Identification and Characterization of Illegal Sales of Cannabis and Nicotine Delivery Products on Telegram Messaging Platform.

INTRODUCTION: Unregulated and potentially illegal sales of tobacco, nicotine, and cannabis products have been detected on various social media platforms, e-commerce sites, online retailers, and the dark web. New end-to-end encrypted messaging services are popular among online users and present opportunities for marketing, trading, and selling of these products. The purpose of this study was to identify and characterize tobacco, nicotine, and cannabis selling activity on the messaging platform Telegram. METHODS: The study was conducted in three phases: (1) identifying keywords related to tobacco, nicotine, and cannabis products for purposes of detecting Telegram groups and channel messages; (2) automated data collection from public Telegram groups; and (3) manual annotation and classification of messages engaged in marketing and selling products to consumers. RESULTS: Four keywords were identified ("Nicotine," "Vape," "Cannabis," and "Smoke") that yielded 20 Telegram groups with 262 506 active subscribers. Total volume of channel messages was 43 963 unique messages that included 3094 (7.04%) marketing/selling messages. The most commonly sold products in these groups were cannabis-derived products (83.25%, n = 2576), followed by tobacco/nicotine-derived products (6.46%, n = 200), and other illicit drugs (0.77%, n = 24). A variety of marketing tactics and a mix of seller accounts were observed, though most appeared to be individual suppliers. CONCLUSIONS: Telegram is an online messaging application that allows for custom group creation and global connectivity, but also includes unregulated activities associated with the sale of cannabis and nicotine delivery products. Greater attention is needed to conduct monitoring and enforcement on these emerging platforms for unregulated and potentially illegal cannabis and nicotine product sales direct-to-consumer. IMPLICATIONS: Based on study results, Telegram represents an emerging platform that enables a robust cannabis and nicotine-selling marketplace. As local, state, and national tobacco control regulations continue to advance sales restrictions and bans at the retail level, easily accessible and unregulated Internet-based channels must be further assessed to ensure that they do not act as conduits for exposure and access to unregulated or illegal cannabis and nicotine products.

Identification and Characterization of Synthetic Nicotine Product Promotion and Sales on Instagram Using Natural Language Processing.

INTRODUCTION: There has been a rapid proliferation of synthetic nicotine products in recent years, despite newly established regulatory authority and limited research into its health risks. Previous research has implicated social media platforms as an avenue for nicotine product unregulated sales. Yet, little is known about synthetic nicotine product content on social media. We utilized natural language processing to characterize the sales of synthetic nicotine products on Instagram. METHODS: We collected Instagram posts by querying Instagram hashtags (e.g., "#tobaccofreenicotine) related to synthetic nicotine. Using BERT, collected posts were categorized into thematically related topic clusters. Posts within topic clusters relevant to study aims were then manually annotated for variables related to promotion and selling (e.g., cost discussion, contact information for offline sales). RESULTS: A total of 7,425 unique posts were collected with 2,219 posts identified as related to promotion and selling of synthetic nicotine products. Nicotine pouches (52.9%, n=1174), ENDS (30.6%, n=679), and flavored e-liquids (14.1%, n=313) were most commonly promoted. 16.1% (n=345) of posts contained embedded hyperlinks and 5.8% (n=129) provided contact information for purported offline transactions. Only 17.6% (n=391) of posts contained synthetic nicotine specific health warnings. CONCLUSIONS: In the United States, synthetic nicotine products can only be legally marketed if they have received premarket authorization from the FDA. Despite these prohibitions, Instagram appears to be a hub for potentially unregulated sales of synthetic and "tobacco-free" products. Efforts are needed by platforms and regulators to enhance content moderation and prevent unregulated online sales of existing and emerging synthetic nicotine products. IMPLICATIONS: There is limited clinical understanding of synthetic nicotine's unique health risks and how these novel products are changing over time due to regulatory oversight. Despite synthetic nicotine specific regulatory measures, such as the requirement for premarket authorization and FDA warning letters issued to unauthorized sellers, access to and promotion of synthetic nicotine is widely occurring on Instagram, a platform with over 2 billion users and one that is popular among youth and young adults. Activities include direct-to-consumer sales from questionable sources, inadequate health warning disclosure, and exposure with limited age restrictions, all conditions necessary for the sale of various tobacco products. Notably, the number of these Instagram posts increased in response to the announcement of new FDA regulations. In response, more robust online monitoring, content moderation, and proactive enforcement is needed from platforms who should work collaboratively with regulators to identify, report, and remove content in clear violation of platform policies and federal laws. Regulatory implementation and enforcement should prioritize digital platforms as conduits for unregulated access to synthetic nicotine products and other future novel and emerging tobacco products.

