Toward Real-Time Infoveillance of Twitter Health Messages

Using prospective mixed methods to investigate the effect of the COVID-19 pandemic on cannabis demand

Following the COVID-19 pandemic, it is vital to understand how major global stressors influence substance use, including cannabis-related outcomes. The Marijuana Purchase Task assesses hypothetical cannabis demand (i.e., relative reinforcing value) and can detect contextual alterations. This study paired prospective cannabis demand assessment with qualitative inquiry to explore how COVID-19 impacted cannabis use behavior. Individuals previously enrolled in a laboratory cannabis administration study opted in to a remote follow-up survey (n = 41, 46% female). Participants were categorized as those who did or did not increase use based on self-reported changes in cannabis flower use and provided contextual explanations regarding pandemic-related influences on cannabis outcomes. General linear models with repeated measures examined mean differences in demand by occasion (i.e., before/during COVID-19), group (i.e., those who did/did not increase use), and their interaction. Those who increased use exhibited significantly higher demand during the pandemic; those who did not increase use exhibited similar demand across time revealing a Group × Time interaction. Thematic analysis contextualized quantitative findings, explaining external influences that affect use and demand (e.g., changes in cost, access, environment). COVID-19 differentially impacted cannabis use and demand, with prepandemic use affecting trajectories. Contextual influences (i.e., availability, free time, income) facilitate the escalation of use under conditions of extreme global stress.

Content analysis of substance use disorder recovery discourse on Twitter: From personal recovery narratives to marketing of addiction treatment

BACKGROUND

Substance use disorder (SUD) is a prodigious public health issue characterized by a substantial treatment gap. Despite challenges, millions have resolved a prior significant alcohol or drug problem, increasingly using online supports as a part of their recovery efforts (e.g., virtual mutual-help group meetings, traditional social networking sites [SNS]). However, the content surrounding SUD recovery-related discussion on SNS such as Twitter remains largely unexamined. To fill this gap, we explored public tweets using SUD recovery-related hashtags.

METHODS

From January 1, 2022, to December 31, 2022, 455,968 public tweets were collected using SUD recovery-related hashtags. Natural language processing was used to identify and remove irrelevant groupings of tweets from the dataset, resulting in a final corpus of 186,460 tweets. A random subsample of 1800 tweets was extracted for content analysis, involving codebook development, manual annotation by trained coders, and inter-rater reliability assessment (average Cohen's κ = 0.77).

RESULTS

Nearly half (41.7%) of SUD recovery-related posts were from individuals in or seeking recovery, while 21.3% originated from addiction treatment industry accounts. Common themes included addiction treatment marketing (27.4%; some of which promoted scientifically unsupported products or services), emotional support (15.6%), celebrating a recovery milestone (15.4%), alcohol/drug-related sociopolitical commentary (14.7%), expressions of gratitude (11.5%), and mutual-help group participation (8.7%).

CONCLUSIONS

SUD recovery-related content on Twitter reflected individuals seeking social support during efforts to initiate or maintain recovery. However, these accounts may be met with marketing material from entities that misrepresent their services or promote products based on unsubstantiated claims. Stricter (or enforcement of existing) regulations may be warranted to protect vulnerable SNS platform users from entities seeking to exploit them for financial gain.

Use of cannabis to manage symptoms of mental and physical health conditions during pregnancy: analysis of a pro-cannabis pregnancy forum

Introduction

Rates of prenatal cannabis use (PCU) have increased in recent years. Despite evidence of developmental health consequences to offspring and birthing person, there has been a reduction in the perception of PCU-related harms. Due to the stigma and risk of legal consequences associated with disclosing PCU, individuals are often cautious to seek information from their healthcare providers. Thus, pregnant people are more likely to seek information from anonymous sources, such as online support forums. Information garnered from these anonymous online forums can shed light on the patterns and motives for cannabis use among this population. These insights can help to better inform prevention efforts aimed at reducing potential harms of PCU and improve intervention efforts.

Methods

Posts (N = 120) from an online pro-cannabis pregnancy forum called "Ganja Mamas" on WhattoExpect.com were randomly selected and analyzed if they covered topics related to PCU. A qualitative coding structure based on the existing PCU literature was created and refined to include other emergent topics. The coding structure was used to apply thematic analysis to posts; associated codes were grouped into themes. Codes specific to symptom management for physical and mental health were subsequently subjected to further conceptual analysis for the current study.

Results

Four themes related to symptom management during pregnancy were identified: 1) cannabis use and impacts of use for a variety of mental health symptoms, including depression and anxiety; 2) cannabis use for physical health symptoms and conditions, such as nausea and pain; 3) use of cannabis to achieve homeostasis and manage stress; 4) decision-making about using cannabis for symptom management, such as using cannabis instead of prescription medications. Most discussions in this pro-cannabis forum reflected perceptions that cannabis was effective in treating the conditions for which it was used; however, limitations of cannabis' efficacy were also mentioned.

Discussion

There is need for reduced stigma and open communication between pregnant persons who use cannabis and their providers in discussing how to manage their mental and physical health symptoms. Understanding the various symptoms for which individuals use cannabis during pregnancy to self-treat can inform these conversations and the expansion of harm reduction strategies.

"Instead, You're Going to a Friend": Evaluation of a Community-Developed, Peer-Delivered Online Crisis Prevention Intervention

OBJECTIVE

Online communities promote social connection and can be used for formal peer support and crisis intervention. Although some communities have programs to support their members' mental health, few programs have been formally evaluated. The authors present findings from a mixed-methods evaluation of the Stack Up Overwatch Program (StOP), a digital peer support intervention delivered in an online gaming community.

