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

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 (eg, "#tobaccofreenicotine) related to synthetic nicotine. Using Bidirectional Encoder Representations from Transformers, 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 (eg, cost discussion, contact information for offline sales).

RESULTS

A total of 7425 unique posts were collected with 2219 posts identified as related to promotion and selling of synthetic nicotine products. Nicotine pouches (52.9%, n = 1174), electronic nicotine delivery systems (30.6%, n = 679), and flavored e-liquids (14.1%, n = 313) were most commonly promoted. About 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 Food and Drug Administration (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 are 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.

Topic prediction for tobacco control based on COP9 tweets using machine learning techniques

The prediction of tweets associated with specific topics offers the potential to automatically focus on and understand online discussions surrounding these issues. This paper introduces a comprehensive approach that centers on the topic of "harm reduction" within the broader context of tobacco control. The study leveraged tweets from the period surrounding the ninth Conference of the Parties to review the Framework Convention on Tobacco Control (COP9) as a case study to pilot this approach. By using Latent Dirichlet Allocation (LDA)-based topic modeling, the study successfully categorized tweets related to harm reduction. Subsequently, various machine learning techniques were employed to predict these topics, achieving a prediction accuracy of 91.87% using the Random Forest algorithm. Additionally, the study explored correlations between retweets and sentiment scores. It also conducted a toxicity analysis to understand the extent to which online conversations lacked neutrality. Understanding the topics, sentiment, and toxicity of Twitter data is crucial for identifying public opinion and its formation. By specifically focusing on the topic of "harm reduction" in tweets related to COP9, the findings offer valuable insights into online discussions surrounding tobacco control. This understanding can aid policymakers in effectively informing the public and garnering public support, ultimately contributing to the successful implementation of tobacco control policies.

Detecting nuance in conspiracy discourse: Advancing methods in infodemiology and communication science with machine learning and qualitative content coding

The spread of misinformation and conspiracies has been an ongoing issue since the early stages of the internet era, resulting in the emergence of the field of infodemiology (i.e., information epidemiology), which investigates the transmission of health-related information. Due to the high volume of online misinformation in recent years, there is a need to continue advancing methodologies in order to effectively identify narratives and themes. While machine learning models can be used to detect misinformation and conspiracies, these models are limited in their generalizability to other datasets and misinformation phenomenon, and are often unable to detect implicit meanings in text that require contextual knowledge. To rapidly detect evolving conspiracist narratives within high volume online discourse while identifying nuanced themes requiring the comprehension of subtext, this study describes a hybrid methodology that combines natural language processing (i.e., topic modeling and sentiment analysis) with qualitative content coding approaches to characterize conspiracy discourse related to 5G wireless technology and COVID-19 on Twitter (currently known as 'X'). Discourse that focused on correcting 5G conspiracies was also analyzed for comparison. Sentiment analysis shows that conspiracy-related discourse was more likely to use language that was analytic, combative, past-oriented, referenced social status, and expressed negative emotions. Corrections discourse was more likely to use words reflecting cognitive processes, prosocial relations, health-related consequences, and future-oriented language. Inductive coding characterized conspiracist narratives related to global elites, anti-vax sentiment, medical authorities, religious figures, and false correlations between technology advancements and disease outbreaks. Further, the corrections discourse did not address many of the narratives prevalent in conspiracy conversations. This paper aims to further bridge the gap between computational and qualitative methodologies by demonstrating how both approaches can be used in tandem to emphasize the positive aspects of each methodology while minimizing their respective drawbacks.

Tobacco Product Marketing Orders and Online Marketing and Sale of Unauthorized ENDS Products

This study characterizes online marketing of unauthorized electronic nicotine delivery systems (ENDS).

Implications of nicotine vaping products for tobacco control in ASEAN low-income and middle-income countries: in-depth interviews with experts from the region

OBJECTIVES

The use of nicotine vaping products (NVPs) has increased in low-income and middle-income countries (LMICs) in the Association of Southeast Asian Nations (ASEAN) region; however, it is uncertain what implications the presence and use of NVPs have for tobacco control.