Assessing the Impact of the Massachusetts Temporary Flavor Ban on Licensed Tobacco Retailers.

Introduction: In 2019, the state of Massachusetts signed into law the first statewide sales restrictions of flavored ENDS/tobacco products for both physical and online shops in response to a previous executive order to curb E-Cigarette, or Vaping Product, Use Associated Lung Injury (EVALI) cases that were surging throughout the nation. Methodology: This study obtained licensure data from the Massachusetts Department of Revenue, to observe the changes in retail licensure comparing the pre ban (October 2018-August 2019) and post ban periods (October 2020- August 2021). A series of linear regression tests were conducted on both periods using census tract data to explore potential associations with sociodemographic covariates, including median age, median household income, and population proportion by gender, age, and race/ethnicity groups. Results: Analysis of the Massachusetts post-ban period (October 2020-August 2021) found that new tobacco retail licenses issued decreased by 52.9% (n = 968) when compared to the pre-ban period (October 2018-August 2019) of 1831. A significant positive association was discovered between change in new retailer count and proportion male population (2.48 ± 1.05, P = .018) as well as proportion Hispanic population (1.19 ± .25, P < .001) at the census tract level. Conclusion/Discussion: Our analysis indicates that, following the temporary MA flavor sales ban, the total number of licenses decreased, though decreases were more pronounced for new licenses when compared to continuing licenses. Higher increases in new tobacco retailer density were significantly associated with concentration of male and Hispanic populations.

Assessing Characteristics and Compliance of Online Delta-8 Tetrahydrocannabinol Product Sellers.

Introduction: The debate over the legal status of many cannabis- and hemp-derived products, including delta-8 tetrahydrocannabinol (THC), is in question. Although low concentrations of delta-8 THC are legal at the Federal level, many states have implemented their own regulations to both allow and restrict its use and sale. Of concern, sellers with unknown legal credentials have appeared online and are actively selling this product. Materials and Methods: We characterized the marketing, sale, and compliance of online delta-8 THC sellers using (1) data collected from the Twitter Application Programming Interface with delta-8 THC-related keywords; (2) unsupervised topic modeling using the Biterm Topic Model to identify clusters of tweets involved in marketing and selling; (3) inductive coding to identify marketing and selling characteristics; and (4) web forensics and simulated shopping to determine compliance with state restrictions for delta-8 THC sales. Results: In total, 110 unique hyperlinks associated with 7085 tweets that included marketing and selling activity for delta-8 THC were collected. From these links, we conducted simulated purchasing in January 2021 to identify compliant and noncompliant websites. Among the vendors, age verification was not found in over half of websites (59, 53.63%); 60 (54.55%) did not report a physical address; and 74 (65.45%) sold delta-8 products direct-to-consumer. Sixty-seven (90.54%) of detected vendors shipped delta-8 products to addresses in states that prohibit sales. Forty-three (64.18%) of Internet Protocol addresses were located within the United States; all others were international. Conclusion: Our analysis suggests that online storefronts are illegally selling and shipping cannabinoid derivatives to U.S. consumers. Further research is needed to understand downstream health and regulatory impacts from this unregulated access.

Applying topic modelling and qualitative content analysis to identify and characterise ENDS product promotion and sales on Instagram.

