Social Media Mining for Toxicovigilance: Automatic Monitoring of Prescription Medication Abuse from Twitter

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

An Examination of Neurodevelopmental Outcomes, Healthcare Utilization, and Stigmatizing Language in Populations With Neonatal Abstinence Syndrome (NAS)

Substance use disorder (SUD) during pregnancy, which includes opioid use disorder (OUD), has developed into a significant medical and social concern, as it can cause a range of complications for pregnant women, fetuses, and infants. One common condition resulting from OUD is neonatal abstinence syndrome (NAS), a withdrawal syndrome experienced by infants after being exposed to opioids in the womb. NAS can cause visual physiological or neurodevelopmental complications or outcomes in newborns. Unfortunately, large-scale studies focusing on long-term neurodevelopmental outcomes of infants with NAS are minimal. NAS consists of indications and symptoms that can also affect the autonomic nervous, gastrointestinal, and respiratory systems, often requiring extended hospitalization and extensive pharmacological treatment. Despite the increase in the number of children suffering from NAS and the healthcare utilization consumed by their treatment, little is known about these children\u27 outcomes and diagnoses behind the utilizations after their initial hospitalization. Additionally, pregnant women with SUD and their children are often stigmatized, mainly through the perpetuation of stigmatizing words and inaccurate beliefs. Unfortunately, extensive use of stigmatizing language exists on social media platforms, with Twitter containing a substantive portion of the posts. This dissertation consists of three manuscripts that provide a comprehensive understanding of neurodevelopmental outcome, healthcare utilization, and stigmatizing language around NAS. The first manuscript compares neurodevelopmental diagnosis and screening of children treated with a NAS innovation program and children treated with traditional NAS care in South Carolina from birth to 4.5 years of age. It applies Kaplan-Meier survival curves to demonstrate and compare the survival (outcome), and Cox Proportional Hazard (PH) survival analysis models to identify how often neurodevelopmental screenings and diagnosis occur for children with NAS treated with and without the innovation program. The second manuscript explores two healthcare utilization outcomes, hospital readmission and hospital length of stay (LOSD), among children with NAS and children born late preterm in South Carolina, with follow-up years from 0–3 years of age. The risk of hospital readmission was examined using logistic regression, and unadjusted and adjusted negative binomial regression analyses were used to model the relationship between hospital LOSD for children with NAS and those born late preterm. Finally, the third manuscript examines social media data around OUD and NAS to expand understanding of the general population\u27s views and the potential unintended impacts of this communication environment on mothers and infants. This study consists of an event analysis of Twitter data, generated by a social media listening platform Sprinklr, to describe the use of stigmatizing language around OUD and NAS. The event was divided into three timeframes and the tests of significance were performed across all three timeframes using chi-square tests. In conclusion, this dissertation synthesized and discussed the results from the three studies. It also provided a broad discussion on the potential policy implications for clinical practice and possible directions for future research. For instance, increase insurance coverage through Medicaid and the state children’s health insurance programs, and the need for reaching a consensus on a specially established “addiction-ary,” particularly for NAS-related language

Cannabidiol tweet miner: a framework for identifying misinformation In CBD tweets.

As regulations surrounding cannabis continue to develop, the demand for cannabis-based products is on the rise. Despite not producing the psychoactive effects commonly associated with THC, products containing cannabidiol (CBD) have gained immense popularity in recent years as a potential treatment option for a range of conditions, particularly those associated with pain or sleep disorders. However, due to current federal policies, these products have yet to undergo comprehensive safety and efficacy testing. Fortunately, utilizing advanced natural language processing (NLP) techniques, data harvested from social networks have been employed to investigate various social trends within healthcare, such as disease tracking and drug surveillance. By leveraging Twitter data, NLP can offer invaluable insights into public perceptions around CBD, as well as the marketing tactics employed by those marketing such loosely-regulated substances to the general public. Given the lack of comprehensive clinical CBD testing, the various health claims made by CBD sellers regarding their products are highly dubious and potentially perilous, as is evident from the ongoing COVID-19 misinformation. It is therefore critically important to efficiently identify unsupportable claims to guide public health policy and action. To this end, we present our proposed framework, the Cannabidiol Tweet Miner (CBD-TM), which utilizes advanced natural language processing (NLP) techniques, including text mining and sentiment analysis, to analyze the similarities and differences between commercial and personal tweets that mention CBD. CBD-TM enables us to identify conditions typically associated with commercial CBD advertising, or conditions not associated with positive sentiment, that are also absent from personal conversations. Through our technical contributions, including NLP, text mining, and sentiment analysis, we can effectively uncover areas where the public may be misled by CBD sellers. Since the rise in popularity of CBD, advertisements making bold claims about its benefits have become increasingly prevalent. The COVID-19 pandemic created a new opportunity for sellers to promote and sell products that purportedly treat and/or prevent the virus, with CBD being one of them. Although the U.S. Food and Drug Administration issued multiple warnings to CBD sellers, this type of misinformation still persists. In response, we have extended the CBD-TM framework with an additional layer of tweet classification designed to identify tweets that make potentially misleading claims about CBD\u27s efficacy in treating and/or preventing COVID-19. Our approach harnesses modern NLP algorithms, utilizing a transformer-based language model to establish the semantic relationship between statements extracted from the FDA\u27s website that contain false information and tweets conveying similar false claims. Our technical contributions build upon the impressive performance of deep language models in various natural language processing and understanding tasks. Specifically, we employ transfer learning via pre-trained deep language models, enabling us to achieve improved misinformation identification in tweets, even with relatively small training sets. Furthermore, this extension of CBD-TM can be easily adapted to detect other forms of misinformation. Through our innovative use of NLP techniques and algorithms, we can more effectively identify and combat false and potentially harmful claims related to CBD and COVID-19, as well as other forms of misinformation. As the conversations surrounding CBD on Twitter evolve over time, concept drift can occur, leading to changes in the topics being discussed. We observed significant changes within the CBD Twitter data stream with the emergence of COVID-19, introducing a new medical condition associated with CBD that would not have been discussed in conversations prior to the pandemic. These shifts in conversation introduce concept drift into CBD-TM, which has the potential to negatively impact our tweet classification models. Therefore, it is crucial to identify when such concept drift occurs to maintain the accuracy of our models. To this end, we propose an innovative approach for identifying potential changes within social network streams, allowing us to determine how and when these conversations evolve over time. Our approach leverages a BERT-based topic model, which can effectively capture how conversations related to CBD change over time. By incorporating advanced NLP techniques and algorithms, we are able to better understand the changes in topic that occur within the CBD Twitter data stream, allowing us to more effectively manage concept drift in CBD-TM. Our technical contributions enable us to maintain the accuracy and effectiveness of our tweet classification models, ensuring that we can continue to identify and address potentially harmful misinformation related to CBD

Predictive Analysis on Twitter: Techniques and Applications

Predictive analysis of social media data has attracted considerable attention from the research community as well as the business world because of the essential and actionable information it can provide. Over the years, extensive experimentation and analysis for insights have been carried out using Twitter data in various domains such as healthcare, public health, politics, social sciences, and demographics. In this chapter, we discuss techniques, approaches and state-of-the-art applications of predictive analysis of Twitter data. Specifically, we present fine-grained analysis involving aspects such as sentiment, emotion, and the use of domain knowledge in the coarse-grained analysis of Twitter data for making decisions and taking actions, and relate a few success stories