Topics of Nicotine-Related Discussions on Twitter: Infoveillance Study

Uncovering the Complexity of Perinatal Polysubstance Use Disclosure Patterns on X: Mixed Methods Study

BACKGROUND

According to the Morbidity and Mortality Weekly Report, polysubstance use among pregnant women is prevalent, with 38.2% of those who consume alcohol also engaging in the use of one or more additional substances. However, the underlying mechanisms, contexts, and experiences of polysubstance use are unclear. Organic information is abundant on social media such as X (formerly Twitter). Traditional quantitative and qualitative methods, as well as natural language processing techniques, can be jointly used to derive insights into public opinions, sentiments, and clinical and public health policy implications.

OBJECTIVE

Based on perinatal polysubstance use (PPU) data that we extracted on X from May 1, 2019, to October 31, 2021, we proposed two primary research questions: (1) What is the overall trend and sentiment of PPU discussions on X? (2) Are there any distinct patterns in the discussion trends of PPU-related tweets? If so, what are the implications for perinatal care and associated public health policies?

METHODS

We used X's application programming interface to extract >6 million raw tweets worldwide containing ≥2 prenatal health- and substance-related keywords provided by our clinical team. After removing all non-English-language tweets, non-US tweets, and US tweets without disclosed geolocations, we obtained 4848 PPU-related US tweets. We then evaluated them using a mixed methods approach. The quantitative analysis applied frequency, trend analysis, and several natural language processing techniques such as sentiment analysis to derive statistics to preview the corpus. To further understand semantics and clinical insights among these tweets, we conducted an in-depth thematic content analysis with a random sample of 500 PPU-related tweets with a satisfying κ score of 0.7748 for intercoder reliability.

RESULTS

Our quantitative analysis indicates the overall trends, bigram and trigram patterns, and negative sentiments were more dominant in PPU tweets (2490/4848, 51.36%) than in the non-PPU sample (1323/4848, 27.29%). Paired polysubstance use (4134/4848, 85.27%) was the most common, with the combination alcohol and drugs identified as the most mentioned. From the qualitative analysis, we identified 3 main themes: nonsubstance, single substance, and polysubstance, and 4 subthemes to contextualize the rationale of underlying PPU behaviors: lifestyle, perceptions of others' drug use, legal implications, and public health.

CONCLUSIONS

This study identified underexplored, emerging, and important topics related to perinatal PPU, with significant stigmas and legal ramifications discussed on X. Overall, public sentiments on PPU were mixed, encompassing negative (2490/4848, 51.36%), positive (1884/4848, 38.86%), and neutral (474/4848, 9.78%) sentiments. The leading substances in PPU were alcohol and drugs, and the normalization of PPU discussed on X is becoming more prevalent. Thus, this study provides valuable insights to further understand the complexity of PPU and its implications for public health practitioners and policy makers to provide proper access and support to individuals with PPU.

Statin Twitter: Human and Automated Bot Contributions, 2010 to 2022

BACKGROUND

Many individuals eligible for statin therapy decline treatment, often due to fear of adverse effects. Misinformation about statins is common and drives statin reluctance, but its prevalence on social media platforms, such as Twitter (now X) remains unclear. Social media bots are known to proliferate medical misinformation, but their involvement in statin-related discourse is unknown. This study examined temporal trends in volume, author type (bot or human), and sentiment of statin-related Twitter posts (tweets).

METHODS AND RESULTS

We analyzed original tweets with statin-related terms from 2010 to 2022 using a machine learning-derived classifier to determine the author's bot probability, natural language processing to assign each tweet a negative or positive sentiment, and manual qualitative analysis to identify statin skepticism in a random sample of all tweets and in highly influential tweets. We identified 1 155 735 original statin-related tweets. Bots produced 333 689 (28.9%), humans produced 699 876 (60.6%), and intermediate probability accounts produced 104 966 (9.1%). Over time, the proportion of bot tweets decreased from 47.8% to 11.3%, and human tweets increased from 43.6% to 79.8%. The proportion of negative-sentiment tweets increased from 27.8% to 43.4% for bots and 30.9% to 38.4% for humans. Manually coded statin skepticism increased from 8.0% to 19.0% for bots and from 26.0% to 40.0% for humans.