BACKGROUND: Increased public health and regulatory scrutiny concerning the youth vaping epidemic has led to greater attention to promotion and sales of vaping products on social media platforms. OBJECTIVES: We used unsupervised machine learning to identify and characterise sale offers of electronic nicotine delivery systems (ENDS) and associated products on Instagram. We examined types of sellers, geographic ENDS location and use of age verification. METHODS: Our methodology was composed of three phases: data collection, topic modelling and content analysis. We used data mining approaches to query hashtags related to ENDS product use among young adults to collect Instagram posts. For topic modelling, we applied an unsupervised machine learning approach to thematically categorise and identify topic clusters associated with selling activity. Content analysis was then used to characterise offers for sale of ENDS products. RESULTS: From 70 725 posts, we identified 3331 engaged in sale of ENDS products. Posts originated from 20 different countries and were roughly split between individual (46.3%) and retail sellers (43.4%), with linked online sellers (8.8%) representing a smaller volume. ENDS products most frequently offered for sale were flavoured e-liquids (53.0%) and vaping devices (20.5%). Online sellers offering flavoured e-liquids were less likely to use age verification at point of purchase (29% vs 64%) compared with other products. CONCLUSIONS: Instagram is a global venue for unregulated ENDS sales, including flavoured products, and access to websites lacking age verification. Such posts may violate Instagram's policies and US federal and state law, necessitating more robust review and enforcement to prevent ENDS uptake and access.

Impact of COVID-19 on HIV Prevention Access: A Multi-platform Social Media Infodemiology Study.

This study seeks to identify and characterize key barriers associated with PrEP therapy as self-reported by users on social media platforms. We used data mining and unsupervised machine learning approaches to collect and analyze COVID-19 and PrEP-related posts from three social media platforms including Twitter, Reddit, and Instagram. Predominant themes detected by unsupervised machine learning and manual annotation included users expressing uncertainty about PrEP treatment adherence due to COVID-19, challenges related to accessibility of clinics, concerns about PrEP costs and insurance coverage, perceived lower HIV risk leading to lack of adherence, and misinformation about PrEP use for COVID-19 prevention.

Predictors of Time to Coronavirus Disease 2019 Infection in the National Basketball Association: A Retrospective Case-Control Study.

A case-control study was conducted between 1 December 2021 and 31 January 2022 to identify factors, which increase risk for coronavirus disease 2019 (COVID-19) among athletes in the National Basketball Association (NBA). Behavioral factors and stadium attendance significantly decreased time to COVID-19 infection, but local COVID-19 rates were not associated in a multivariable model.

Content Analysis of Nicotine Poisoning (Nic Sick) Videos on TikTok: Retrospective Observational Infodemiology Study.

BACKGROUND: TikTok is a microvideo social media platform currently experiencing rapid growth and with 60% of its monthly users between the ages of 16 and 24 years. Increased exposure to e-cigarette content on social media may influence patterns of use, including the risk of overconsumption and possible nicotine poisoning, when users engage in trending challenges online. However, there is limited research assessing the characteristics of nicotine poisoning-related content posted on social media. OBJECTIVE: We aimed to assess the characteristics of content on TikTok that is associated with a popular nicotine poisoning-related hashtag. METHODS: We collected TikTok posts associated with the hashtag #nicsick, using a Python programming package (Selenium) and used an inductive coding approach to analyze video content and characteristics of interest. Videos were manually annotated to generate a codebook of the nicotine sickness-related themes. Statistical analysis was used to compare user engagement characteristics and video length in content with and without active nicotine sickness TikTok topics. RESULTS: A total of 132 TikTok videos associated with the hashtag #nicsick were manually coded, with 52.3% (69/132) identified as discussing firsthand and secondhand reports of suspected nicotine poisoning symptoms and experiences. More than one-third of nicotine poisoning-related content (26/69, 37.68%) portrayed active vaping by users, which included content with vaping behavior such as vaping tricks and overconsumption, and 43% (30/69) of recorded users self-reported experiencing nicotine sickness, poisoning, or adverse events such as vomiting following nicotine consumption. The average follower count of users posting content related to nicotine sickness was significantly higher than that for users posting content unrelated to nicotine sickness (W=2350.5, P=.03). CONCLUSIONS: TikTok users openly discuss experiences, both firsthand and secondhand, with nicotine adverse events via the #nicsick hashtag including reports of overconsumption resulting in sickness. These study results suggest that there is a need to assess the utility of digital surveillance on emerging social media platforms for vaping adverse events, particularly on sites popular among youth and young adults. As vaping product use-patterns continue to evolve, digital adverse event detection likely represents an important tool to supplement traditional methods of public health surveillance (such as poison control center prevalence numbers).

