Bryan A Liang’s research while affiliated with University of California System and other places

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.

Due to corruption and limited oversight, philanthropy in China has come under increasing scrutiny by both the Chinese government and public. In particular, corruption has impacted charities, resulting in operational funding declines that have also impacted legitimate charities serving the vulnerable. Hence, an increase in the number of Chinese charities without adequate transparency and needed good governance threatens the health of these organizations and their service groups. In response, the Chinese government enacted the 2016 Charity Law, implemented on September 1, 2016. Yet the impact of this law, if any, is unknown. We therefore conducted a literature review of the academic and gray literature to assess characteristics of the law, its strengths and limitations, and to explore anti-corruption case studies. In addition, we reviewed publicly available secondary data on the transparency status—complete financial information and responsible staff—of Chinese charity organizations and a group of health-related specific charities before and after the Law’s enactment. This included the transparency scores of the top 100 charities and top 30 health-related charities in China. The academic literature we reviewed focused on general governmental corruption and little on charity-related corruption. However, the gray literature reflected a poor view of charities in China due to publicized claims of fund and abuse. Based on our secondary data analysis, we found charity organizations’ transparency shows some variation but as a total continues to score low overall across the top net asset holding charities. Importantly, based on a regression analysis, the transparency scores of Chinese charities did not experience a significant change despite the Charity Law’s enactment. In response, strengthening the Charity Law by enhancing enforcement of financial regulations and implementing systemic good governance measures is needed. Further, efficiency initiatives such as philanthropy de-administration, e-government, and outsourcing monitoring of Chinese charities to external agencies would promote trustworthiness and credibility of Chinese charity organizations now and in the future.

Background: The 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 proliferation of this content is growing ubiquity of Internet-based technologies, including popular social media platforms that now have billions of global users. Objective: To collect, analyze, identify and enable reporting of suspected fake, counterfeit, and unapproved COVID-19-related healthcare products from Twitter and Instagram. Methods: The study was conducted in two phases beginning with collection of COVID-19-related Twitter and Instagram posts using a combination of web scraping on Instagram and filtering the public streaming Twitter API for keywords associated with suspect marketing and sale of COVID-19 products. The second phase involved data analysis using natural language processing 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 - April for Twitter and February - May for Instagram. After applying our NLP and deep learning approaches, we identified 1,271 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. Clinicaltrial: Not applicable.

BACKGROUND The 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 proliferation of this content is growing ubiquity of Internet-based technologies, including popular social media platforms that now have billions of global users. OBJECTIVE To collect, analyze, identify and enable reporting of suspected fake, counterfeit, and unapproved COVID-19-related healthcare products from Twitter and Instagram. METHODS The study was conducted in two phases beginning with collection of COVID-19-related Twitter and Instagram posts using a combination of web scraping on Instagram and filtering the public streaming Twitter API for keywords associated with suspect marketing and sale of COVID-19 products. The second phase involved data analysis using natural language processing 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 – April for Twitter and February – May for Instagram. After applying our NLP and deep learning approaches, we identified 1,271 tweets and 596 Instagram posts associated with questionable sales of COVID-19-related products. Generally, product introduction came in three waves, with the first consisting of questionable immunity-boosting treatments, a second involving suspect testing kits, and a third of pharmaceuticals that have not been approved for COVID-19 treatment, with these waves following news coverage about product developments. Other major themes detected included accounts with descriptive COVID-19 accounts, products offered in different languages, various claims of product credibility, 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 are active on two popular social media platforms. The challenge of combating this form of cybercrime is likely to continue as the pandemic progresses and more people seek access to COVID-19 information and treatment. Visualization of detected sellers and identification of their social media communication strategies can provide needed intelligence to public health agencies, regulatory authorities, legitimate manufacturers, and technology platforms to better remove and prevent this content from harming the public.

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. ResultsA 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 (

BACKGROUND The coronavirus (COVID-19) pandemic is a globally, rapidly spreading event with close to 2.5 million cases as of mid-April, representing an outbreak of historical scope and one with an accelerating trajectory. However, there are ongoing 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 of COVID-19-related symptoms, experiences with access to testing, and mentions of recovery using an unsupervised machine learning approach. METHODS Tweets were collected from the Twitter public API from March 3-20 filtered for general COVID-19-related keywords and then further filtered for terms related to COVID-19 symptoms. After data cleaning and processing data, 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 related to COVID-19 symptoms, testing, and recovery conversations. Tweets in these clusters were then extracted and manually annotated for content analysis and then analyzed for statistical and geographic characteristics. RESULTS A total of 4,492,954 tweets were collected that contained COVID-19-related symptom terms. After using BTM to identify relevant COVID-19 clusters and removing duplicate tweets, we identified a total of 3,465 (<1%) tweets that included user generated conversations about experiences perceived to be related to COVID-19. These tweets were grouped into five main categories including first and second-hand reports of COVID-19-related symptoms, symptom reporting concurrent with lack of access to testing, discussion of recovery, confirmation of negative COVID-19 diagnosis after receiving testing, and users recalling past symptoms and questioning whether they had been previously infected with COVID-19. Co-occurrence of themes was statistically significant for users reporting symptoms with lack of testing and with discussion of recovery. Sixty-three percent (n=1112) of tweets with geospatial coordinates were from the U.S. CONCLUSIONS In this study, we analyzed Twitter for the purposes of characterizing conversations regarding self-reporting of COVID-19-related symptoms, access to testing, and experiences with purported recovery for the purposes of digital contact tracing. It appears that many users reported COVID-19-related symptoms, but never got tested due to lack of access. However, it is unclear how many of these users were actual cases and in the absence of further testing, accurate case estimations may never be known. Future studies should continue to explore the utility of social media and other forms of electronic data to estimate COVID-19 disease severity.