Identification of Myths and Misinformation About Treatment for Opioid Use Disorder on Social Media: Infodemiology Study.

BACKGROUND: Health misinformation and myths about treatment for opioid use disorder (OUD) are present on social media and contribute to challenges in preventing drug overdose deaths. However, no systematic, quantitative methodology exists to identify what types of misinformation are being shared and discussed. OBJECTIVE: We developed a multistage analytic pipeline to assess social media posts from Twitter (subsequently rebranded as X), YouTube, Reddit, and Drugs-Forum for the presence of health misinformation about treatment for OUD. METHODS: Our approach first used document embeddings to identify potential new statements of misinformation from known myths. These statements were grouped into themes using hierarchical agglomerative clustering, and public health experts then reviewed the results for misinformation. RESULTS: We collected a total of 19,953,599 posts discussing opioid-related content across the aforementioned platforms. Our multistage analytic pipeline identified 7 main clusters or discussion themes. Among a high-yield data set of posts (n=303) for further public health expert review, these included discussion about potential treatments for OUD (90/303, 29.8%), the nature of addiction (68/303, 22.5%), pharmacologic properties of substances (52/303, 16.9%), injection drug use (36/303, 11.9%), pain and opioids (28/303, 9.3%), physical dependence of medications (22/303, 7.2%), and tramadol use (7/303, 2.3%). A public health expert review of the content within each cluster identified the presence of misinformation and myths beyond those used as seed myths to initialize the algorithm. CONCLUSIONS: Identifying and addressing misinformation through appropriate communication strategies could be an increasingly important component of preventing overdose deaths. To further this goal, we developed and tested an approach to aid in the identification of myths and misinformation about OUD from large-scale social media content.

Chitosan/alginate/pectin biopolymer-based Nanoemulsions for improving the shelf life of refrigerated chicken breast.

This study investigated the use of nanoemulsions and various polymer coatings to enhance the quality and shelf life of chicken breast. This comprehensive study explored the antibacterial activity of essential oils (EOs) against Escherichia coli and Staphylococcus aureus, as well as the characterization of nanoemulsions (Nes) and nanoemulsion-based coatings. The antimicrobial potential of EOs, such as cinnamon, tea tree, jojoba, thyme, and black cumin seed oil, was evaluated against microorganisms, and thyme oil exhibited the highest inhibitory effect, followed by cinnamon and tea tree oil by disk diffusion analysis. The MIC and MBC values of EOs were found between 0.16-2.5 mg/mL and 0.16-5 mg/mL, respectively, while thyme EO resulted in the lowest values showing its antimicrobial potential. Then, the essential oil nanoemulsions (EONe) and their coatings, formulated with thyme oil, alginate, chitosan, and pectin, were successfully characterized. Optical microscope observations confirmed the uniform distribution of droplets in all (EONe), while particle size analysis demonstrated multimodal droplet size distributions. The EONe-chitosan coating showed the highest efficacy in reducing cooking loss, while the EONe-chitosan, EONe-alginate, and EONe-pectin coatings displayed promising outcomes in preserving color stability. Microbial analysis revealed the significant inhibitory effects of the EONe-chitosan coating against mesophilic bacteria, psychrophilic bacteria, and yeasts, leading to an extended shelf life of chicken breast. These results suggest the potential application of thyme oil and NE-based coatings in various industries for antimicrobial activity and quality preservation.

Characterizing and Identifying the Prevalence of Web-Based Misinformation Relating to Medication for Opioid Use Disorder: Machine Learning Approach.

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.