Characterizing California licensure status and tobacco user experience with adverse events using Yelp data.

Various tobacco vendors, including alternative tobacco product sellers, are listed on the popular crowdsourced business listing platform Yelp. Yelp is used to rate and choose tobacco, electronic nicotine delivery systems (ENDS) goods/services and includes self-reporting of user experiences with shops and products. We cross-referenced California Department of Tax and Fee Administration (CDTFA) licensed tobacco, vape, and head shop retail stores with publicly available Yelp business listings to identify licensed and unlicensed stores in California. We extracted metadata associated with store accounts and analyzed user comments and ratings for discussion of tobacco-related complaints and adverse events. We detected a total of 3,717 shops that were categorized as tobacco/vape/head shops on Yelp and by cross-referencing with CDTFA data, licensed businesses accounted for 49.5% (n = 1,841), licensed individual retailers 31.6% (n = 1,174), and suspected unlicensed storefronts 18.9% (n = 702). Businesses and individuals with a state tobacco retail license received a higher average rating from Yelp users (3.86 out of 5) compared to unlicensed shops (3.57) (p < 0.001). Additionally, 4,682 unique comments about licensed businesses, 1,535 unique comments about individual retailers, and 560 unique comments about unlicensed vendors were reviewed, with themes including discussion about defective and counterfeit products and adverse events including coughing, difficulty breathing and reports of hospitalization detected. In contrast, comments about licensed stores predominantly discussed customer service issues. Close to one-fifth of tobacco, vape and/or head shops reviewed on Yelp were not in CDTFA's licensure database. Overall self-reported tobacco user experiences appeared to differ in content and severity based on whether an establishment was licensed. These results have the potential to identify unauthorized stores and adverse events associated with their tobacco and vaping products or services.

Association of tobacco retailer count with smoking population versus vaping population in California (2019).

BACKGROUND: Access to tobacco products, including vape products, from local brick-and-mortar stores influences the exposure, uptake, and use of these products in local communities. METHODS: Licensed tobacco retailers in California were classified as specialized tobacco/vape stores or non-specialized stores by obtaining categories published on Yelp. California smoking and vaping prevalence data were obtained from the 500 cities project and ESRI community analyst tool respectively. A series of simple linear regression tests were performed, at the zip code level, between the retailer count in each store category and smoking/vaping population. The Getis-Ord Gi* and Anselin Local Moran's I statistics were used for characterization of tobacco retail density hotspots and cold spots. RESULTS: The association between CA smoking/vaping population and number of tobacco retailers was statistically significant for all store categories. Variability in smoking population was best explained by variability in non-specialized storefronts(R2=0.84). Spatial variability in tobacco-only storefronts explained the least proportion of variability in the overall smoking population. Similar results were obtained specific to vaping population, although the proportion of population explained by variability in the number of non-specialized storefronts was comparatively lower(R2=0.80). CONCLUSIONS: Localities with greater numbers of non-specialized tobacco retailers had higher rates of smoking/vaping populations, and this association was much stronger for localities with greater numbers of specialized retailers. Non-specialized storefronts may represent convenient access points for nicotine products, while specialized storefronts may represent critical access points for initiation. Hence, regulations that address the entirety of the tobacco/vaping retail environment by limiting widespread access from non-specialized stores and reducing appeal generated by specialized retailers should be incorporated in future tobacco regulatory science and policymaking.

Unsupervised Machine Learning to Detect and Characterize Barriers to Pre-exposure Prophylaxis Therapy: Multiplatform Social Media Study.

