin-assisted psychotherapy to ketamine-assisted psychotherapy in individuals with alcohol use disorder. Our primary outcome will be the mean percent heavy drinking days in the four-week periods following drug administration sessions among participants in both treatment groups. We hope that our results will provide further evidence for the therapeutic role of psilocybin in addiction and that our study design may serve as a model for future randomized controlled trials of psychedelics

Substance Use Initiation among Mexican Children: An Examination of Individual and Ecological Factors

Mexico is experiencing increased rates of substance use among children and adolescents. This is concerning as early substance use is associated with an increased risk for developing mental and physical health problems during adulthood. These outcomes may be prevented through early identification and intervention before individuals encounter the negative consequences of substance use/abuse. The current dissertation sought to improve our knowledge regarding factors associated with substance use and intention for first time use among Mexican children. Three manuscripts examined child individual characteristics and aspects of their environment. The first manuscript examined demographic characteristics to determine whether particular groups of children were at increased risk for substance use and intensions for first time use. We found that being a boy, of indigenous background, non-religious, and over developmental age for grade were all associated with risk. The second manuscript focuses on examining parent characteristics and practices on substance use and intention for first time use. We found that parental illicit substance use was associated with the largest increases in risk and positive parenting was a protective factor. The third manuscript utilized machine learning, an algorithmic approach that predicts membership in one of two groups, to assist in the identification of high value factors that distinguish between substance users and non-users. Findings from this research identified factors associated with childhood substance use at individual and environmental levels. Being a boy and having a best friend or father that used illicit substances were the key indicators that could provide valuable information as screening questions. These findings provide valuable information needed to inform the development of early substance use prevention programs in Mexico. Results also suggest that machine learning may be an important tool in uncovering information that could bolster prevention efforts by improving our ability to identify children at risk for substance use. This research was supported by the Utah State University Psychology Department and School of Graduate Studies

Retrying Leopold and Loeb: A Neuropsychological Perspective

They called it the crime of the century; in 1924 in Chicago two brilliant, well-educated, and wealthy young men kidnapped and murdered a 14-year-old boy and killed him for the thrill of it . Expert testimony was presented by several well-known psychiatrists and psychologists, but even with all their clinical insights, none could reach a conclusion about the causal relation between their disturbed childhoods and a violent senseless crime. In fact, the well-known criminal defense attorney Clarence Darrow made little mention of the extensive psychiatric and psychological workups, and the judge did not deal with it in his sentencing. A review of the findings does suggest a delusional disorder for one of the defendants and psychopathy for the other; the interaction of these two disordered personalities led to a perfect storm a confluence of factors that only in combination could result in the brutal crime. Recent developments in neuropsychology allow us to see how these two disordered personalities interacted; the neuropsychological basis of delusional disorder and of psychopathy will be explored in this presentation along with a re-imagined closing argument by their attorney

Exploring perspectives of people with type-1 diabetes on goalsetting strategies within self-management education and care

Background. Collaborative goal-setting strategies are widely recommended for diabetes self-management support within healthcare systems. Creating self-management plans that fit with peoples’ own goals and priorities has been linked with better diabetic control. Consequently, goal-setting has become a core component of many diabetes selfmanagement programmes such as the ‘Dose Adjustment for Normal Eating (DAFNE) programme’. Within DAFNE, people with Type-1 Diabetes (T1D) develop their own goals along with action-plans to stimulate goal-achievement. While widely implemented, limited research has explored how goal-setting strategies are experienced by people with diabetes.Therefore, this study aims to explore the perspectives of people with T1D on theimplementation and value of goal-setting strategies within DAFNE and follow-up diabetes care. Furthermore, views on barriers and facilitators to goal-attainment are explored.Methods. Semi-structured interviews were conducted with 20 people with T1D who attended a DAFNE-programme. Following a longitudinal qualitative research design, interviews took place 1 week, and 6-8 months after completion of DAFNE. A recurrent cross-sectional approach is applied in which themes will be identified at each time-point using thematic analyses.Expected results. Preliminary identified themes surround the difference in value that participants place on goal-setting strategies, and the lack of support for goal-achievement within diabetes care.Current stage. Data collection complete; data-analysis ongoing.Discussion. Goal-setting strategies are increasingly included in guidelines for diabetes support and have become essential parts of many primary care improvement schemes. Therefore, exploring the perspectives of people with T1D on the value and implementation of goal-setting strategies is vital for their optimal application