DESIGN

In-depth interviews were conducted to explore ASEAN tobacco control experts' (n=11) views on the rise of NVP use in ASEAN LMICs, current NVP policies, the potential harm reduction and smoking cessation utilities of these devices, and what implications they may have for tobacco control. Data were analysed using inductive, reflexive thematic analysis.

RESULTS

Five themes emerged: (1) NVPs threaten tobacco control in ASEAN LMICs; (2) commercial factors influence youth appeal and access: product attributes, marketing, supply chains; (3) opposition to the smoking cessation and harm reduction utilities of NVPs; (4) policies are inconsistent and fragmented in the region; and (5) tobacco industry power and tactics have been used to capture NVP markets.

CONCLUSIONS

ASEAN tobacco control experts believe that NVPs pose a threat to youth and non-smokers in LMICs in the region, largely because of tobacco industry NVP marketing activities. They do not support the use of NVPs for smoking cessation or harm reduction and call for more restrictions and consistent policy enforcement across the region to protect young people, while also cautiously recognising that use of NVPs may have some benefits for smokers.

Public Figure Vaccination Rhetoric and Vaccine Hesitancy: Retrospective Twitter Analysis

Background

Social media has emerged as a critical mass communication tool, with both health information and misinformation now spread widely on the web. Prior to the COVID-19 pandemic, some public figures promulgated anti-vaccine attitudes, which spread widely on social media platforms. Although anti-vaccine sentiment has pervaded social media throughout the COVID-19 pandemic, it is unclear to what extent interest in public figures is generating anti-vaccine discourse.

Objective

We examined Twitter messages that included anti-vaccination hashtags and mentions of public figures to assess the connection between interest in these individuals and the possible spread of anti-vaccine messages.

Methods

We used a data set of COVID-19-related Twitter posts collected from the public streaming application programming interface from March to October 2020 and filtered it for anti-vaccination hashtags "antivaxxing," "antivaxx," "antivaxxers," "antivax," "anti-vaxxer," "discredit," "undermine," "confidence," and "immune." Next, we applied the Biterm Topic model (BTM) to output topic clusters associated with the entire corpus. Topic clusters were manually screened by examining the top 10 posts most highly correlated in each of the 20 clusters, from which we identified 5 clusters most relevant to public figures and vaccination attitudes. We extracted all messages from these clusters and conducted inductive content analysis to characterize the discourse.

Results

Our keyword search yielded 118,971 Twitter posts after duplicates were removed, and subsequently, we applied BTM to parse these data into 20 clusters. After removing retweets, we manually screened the top 10 tweets associated with each cluster (200 messages) to identify clusters associated with public figures. Extraction of these clusters yielded 768 posts for inductive analysis. Most messages were either pro-vaccination (n=329, 43%) or neutral about vaccination (n=425, 55%), with only 2% (14/768) including anti-vaccination messages. Three main themes emerged: (1) anti-vaccination accusation, in which the message accused the public figure of holding anti-vaccination beliefs; (2) using "anti-vax" as an epithet; and (3) stating or implying the negative public health impact of anti-vaccination discourse.

Conclusions

Most discussions surrounding public figures in common hashtags labelled as "anti-vax" did not reflect anti-vaccination beliefs. We observed that public figures with known anti-vaccination beliefs face scorn and ridicule on Twitter. Accusing public figures of anti-vaccination attitudes is a means of insulting and discrediting the public figure rather than discrediting vaccines. The majority of posts in our sample condemned public figures expressing anti-vax beliefs by undermining their influence, insulting them, or expressing concerns over public health ramifications. This points to a complex information ecosystem, where anti-vax sentiment may not reside in common anti-vax-related keywords or hashtags, necessitating further assessment of the influence that public figures have on this discourse.