Background: Among racial and ethnic minority groups, the risk of HIV infection is an ongoing public health challenge. Pre-exposure prophylaxis (PrEP) is highly effective for preventing HIV when taken as prescribed. However, there is a need to understand the experiences, attitudes, and barriers of PrEP for racial and ethnic minority populations and sexual minority groups. Objective: This infodemiology study aimed to leverage big data and unsupervised machine learning to identify, characterize, and elucidate experiences and attitudes regarding perceived barriers associated with the uptake and adherence to PrEP therapy. This study also specifically examined shared experiences from racial or ethnic populations and sexual minority groups. Methods: The study used data mining approaches to collect posts from popular social media platforms such as Twitter, YouTube, Tumblr, Instagram, and Reddit. Posts were selected by filtering for keywords associated with PrEP, HIV, and approved PrEP therapies. We analyzed data using unsupervised machine learning, followed by manual annotation using a deductive coding approach to characterize PrEP and other HIV prevention-related themes discussed by users. Results: We collected 522,430 posts over a 60-day period, including 408,637 (78.22%) tweets, 13,768 (2.63%) YouTube comments, 8728 (1.67%) Tumblr posts, 88,177 (16.88%) Instagram posts, and 3120 (0.6%) Reddit posts. After applying unsupervised machine learning and content analysis, 785 posts were identified that specifically related to barriers to PrEP, and they were grouped into three major thematic domains: provider level (13/785, 1.7%), patient level (570/785, 72.6%), and community level (166/785, 21.1%). The main barriers identified in these categories included those associated with knowledge (lack of knowledge about PrEP), access issues (lack of insurance coverage, no prescription, and impact of COVID-19 pandemic), and adherence (subjective reasons for why users terminated PrEP or decided not to start PrEP, such as side effects, alternative HIV prevention measures, and social stigma). Among the 785 PrEP posts, we identified 320 (40.8%) posts where users self-identified as racial or ethnic minority or as a sexual minority group with their specific PrEP barriers and concerns. Conclusions: Both objective and subjective reasons were identified as barriers reported by social media users when initiating, accessing, and adhering to PrEP. Though ample evidence supports PrEP as an effective HIV prevention strategy, user-generated posts nevertheless provide insights into what barriers are preventing people from broader adoption of PrEP, including topics that are specific to 2 different groups of sexual minority groups and racial and ethnic minority populations. Results have the potential to inform future health promotion and regulatory science approaches that can reach these HIV and AIDS communities that may benefit from PrEP.

Synthetic Cannabinoids in Prisons: Content Analysis of TikToks.

Background: Synthetic cannabinoids are a significant public health concern, especially among incarcerated populations due to increased reports of abuse. Recent news reports have highlighted the severe consequences of K2/Spice, a synthetic cannabinoid, among the prison population in the United States. Despite regulations against cell phone use, inmates use TikTok to post K2/Spice-related content. Objective: This study aimed to examine TikTok posts for use and illicit distribution of psychoactive substances (eg, K2/Spice) among incarcerated populations. Methods: The study collected TikTok videos associated with the #k2spice hashtag and used a data collection approach similar to snowball sampling. Inductive coding was used to conduct content analysis of video characteristics. Videos were manually annotated to generate binary classifications related to the use of K2/Spice as well as selling and buying activities associated with it. Statistical analysis was used to determine associations between a video's user engagement and an intent to buy or sell K2/Spice. Results: A total of 89 TikTok videos with the hashtag #k2spice were manually coded, with 40% (n=36) identified as displaying the use, solicitation, or adverse effects of K2/Spice among the prison population. Of them, 44.44% (n=16) were in a prison-based setting documenting adverse effects including possible overdose. Videos with higher user engagement were positively correlated with comments indicating an intent to buy or sell K2/Spice. Conclusions: K2/Spice is a drug subject to abuse among prison inmates in the United States, including depictions of its harmful effects being recorded and shared on TikTok. Lack of policy enforcement on TikTok and the need for better access to treatment services within the prison system may be exacerbating substance use among this highly vulnerable population. Minimizing the potential individual harm of this content on the incarcerated population should be a priority for social media platforms and the criminal justice system alike.

Campus Smoking Policies and Smoking-Related Twitter Posts Originating From California Public Universities: Retrospective Study.