CONCLUSIONS

Over the past decade, humans have overtaken bots as generators of statin-related content on Twitter. Negative sentiment and statin skepticism have increased across all user types. Twitter may be an important forum to combat statin-related misinformation.

Oral Nicotine Gum Discussions on Twitter: Content Analysis

BACKGROUND

Oral nicotine gum such as LUCY, which comes in colorful packaging, mimicking traditional chewing gum, is becoming popular. Many brands of gum have not been approved by the FDA for smoking cessation. This study examined public discourse about, including sentiment toward, oral nicotine gum on Twitter.

METHODS

We used Twitter's Streaming Application Programming Interface to collect data from January 1, 2021, to December 21, 2021, using "nicotine gum" and/or "#nicotinegum" search terms (N = 19 171 unique tweets were collected). We used an inductive approach to become familiar with the data, generated a codebook, and conducted a content analysis on (n = 2152) tweets.

RESULTS

Cessation (n = 716, 33.3%), personal experience (n = 370, 17.2%), and addiction to gum (n = 135, 6.3%) were the most prevalent themes. Cessation tweets primarily discussed cigarette smoking cessation (n = 418, 58.4% of cessation tweets) and successful cessation experiences (n = 155, 21.6%). Other identified themes pertained to using nicotine gum for cognitive enhancement or catching a "buzz" (n = 102, 4.7%), marketing (n = 98, 4.6%), using nicotine gum with other substances (n = 90, 4.2%), and adverse effects (n = 63, 2.9%). Sentiment analysis results revealed that 675 (44.2%) tweets were categorized as neutral, 605 (39.6%) tweets were classified as positive, and 248 tweets (16.2%) were negative.

CONCLUSIONS

About one-third of tweets in our corpus mentioned nicotine gum in the context of smoking cessation. Most nicotine gum-related posts conveyed positive and neutral sentiments. Future studies should consider adding novel nicotine gum-specific search terms as well as exploring other social media platforms to gain more insights about these products.

IMPLICATIONS

Our findings suggest that Twitter has the potential to track and facilitate conversations between those seeking cigarette cessation advice and those who have successfully quit tobacco by using nicotine gum. Monitoring of promotional content from nicotine gum companies is needed to ensure these products are not appealing to youth and nonusers of tobacco.

Enhancing the Predictive Power of Google Trends Data Through Network Analysis: Infodemiology Study of COVID-19

BACKGROUND

The COVID-19 outbreak has revealed a high demand for timely surveillance of pandemic developments. Google Trends (GT), which provides freely available search volume data, has been proven to be a reliable forecast and nowcast measure for public health issues. Previous studies have tended to use relative search volumes from GT directly to analyze associations and predict the progression of pandemic. However, GT's normalization of the search volumes data and data retrieval restrictions affect the data resolution in reflecting the actual search behaviors, thus limiting the potential for using GT data to predict disease outbreaks.

OBJECTIVE

This study aimed to introduce a merged algorithm that helps recover the resolution and accuracy of the search volume data extracted from GT over long observation periods. In addition, this study also aimed to demonstrate the extended application of merged search volumes (MSVs) in combination of network analysis, via tracking the COVID-19 pandemic risk.

METHODS

We collected relative search volumes from GT and transformed them into MSVs using our proposed merged algorithm. The MSVs of the selected coronavirus-related keywords were compiled using the rolling window method. The correlations between the MSVs were calculated to form a dynamic network. The network statistics, including network density and the global clustering coefficients between the MSVs, were also calculated.

