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

10.2196/30753 ◽  
2021 ◽  
Vol 23 (12) ◽  
pp. e30753
Author(s):  
Mai ElSherief ◽  
Steven A Sumner ◽  
Christopher M Jones ◽  
Royal K Law ◽  
Akadia Kacha-Ochana ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Large Scale ◽  
Addiction Treatment ◽  
Opioid Use Disorder ◽  
Supervised Machine Learning ◽  
Computational Techniques ◽  
Opioid Use ◽  
Web Based ◽  
Health Communities

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.

scholarly journals Characterizing and Identifying the Prevalence of Web-Based Misinformation Relating to Medication for Opioid Use Disorder: Machine Learning Approach (Preprint)

2021 ◽  
Author(s):  
Mai ElSherief ◽  
Steven A Sumner ◽  
Christopher M Jones ◽  
Royal K Law ◽  
Akadia Kacha-Ochana ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Large Scale ◽  
Addiction Treatment ◽  
Opioid Use Disorder ◽  
Supervised Machine Learning ◽  
Computational Techniques ◽  
Opioid Use ◽  
Web Based ◽  
Health Communities

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.

scholarly journals Big data and democratic speech: Predicting deliberative quality using machine learning techniques

2021 ◽  
Vol 14 (2) ◽  
pp. 205979912110104
Author(s):  
Eleonore Fournier-Tombs ◽  
Michael K. MacKenzie
Keyword(s):  
Machine Learning ◽  
Speech Acts ◽  
Large Scale ◽  
Large Datasets ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Computational Techniques ◽  
Learning Techniques ◽  
Campaign Speeches ◽  
Northern Territories

This article explores techniques for using supervised machine learning to study discourse quality in large datasets. We explain and illustrate the computational techniques that we have developed to facilitate a large-scale study of deliberative quality in Canada’s three northern territories: Yukon, Northwest Territories, and Nunavut. This larger study involves conducting comparative analyses of hundreds of thousands of parliamentary speech acts since the creation of Nunavut 20 years ago. Without computational techniques, we would be unable to conduct such an ambitious and comprehensive analysis of deliberative quality. The purpose of this article is to demonstrate the machine learning techniques that we have developed with the hope that they might be used and improved by other communications scholars who are interested in conducting textual analyses using large datasets. Other possible applications of these techniques might include analyses of campaign speeches, party platforms, legislation, judicial rulings, online comments, newspaper articles, and television or radio commentaries.

scholarly journals Text Polarity Detection using Multiple Supervised Machine Learning Algorithms

2020 ◽  
Vol 9 (3) ◽  
pp. 1612-1618
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Large Scale ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
The Public ◽  
Social Media Platforms ◽  
Day By Day

Sentiment analysis is the classifying of a review, opinion or a statement into categories, which brings clarity about specific sentiments of customers or the concerned group to businesses and developers. These categorized data are very critical to the development of businesses and understanding the public opinion. The need for accurate opinion and large-scale sentiment analysis on social media platforms is growing day by day. In this paper, a number of machine learning algorithms are trained and applied on twitter datasets and their respective accuracies are determined separately on different polarities of data, thereby giving a glimpse to which algorithm works best and which works worst..

Exploring fake news identification using word and sentence embeddings

2021 ◽  
pp. 1-8
Author(s):  
V.T Priyanga ◽  
J.P Sanjanasri ◽  
Vijay Krishna Menon ◽  
E.A Gopalakrishnan ◽  
K.P Soman
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Network Analysis ◽  
Supervised Machine Learning ◽  
Breeding Ground ◽  
Fake News ◽  
Data Set ◽  
Highly Correlated ◽  
Use Of Social Media ◽  
The Liar

The widespread use of social media like Facebook, Twitter, Whatsapp, etc. has changed the way News is created and published; accessing news has become easy and inexpensive. However, the scale of usage and inability to moderate the content has made social media, a breeding ground for the circulation of fake news. Fake news is deliberately created either to increase the readership or disrupt the order in the society for political and commercial benefits. It is of paramount importance to identify and filter out fake news especially in democratic societies. Most existing methods for detecting fake news involve traditional supervised machine learning which has been quite ineffective. In this paper, we are analyzing word embedding features that can tell apart fake news from true news. We use the LIAR and ISOT data set. We churn out highly correlated news data from the entire data set by using cosine similarity and other such metrices, in order to distinguish their domains based on central topics. We then employ auto-encoders to detect and differentiate between true and fake news while also exploring their separability through network analysis.