BACKGROUND: The number of colleges and universities with smoke- or tobacco-free campus policies has been increasing. The effects of campus smoking policies on overall sentiment, particularly among young adult populations, are more difficult to assess owing to the changing tobacco and e-cigarette product landscape and differential attitudes toward policy implementation and enforcement. OBJECTIVE: The goal of the study was to retrospectively assess the campus climate toward tobacco use by comparing tweets from California universities with and those without smoke- or tobacco-free campus policies. METHODS: Geolocated Twitter posts from 2015 were collected using the Twitter public application programming interface in combination with cloud computing services on Amazon Web Services. Posts were filtered for tobacco products and behavior-related keywords. A total of 42,877,339 posts were collected from 2015, with 2837 originating from a University of California or California State University system campus, and 758 of these manually verified as being about smoking. Chi-square tests were conducted to determine if there were significant differences in tweet user sentiments between campuses that were smoke- or tobacco-free (all University of California campuses and California State University, Fullerton) compared to those that were not. A separate content analysis of tweets included in chi-square tests was conducted to identify major themes by campus smoking policy status. RESULTS: The percentage of positive sentiment tweets toward tobacco use was higher on campuses without a smoke- or tobacco-free campus policy than on campuses with a smoke- or tobacco-free campus policy (76.7% vs 66.4%, P=.03). Higher positive sentiment on campuses without a smoke- or tobacco-free campus policy may have been driven by general comments about one's own smoking behavior and comments about smoking as a general behavior. Positive sentiment tweets originating from campuses without a smoke- or tobacco-free policy had greater variation in tweet type, which may have also contributed to differences in sentiment among universities. CONCLUSIONS: Our study introduces preliminary data suggesting that campus smoke- and tobacco-free policies are associated with a reduction in positive sentiment toward smoking. However, continued expressions and intentions to smoke and reports of one's own smoking among Twitter users suggest a need for more research to better understand the dynamics between implementation of smoke- and tobacco-free policies and resulting tobacco behavioral sentiment.

Digital Surveillance to Identify California Alternative and Emerging Tobacco Industry Policy Influence and Mobilization on Facebook.

Growing popularity of electronic nicotine-delivery systems (ENDS) has coincided with a need to strengthen tobacco-control policy. In response, the ENDS industry has taken actions to mobilize against public health measures, including coordination on social media platforms. To explore this phenomenon, data mining was used to collect public posts on two Facebook public group pages: the California Consumer Advocates for Smoke Free Alternatives Association (CCASAA) and the community page of the Northern California Chapter of SFATA (NC-SFATA). Posts were manually annotated to characterize themes associated with industry political interference and user interaction. We collected 288 posts from the NC-SFATA and 411 posts from CCASAA. A total of 522 (74.7%) posts were categorized as a form of political interference, with 339 posts (64.9%) from CCASAA and 183 posts (35.1%) from NC-SFATA. We identified three different categories of policy interference-related posts: (1) providing updates on ENDS-related policy at the federal, state, and local levels; (2) sharing opinions about ENDS-related policies; (3) posts related to scientific information related to vaping; and (4) calls to action to mobilize against tobacco/ENDS policies. Our findings indicate that pro-tobacco social media communities on Facebook, driven by strategic activities of trade associations and their members, may act as focal points for anti-policy information dissemination, grass-roots mobilization, and industry coordination that needs further research.

A geospatial analysis of age disparities in resolute localities of tobacco and vaping-specific storefronts in California.

INTRODUCTION: Concomitant with the popularization of vaping, vape shops have dramatically proliferated over the past years. This study assesses whether vape storefronts in California are significantly associated with density of different age groups, and whether this differs between tobacco storefronts or non-specific tobacco retailers. METHODS: Addresses for licensed tobacco retailers were obtained from the California Department of Tax and Fee Administration. Business names and addresses were used to obtain store categories cross-referenced from Yelp. Using a cross-sectional ecological design, stores categorized as 'Vape Shop' or 'Tobacco Shop' were geolocated and compared with age-related variables from the American Community Survey. Regression was conducted in R to determine relationships between age group concentration, in ventiles, and proportion of tracts with tobacco-specific or vape-specific stores. Geospatial visualization was conducted using ArcGIS. RESULTS: We found 848 vape shops, 820 tobacco shops, 419 categorized as both, and 20320 retailers with neither category. Overall, 1800 tobacco and/or vape shops were categorized in 1557 of California's 23194 census tracts. A positive linear association was found between ventiles of two age categories, 20-24 and 25-34 years, and proportion of tracts with vape-specific or tobacco-specific shops separately. CONCLUSIONS: Positive associations were found for ages 20-34 years but not for other ages, suggesting vape shops are strategically located in areas populated by young adults. Location-based targeting increases access, thereby increasing proportion of tobacco users, and could be a critical factor in e-cigarette uptake and use. Further study to identify additional age-related demographic characteristics among clientele of tobacco storefronts is warranted.