RESULTS

Our research findings suggested that although GT restricts the search data retrieval into weekly data points over a long period, our proposed approach could recover the daily search volume over the same investigation period to facilitate subsequent research analyses. In addition, the dynamic time warping diagrams show that the dynamic networks were capable of predicting the COVID-19 pandemic trends, in terms of the number of COVID-19 confirmed cases and severity risk scores.

CONCLUSIONS

The innovative method for handling GT search data and the application of MSVs and network analysis to broaden the potential for GT data are useful for predicting the pandemic risk. Further investigation of the GT dynamic network can focus on noncommunicable diseases, health-related behaviors, and misinformation on the internet.

An Analysis of Twitter Posts About the U.S. Food and Drug Administration's Menthol Ban

INTRODUCTION

Although the U.S. Food and Drug Administration (FDA) banned characterizing flavors in cigarettes in 2009, this initial ban exempted menthol. After examining numerous reports on the adverse health effects of menthol cigarettes, the FDA proposed a menthol ban in April 2022. This study analyzed Twitter data to describe public reaction to this announcement.

AIMS AND METHODS

Posts containing the word "menthol" and/or "#menthol" were collected from April 21, 2022 to May 5, 2022 from Twitter's Streaming Application Programming Interface (API). A random sampling procedure supplied 1041 tweets for analysis. Following an inductive approach to content analysis, posts were classified into one or more of 11 themes.

RESULTS

Posts discussed the FDA announcement (n = 153, 14.7%), racial discrimination (n = 101, 9.7%), distrust in government (n = 67, 6.4%), inconsistencies between policies (n = 52, 5.0%), public health benefits (n = 42, 4%), freedom of choice (n = 22, 2.1%), and health equity (n = 21, 2.0%). Posts contained misinformation (n = 20, 1.9%), and discussed the potential for illicit markets (n = 18, 1.7%) and the need for cessation support (n = 4, 0.4%). 541 (52.0%) tweets did not fit into any of the prescribed themes.

CONCLUSIONS

Twitter posts with the word "menthol" commonly discussed distrust in government and mentioned racial discrimination. Findings demonstrated the possibility of near real-time Twitter monitoring of public opinion on a menthol ban. These data may be valuable for designing tobacco control health communication campaigns in the future.

IMPLICATIONS

The U.S. FDA proposed a ban on menthol cigarettes in April 2022. This study's content analyzed Twitter posts over a 2-week period to understand the public's response to the proposed menthol ban. Twitter posts with the word "menthol" often discussed distrust in government and mentioned racial discrimination. Findings demonstrated the possibility of near real-time Twitter monitoring of public opinion of regulatory action. Findings underscore the need to educate the public about the potential health benefits of banning menthol from cigarettes, particularly for populations that experience tobacco-related health disparities.

Attitudes towards the 'Shisha No Thanks' campaign video: Content analysis of Facebook comments

INTRODUCTION

While social media are commonly used in public health campaigns, there is a gap in our understanding of what happens after the campaign is seen by the target audience. This study aims to understand how the Shisha No Thanks campaign video was received by the Facebook audience by analyzing Facebook comments posted to it. Specifically, this study aims to determine whether the Facebook audience accepted or rejected the campaign’s message.

METHODS

A sample of the Facebook comments was extracted, and the study team, which included cultural support workers, developed content categories consistent with the research question. Each comment was then coded by three team members, and only assigned a category if there was agreement by at least two members.

RESULTS

Of the 4990 comments that were sampled, 9.1% (456) accepted the campaign message, 22.9% (1144) rejected the message, 21.8% (1089) were unclear, and 46.1% (2301) contained only tagged names. Of the sample, 2.8% (138) indicated the commenter took on board the campaign message by expressing an intention to stop smoking shisha, or asking a friend to stop smoking shisha. Of the comments that showed rejection of the campaign, the majority were people dismissing the campaign by laughing at it or expressing pro-shisha sentiments.

CONCLUSIONS

This study demonstrates that conducting content analyses of social media comments can provide important insight into how a campaign message is received by a social media audience.