Predicting Opioid Overdose Readmission and Opioid Use Disorder with Machine Learning

2020 ◽  
Author(s):  
Sarah McDougall ◽  
Priyanka Annapureddy ◽  
Praveen Madiraju ◽  
Nicole Fumo ◽  
Stephen Hargarten
Keyword(s):  
Machine Learning ◽  
Opioid Use Disorder ◽  
Opioid Overdose ◽  
Opioid Use

scholarly journals The association between cannabis use and outcome in pharmacological treatment for opioid use disorder

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Tea Rosic ◽  
Raveena Kapoor ◽  
Balpreet Panesar ◽  
Leen Naji ◽  
Darren B. Chai ◽  
...  
Keyword(s):  
Side Effects ◽  
Pharmacological Treatment ◽  
Addiction Treatment ◽  
Age Of Onset ◽  
Opioid Use Disorder ◽  
Cannabis Use ◽  
Self Report ◽  
Opioid Use ◽  
Differential Outcomes ◽  
Urine Drug

Abstract Background With the ongoing opioid crisis and policy changes regarding legalization of cannabis occurring around the world, it is necessary to consider cannabis use in the context of opioid use disorder (OUD) and its treatment. We aimed to examine (1) past-month cannabis use in patients with OUD, (2) self-reported cannabis-related side effects and craving, and (3) the association between specific characteristics of cannabis use and opioid use during treatment in cannabis users. Methods Participants receiving pharmacological treatment for OUD (n = 2315) were recruited from community-based addiction treatment clinics in Ontario, Canada, and provided information on past-month cannabis use (self-report). Participants were followed for 3 months with routine urine drug screens in order to assess opioid use during treatment. We used logistic regression analysis to explore (1) the association between any cannabis use and opioid use during treatment, and (2) amongst cannabis-users, specific cannabis use characteristics associated with opioid use. Qualitative methods were used to examine responses to the question: “What effect does marijuana have on your treatment?”. Results Past-month cannabis use was reported by 51% of participants (n = 1178). Any cannabis use compared to non-use was not associated with opioid use (OR = 1.03, 95% CI 0.87–1.23, p = 0.703). Amongst cannabis users, nearly 70% reported daily use, and half reported experiencing cannabis-related side effects, with the most common side effects being slower thought process (26.2%) and lack of motivation (17.3%). For cannabis users, daily cannabis use was associated with lower odds of opioid use, when compared  with occasional use (OR = 0.61, 95% CI 0.47–0.79, p < 0.001) as was older age of onset of cannabis use (OR = 0.97, 95% CI 0.94, 0.99, p = 0.032), and reporting cannabis-related side effects (OR = 0.67, 95% CI 0.51, 0.85, p = 0.001). Altogether, 75% of cannabis users perceived no impact of cannabis on their OUD treatment. Conclusion Past-month cannabis use was not associated with more or less opioid use during treatment. For patients who use cannabis, we identified specific characteristics of cannabis use associated with differential outcomes. Further examination of characteristics and patterns of cannabis use is warranted and may inform more tailored assessments and treatment recommendations.

Neurofeedback for Opioid Dependent Patients in an Outpatient Setting: A Pilot Feasibility Study

2021 ◽  
Author(s):  
Helena A. Rempala ◽  
Justin A. Barterian
Keyword(s):  
Large Scale ◽  
The United States ◽  
Outpatient Setting ◽  
Opioid Use Disorder ◽  
High Rate ◽  
Treatment Center ◽  
Opioid Use ◽  
Treatment As Usual ◽  
Factors Affecting ◽  
Study Approach

Abstract Background: Neurofeedback (NF) has been described as “probably efficacious” when used in conjunction with other interventions for substance use disorders, including the most recent studies in population of individuals with opioid use disorder. Despite these promising outcomes, the seriousness of the opioid epidemic, and the high rate of relapse even with the most effective medication-assisted maintenance treatments NF continues to be an under-researched treatment modality. This article explores factors that affected the feasibility of adding Alpha/Theta Neurofeedback to treatment as usual for opioid dependence in an outpatient urban treatment center. The study strived to replicate previous research completed in Iran that found benefits of NF for opioid dependence.Methods: Out of approximately two dozen patients eligible for Alpha/Theta NF, about 60% (n=15) agreed to participate; however, only 2 participants completed treatment. The rates of enrollment in response to active treatment were monitored. Results: The 4 factors affecting feasibility were: 1) the time commitment required of participants, 2) ineffectiveness of standard incentives to promote participation, 3) delayed effects of training, and 4) the length and number of treatments required.Conclusion: The findings indicate a large scale study examining the use of NF for the treatment of opioid use disorder in the United States will likely be difficult to accomplish without modification to the traditional randomized control study approach and suggests challenges to the implementation of this treatment in an outpatient setting.