Characterizing Self-Reported Tobacco, Vaping, and Marijuana-Related Tweets Geolocated for California College Campuses.

Introduction: College-aged youth are active on social media yet smoking-related social media engagement in these populations has not been thoroughly investigated. We sought to conduct an exploratory infoveillance study focused on geolocated data to characterize smoking-related tweets originating from California 4-year colleges on Twitter. Methods: Tweets from 2015 to 2019 with geospatial coordinates in CA college campuses containing smoking-related keywords were collected from the Twitter API stream and manually annotated for discussions about smoking product type, sentiment, and behavior. Results: Out of all tweets detected with smoking-related behavior, 46.7% related to tobacco use, 50.0% to marijuana, and 7.3% to vaping. Of these tweets, 46.1% reported first-person use or second-hand observation of smoking behavior. Out of 962 tweets with user sentiment, the majority (67.6%) were positive, ranging from 55.0% for California State University, Long Beach to 95.8% for California State University, Los Angeles. Discussion: We detected reporting of first- and second-hand smoking behavior on CA college campuses representing possible violation of campus smoking bans. The majority of tweets expressed positive sentiment about smoking behaviors, though there was appreciable variability between college campuses. This suggests that anti-smoking outreach should be tailored to the unique student populations of these college communities. Conclusion: Among tweets about smoking from California colleges, high levels of positive sentiment suggest that the campus climate may be less receptive to anti-smoking messages or adherence to campus smoking bans. Further research should investigate the degree to which this varies by campuses over time and following implementation of bans including validating using other sources of data.

Characterizing and assessing compliance of online vendors to the state of Massachusetts ENDS product sales ban.

INTRODUCTION: Recent reports of lung injury associated with Electronic Nicotine Delivery System (ENDS) products precipitated by increasing vaping prevalence and interest in flavors among adolescents has led to policies that restrict the sale, distribution, and accessibility of ENDS products. This study assessed compliance of online ENDS vendors to the Massachusetts temporary sales ban. METHODS: The study involved structured web surveillance for online ENDS vendors using keyword searches on Google search engine (October to November 2019.) Once vendors were identified, we conducted simulated online purchases, defined as placing an order for an ENDS product by putting it in the website shopping cart without finalizing payment. Simulated purchases and content analysis of websites was conducted to determine compliance characteristics. Fisher's exact test was used to identify associations between compliance and website characteristics such as location and age verification requirements. RESULTS: Simulated online purchases from 50 identified ENDS vendors yielded 72% (n=36) stores that were non-compliant and allowed placement of ENDS product orders, without restrictions, to a Massachusetts address. The remaining 14 websites had processes in place to prevent orders from buyers located in Massachusetts. Other characteristics of interest, including use of age verification, location data, and web registrar/registrant data were collected and reported. CONCLUSIONS: The September 2019 Massachusetts executive order was a comprehensive ban on selling ENDS products both online and offline. However, our study found that close to three-fourths of the vendors appeared to be non-compliant, indicating that implementation and enforcement are ongoing challenges for future tobacco control efforts on the internet. Policymaking needs to be specifically tailored to address the unique challenges of online environments, particularly in the context of identifying non-compliant sites, ensuring age verification, and addressing non-US sellers.

Big Data, Natural Language Processing, and Deep Learning to Detect and Characterize Illicit COVID-19 Product Sales: Infoveillance Study on Twitter and Instagram.

