Project description:BackgroundThe misuse of prescription opioids (MUPO) is a leading public health concern. Social media are playing an expanded role in public health research, but there are few methods for estimating established epidemiological metrics from social media. The purpose of this study was to demonstrate that the geographic variation of social media posts mentioning prescription opioid misuse strongly correlates with government estimates of MUPO in the last month.MethodsWe wrote software to acquire publicly available tweets from Twitter from 2012 to 2014 that contained at least one keyword related to prescription opioid use (n = 3,611,528). A medical toxicologist and emergency physician curated the list of keywords. We used the semantic distance (SemD) to automatically quantify the similarity of meaning between tweets and identify tweets that mentioned MUPO. We defined the SemD between two words as the shortest distance between the two corresponding word-centroids. Each word-centroid represented all recognized meanings of a word. We validated this automatic identification with manual curation. We used Twitter metadata to estimate the location of each tweet. We compared our estimated geographic distribution with the 2013-2015 National Surveys on Drug Usage and Health (NSDUH).ResultsTweets that mentioned MUPO formed a distinct cluster far away from semantically unrelated tweets. The state-by-state correlation between Twitter and NSDUH was highly significant across all NSDUH survey years. The correlation was strongest between Twitter and NSDUH data from those aged 18-25 (r = 0.94, p < 0.01 for 2012; r = 0.94, p < 0.01 for 2013; r = 0.71, p = 0.02 for 2014). The correlation was driven by discussions of opioid use, even after controlling for geographic variation in Twitter usage.ConclusionsMentions of MUPO on Twitter correlate strongly with state-by-state NSDUH estimates of MUPO. We have also demonstrated that a natural language processing can be used to analyze social media to provide insights for syndromic toxicosurveillance.

Project description:BackgroundPrescription 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.MethodsWe 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.ResultsOur 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 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.ConclusionsBERT, 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.

Project description:BackgroundHistorically, pharmaceutical companies have struggled with trust and brand reputation among key stakeholders and have adopted innovative marketing strategies to reach patients directly and rebuild those relationships. Social media influencers are a popular strategy to influence younger demographics, including Generation Z and millennials. It is common for social media influencers to work in paid partnerships with brands; this is a multibillion-dollar industry. Long have patients been active in online health communities and social media platforms such as Twitter and Instagram, but in recent years, pharmaceutical marketers have noticed the power of patient persuasion and begun to leverage "patient influencers" in brand campaigns.ObjectiveThis study aimed to explore how patient influencers communicate health literacy on pharmaceutical medications on social media to their communities of followers.MethodsA total of 26 in-depth interviews were conducted with patient influencers using a snowball sampling technique. This study is part of a larger project using an interview guide that included a range of topics such as social media practices, logistics of being an influencer, considerations for brand partnerships, and views on the ethical nature of patient influencers. The constructs of the Health Belief Model were used in this study's data analysis: perceived susceptibility, perceived severity, perceived benefits, perceived barriers, cues to action, and self-efficacy. This study was approved by the institutional review board of the University of Colorado and adhered to ethical standards in interview practice.ResultsAs patient influencers are a new phenomenon, it was our goal to identify how health literacy on prescription medications and pharmaceuticals is being communicated on social media. Using the constructs of the Health Belief Model to guide the analysis, 3 themes were identified: understanding disease through experience, staying informed on the science or field, and suggesting that physicians know best.ConclusionsPatients are actively exchanging health information on social media channels and connecting with other patients who share similar diagnoses. Patient influencers share their knowledge and experience in efforts to help other patients learn about disease self-management and improve their quality of life. Similar to traditional direct-to-consumer advertising, the phenomenon of patient influencers raises ethical questions that need more investigation. In a way, patient influencers are health education agents who may also share prescription medication or pharmaceutical information. They can break down complex health information based on expertise and experience and mitigate the loneliness and isolation that other patients may feel without the support of a community.

Project description:BackgroundSubstance use, including the non-medical use of prescription medications, is a global health problem resulting in hundreds of thousands of overdose deaths and other health problems. Social media has emerged as a potent source of information for studying substance use-related behaviours and their consequences. Mining large-scale social media data on the topic requires the development of natural language processing (NLP) and machine learning frameworks customized for this problem. Our objective in this research is to develop a framework for conducting a content analysis of Twitter chatter about the non-medical use of a set of prescription medications.MethodsWe collected Twitter data for four medications-fentanyl and morphine (opioids), alprazolam (benzodiazepine), and Adderall® (stimulant), and identified posts that indicated non-medical use using an automatic machine learning classifier. In our NLP framework, we applied supervised named entity recognition (NER) to identify other substances mentioned, symptoms, and adverse events. We applied unsupervised topic modelling to identify latent topics associated with the chatter for each medication.ResultsThe quantitative analysis demonstrated the performance of the proposed NER approach in identifying substance-related entities from data with a high degree of accuracy compared to the baseline methods. The performance evaluation of the topic modelling was also notable. The qualitative analysis revealed knowledge about the use, non-medical use, and side effects of these medications in individuals and communities.ConclusionsNLP-based analyses of Twitter chatter associated with prescription medications belonging to different categories provide multi-faceted insights about their use and consequences. Our developed framework can be applied to chatter about other substances. Further research can validate the predictive value of this information on the prevention, assessment, and management of these disorders.

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