Charming e-cigarette users with distorted science: a survey examining social media platform use, nicotine-related misinformation and attitudes towards the tobacco industry

OBJECTIVE

To examine the role of social media in promoting recall and belief of distorted science about nicotine and COVID-19 and whether recall and belief predict tobacco industry beliefs.

DESIGN

Young adults aged 18-34 years (N=1225) were surveyed cross-sectionally via online Qualtrics panel. The survey assessed recall and belief in three claims about nicotine and COVID-19 and three about nicotine in general followed by assessments of industry beliefs and use of social media. Ordinal logistic regression with robust standard errors controlling for gender, race/ethnicity, education, current e-cigarette use and age was used to examine relationships between variables.

RESULTS

Twitter use was associated with higher odds of recall (OR=1.21, 95% CI=1.01 to 1.44) and belief (OR=1.26, 95% CI=1.04 to 1.52) in COVID-19-specific distorted science. YouTube use was associated with higher odds of believing COVID-19-specific distorted science (OR=1.32, 95% CI=1.09 to 1.60). Reddit use was associated with lower odds of believing COVID-19-specific distorted science (OR=0.72, 95% CI=0.59 to 0.88). Recall (OR=1.26, 95% CI=1.07 to 1.47) and belief (OR=1.28, 95% CI=1.09 to 1.50) in distorted science about nicotine in general as well as belief in distorted science specific to COVID-19 (OR=1.61, 95% CI=1.34 to 1.95) were associated with more positive beliefs about the tobacco industry. Belief in distorted science about nicotine in general was also associated with more negative beliefs about the tobacco industry (OR=1.18, 95% CI=1.02 to 1.35).

CONCLUSIONS

Use of social media platforms may help to both spread and dispel distorted science about nicotine. Addressing distorted science about nicotine is important, as it appears to be associated with more favourable views of the tobacco industry which may erode public support for effective regulation.

Content and trend analysis of user-generated nicotine sickness tweets: A retrospective infoveillance study

INTRODUCTION

Exposure to pro-tobacco and electronic nicotine delivery system (ENDS) social media content can lead to overconsumption, increasing the likelihood of nicotine poisoning. This study aims to examine trends and characteristics of nicotine sickness content on Twitter between 2018–2020.

METHODS

Tweets were collected retrospectively from the Twitter Academic Research Application Programming Interface (API) stream filtered for keywords: ‘nic sick’, ‘nicsick’, ‘vape sick’, ‘vapesick’ between 2018–2020. Collected tweets were manually annotated to identify suspected user-generated reports of nicotine sickness and related themes using an inductive coding approach. The Augmented Dickey-Fuller (ADF) test was used to assess stationarity in the monthly variation of the volume of tweets between 2018–2020.

RESULTS

A total of 5651 tweets contained nicotine sickness-related keywords and 18.29% (n=1034) tweets reported one or more suspected nicotine sickness symptoms of varied severity. These tweets were also grouped into five related categories including firsthand and secondhand reports of symptoms, intentional overconsumption of nicotine products, users expressing intention to quit after ‘nic sick’ symptoms, mention of nicotine product type/brand name that they consumed while ‘nic sick’, and users discussing symptoms associated with nicotine withdrawal following cessation attempts. The volume of tweets reporting suspected nicotine sickness appeared to increase throughout the study period, except between February and April 2020. Stationarity in the volume of ‘nicsick’ tweets between 2018–2020 was not statistically significant (ADF= -0.32, p=0.98) indicating a change in the volume of tweets.

CONCLUSIONS

Results point to the need for alternative forms of adverse event surveillance and reporting, to appropriately capture the growing health burden of vaping. Infoveillance approaches on social media platforms can help to assess the volume and characteristics of user-generated content discussing suspected nicotine poisoning, which may not be reported to poison control centers. Increasing volume of user-reported nicotine sickness and intentional overconsumption of nicotine in twitter posts represent a concerning trend associated with ENDS-related adverse events and poisoning.