BACKGROUND: The coronavirus disease (COVID-19) pandemic is perhaps the greatest global health challenge of the last century. Accompanying this pandemic is a parallel "infodemic," including the online marketing and sale of unapproved, illegal, and counterfeit COVID-19 health products including testing kits, treatments, and other questionable "cures." Enabling the proliferation of this content is the growing ubiquity of internet-based technologies, including popular social media platforms that now have billions of global users. OBJECTIVE: This study aims to collect, analyze, identify, and enable reporting of suspected fake, counterfeit, and unapproved COVID-19-related health care products from Twitter and Instagram. METHODS: This study is conducted in two phases beginning with the collection of COVID-19-related Twitter and Instagram posts using a combination of web scraping on Instagram and filtering the public streaming Twitter application programming interface for keywords associated with suspect marketing and sale of COVID-19 products. The second phase involved data analysis using natural language processing (NLP) and deep learning to identify potential sellers that were then manually annotated for characteristics of interest. We also visualized illegal selling posts on a customized data dashboard to enable public health intelligence. RESULTS: We collected a total of 6,029,323 tweets and 204,597 Instagram posts filtered for terms associated with suspect marketing and sale of COVID-19 health products from March to April for Twitter and February to May for Instagram. After applying our NLP and deep learning approaches, we identified 1271 tweets and 596 Instagram posts associated with questionable sales of COVID-19-related products. Generally, product introduction came in two waves, with the first consisting of questionable immunity-boosting treatments and a second involving suspect testing kits. We also detected a low volume of pharmaceuticals that have not been approved for COVID-19 treatment. Other major themes detected included products offered in different languages, various claims of product credibility, completely unsubstantiated products, unapproved testing modalities, and different payment and seller contact methods. CONCLUSIONS: Results from this study provide initial insight into one front of the "infodemic" fight against COVID-19 by characterizing what types of health products, selling claims, and types of sellers were active on two popular social media platforms at earlier stages of the pandemic. This cybercrime challenge is likely to continue as the pandemic progresses and more people seek access to COVID-19 testing and treatment. This data intelligence can help public health agencies, regulatory authorities, legitimate manufacturers, and technology platforms better remove and prevent this content from harming the public.

Machine Learning to Detect Self-Reporting of Symptoms, Testing Access, and Recovery Associated With COVID-19 on Twitter: Retrospective Big Data Infoveillance Study.

BACKGROUND: The coronavirus disease (COVID-19) pandemic is a global health emergency with over 6 million cases worldwide as of the beginning of June 2020. The pandemic is historic in scope and precedent given its emergence in an increasingly digital era. Importantly, there have been concerns about the accuracy of COVID-19 case counts due to issues such as lack of access to testing and difficulty in measuring recoveries. OBJECTIVE: The aims of this study were to detect and characterize user-generated conversations that could be associated with COVID-19-related symptoms, experiences with access to testing, and mentions of disease recovery using an unsupervised machine learning approach. METHODS: Tweets were collected from the Twitter public streaming application programming interface from March 3-20, 2020, filtered for general COVID-19-related keywords and then further filtered for terms that could be related to COVID-19 symptoms as self-reported by users. Tweets were analyzed using an unsupervised machine learning approach called the biterm topic model (BTM), where groups of tweets containing the same word-related themes were separated into topic clusters that included conversations about symptoms, testing, and recovery. Tweets in these clusters were then extracted and manually annotated for content analysis and assessed for their statistical and geographic characteristics. RESULTS: A total of 4,492,954 tweets were collected that contained terms that could be related to COVID-19 symptoms. After using BTM to identify relevant topic clusters and removing duplicate tweets, we identified a total of 3465 (<1%) tweets that included user-generated conversations about experiences that users associated with possible COVID-19 symptoms and other disease experiences. These tweets were grouped into five main categories including first- and secondhand reports of symptoms, symptom reporting concurrent with lack of testing, discussion of recovery, confirmation of negative COVID-19 diagnosis after receiving testing, and users recalling symptoms and questioning whether they might have been previously infected with COVID-19. The co-occurrence of tweets for these themes was statistically significant for users reporting symptoms with a lack of testing and with a discussion of recovery. A total of 63% (n=1112) of the geotagged tweets were located in the United States. CONCLUSIONS: This study used unsupervised machine learning for the purposes of characterizing self-reporting of symptoms, experiences with testing, and mentions of recovery related to COVID-19. Many users reported symptoms they thought were related to COVID-19, but they were not able to get tested to confirm their concerns. In the absence of testing availability and confirmation, accurate case estimations for this period of the outbreak may never be known. Future studies should continue to explore the utility of infoveillance approaches to estimate COVID-19 disease severity.