scholarly journals Demystifying the Opioid Trade in Anonymous Markets and Forums (Preprint)

2020 ◽  
Author(s):  
Zhengyi Li ◽  
Xiangyu Du ◽  
Xiaojing Liao ◽  
Xiaoqian Jiang ◽  
Tiffany Champagne-Langabeer
Keyword(s):  
Public Health ◽  
Supply Chain ◽  
Large Scale ◽  
Supply And Demand ◽  
Opioid Use Disorder ◽  
Public Health Issue ◽  
Opioid Use ◽  
Longitudinal Measurement ◽  
Online Marketplaces ◽  
Trading Activities

BACKGROUND Opioid use disorder presents a public health issue afflicting millions across the globe. There is a pressing need to understand the opioid supply chain to gain new insights into the mitigation of opioid use and effectively combat the opioid crisis. The role of anonymous online marketplaces and forums that resemble eBay or Amazon, where anyone can post, browse, and purchase opioid commodities, has become more and more important in opioid trading. Therefore, a greater understanding of anonymous markets and forums may enable public health officials and other stakeholders to comprehend the scope of the crisis. OBJECTIVE The objective of this work is to profile the opioid supply chain in anonymous markets and forums via a large-scale, longitudinal measurement study on anonymous market listings and posts. Toward this, we propose a series of techniques to collect data, to identify opioid jargon terms used in the anonymous marketplaces and forums, and to profile the opioid commodities, suppliers, and transactions. METHODS We first conducted a whole-site crawl of anonymous online marketplaces and forums to solicit data. Then, we developed a suite of opioid domain-specific text mining techniques (e.g., opioid jargon detection, opioid trading information retrieval) to recognize information relevant to opioid trading activities (e.g., commodities, price, shipping information, suppliers, etc.). After that, we conducted a comprehensive, large-scale, longitudinal study to demystify opioid trading activities in anonymous markets and forums. RESULTS A total of 248,359 listings from 10 anonymous online marketplaces and 1,138,961 traces (i.e., threads of posts) from 6 underground forums were collected. Among them, we identified 28,106 opioid product listings and 13,508 opioid-related promotional and review forum traces from 5147 unique opioid suppliers’ IDs and 2778 unique opioid buyers’ IDs. Our study characterized opioid suppliers (e.g., activeness and cross-market activities), commodities (e.g., popular items and their evolution), and transactions (e.g., origins and shipping destination) in anonymous marketplaces and forums, which enabled a greater understanding of the underground trading activities involved in international opioid supply and demand. CONCLUSIONS The results provide insight into opioid trading in the anonymous markets and forums, and may prove an effective mitigation data point for illuminating the opioid supply chain.

scholarly journals Self-Supervised Machine Learning Approach for Identifying Biochemical Influences on Protein-Ligand Binding Affinity

2021 ◽  
Author(s):  
Arjun Singh
Keyword(s):  
Machine Learning ◽  
Binding Affinity ◽  
3D Models ◽  
Target Protein ◽  
Supervised Machine Learning ◽  
Significant Feature ◽  
Computational Techniques ◽  
Lasso Regression ◽  
Generative Adversarial Network ◽  
Adversarial Network

Abstract Drug discovery is incredibly time-consuming and expensive, averaging over 10 years and $985 million per drug. Calculating the binding affinity between a target protein and a ligand is critical for discovering viable drugs. Although supervised machine learning (ML) models can predict binding affinity accurately, they suffer from lack of interpretability and inaccurate feature selection caused by multicollinear data. This study used self-supervised ML to reveal underlying protein-ligand characteristics that strongly influence binding affinity. Protein-ligand 3D models were collected from the PDBBind database and vectorized into 2422 features per complex. LASSO Regression and hierarchical clustering were utilized to minimize multicollinearity between features. Correlation analyses and Autoencoder-based latent space representations were generated to identify features significantly influencing binding affinity. A Generative Adversarial Network was used to simulate ligands with certain counts of a significant feature, and thereby determine the effect of a feature on improving binding affinity with a given target protein. It was found that the CC and CCCN fragment counts in the ligand notably influence binding affinity. Re-pairing proteins with simulated ligands that had higher CC and CCCN fragment counts could increase binding affinity by 34.99-37.62% and 36.83%-36.94%, respectively. This discovery contributes to a more accurate representation of ligand chemistry that can increase the accuracy, explainability, and generalizability of ML models so that they can more reliably identify novel drug candidates. Directions for future work include integrating knowledge on ligand fragments into supervised ML models, examining the effect of CC and CCCN fragments on fragment-based drug design, and employing computational techniques to elucidate the chemical activity of these fragments.

Sign in / Sign up

Export Citation Format

Share Document