METHODS

Data were collected from members of the Stack Up Discord server between June and October 2020 and included chat messages, survey responses, encounter forms (documenting information from private interactions between users and peer supporters), and interviews with peer support team members. The authors analyzed data on demographic characteristics, mental health and crises, use of and experiences with StOP, and chat posts. Thematic analysis and descriptive statistics were combined in a joint display table, with mixed-methods findings explained in narrative form.

RESULTS

The findings show that StOP provides users in crisis with a source of mental health support when other options have been exhausted and that military and veteran users valued the connections and friendships they formed while using it. Participants reported that StOP met needs for support and connection when formal services were inaccessible or did not meet their needs, and volunteer peer supporters detailed how StOP's design facilitates use of the intervention. Volunteering offered members of the peer support team a "family feeling" facilitated by the unique chat room structure.

CONCLUSIONS

Community-based crisis prevention programs administered through chat rooms may provide valuable support to both users and peer support providers.

The Mediating Role of Students' Health Information Literacy Skills: Exploring the Relationship Between Web Resource Utilization and Health Information Evaluation Proficiency

BACKGROUND

In an increasingly digital age, students rely heavily on web resources to access health information. However, evaluating the reliability and relevance of such information is crucial for informed decision-making. This study examines the importance of students' health information literacy skills (HILS) as mediators, particularly their ability to utilize web resources and successfully evaluate health information.

OBJECTIVES

This research investigates the mediating role of students' HILS in the relationship between their utilization of web resources and their proficiency in evaluating health information.

METHOD

An online survey was distributed to current students at a public university in Bangladesh as part of the data collection process for this study. Using Google Forms, the authors created a structured questionnaire. The survey was distributed through Messenger groups, Facebook pages and email invitations to reach the target audience effectively. The researchers thoroughly analysed the gathered data using structural equation modelling (SEM) techniques and SmartPLS-4 software to look for correlations between the variables.

RESULT

The study revealed that among the 122 participants, a significant number (N = 47) accessed internet health information on an occasional basis, whereas 30 individuals reported using it infrequently. The data revealed that 58 individuals, accounting for 47.5% of the sample, possessed the necessary abilities to access and assess online health information. Additionally, 57 participants, representing 46.7% of the sample, demonstrated proficiency in conducting online health information searches. The measurement model demonstrated good convergent validity, as evidenced by composite reliability (CR) scores and Cronbach's ⍺ values over 0.700 and an average extracted variance (AVE) of 0.500. The structural model demonstrated R2 values exceeding 0.1, thus validating its dependable forecasting capability. Notable effects were seen, with f2 values of 0.335 and 0.317 for the challenges in accessing and evaluating health information (CAEHI) to health information evaluation (HIE) and CAEHI to HILS relationships, respectively. The mediation analysis found that HILS act as a mediator between types of web resources (TWRs) and HIE, with TWR having an indirect impact on HIE through HILS.

DISCUSSION

The result supports all hypotheses. Therefore, it is evident that students' HILS mediate the relationship between utilization of web resources and their proficiency in evaluating health information.

CONCLUSION

This study's findings could significantly impact instructional practices meant to raise students' health information literacy. This initiative seeks to enable students to make informed decisions about their health by providing them with the necessary tools to analyse and evaluate health-related information.

PATIENT OR PUBLIC CONTRIBUTION

Research on health information literacy can assist patients and the general public by instructing them on how to assess trustworthy online health resources. Students gave insightful feedback that assisted in shaping the study and guaranteeing its relevancy. If they better comprehend health information literacy, patients and the general public can use web-based resources and critically evaluate health information more accurately.

Social Acceptability of Health Behavior Posts on Social Media: An Experiment

INTRODUCTION

Social media sites like Twitter (now X) are increasingly used to create health behavior metrics for public health surveillance. Yet little is known about social norms that may bias the content of posts about health behaviors. Social norms for posts about four health behaviors (smoking tobacco, drinking alcohol, physical activity, eating food) on Twitter/X were evaluated.

METHODS

This was a randomized experiment delivered via web-based survey to adult, English-speaking Twitter/X users in three Michigan, USA, counties from 2020 to 2022 (n=559). Each participant viewed 24 posts presenting experimental manipulations regarding four health behaviors and answered questions about each post's social acceptability. Principal component analysis was used to combine survey responses into one perceived social acceptability measure. Linear mixed models with the Benjamini-Hochberg correction were implemented to test seven study hypotheses in 2023.

RESULTS

Supporting six hypotheses, posts presenting healthier (CI: 0.028, 0.454), less stigmatized behaviors (CI: 0.552, 0.157) were more socially acceptable than posts regarding unhealthier, stigmatized behaviors. Unhealthy (CI: -0.268, -0.109) and stigmatized behavior (CI: -0.261, -0.103) posts were less acceptable for more educated participants. Posts about collocated activities (CI: 0.410, 0.573) and accompanied by expressions of liking (CI: 0.906, 1.11) were more acceptable than activities undertaken alone or disliked. Contrary to one hypothesis, posts reporting unusual activities were less acceptable than usual ones (CI: -0.472, 0.312).

CONCLUSIONS

Perceived social acceptability may be associated with the frequency and content of health behavior posts. Users of Twitter/X and other social media platform posts to estimate health behavior prevalence should account for potential estimation biases from perceived social acceptability of posts.

A Descriptive Analysis of a Popular Pregnancy Forum: Comments on the Developmental Consequences of Cannabis Use on Offspring

OBJECTIVE

Pregnant and postpartum people want more and higher quality information about the effects of perinatal cannabis use (PCU) on child health, and they turn to anonymous sources of information, such as online pregnancy forums, to make decisions about its use. This study characterized perceptions of the developmental impact of PCU on children via a narrative evaluation of a public forum on which people discuss a range of issues around cannabis use.

METHOD

A random sample of 10 threads per month from June 2020 to May 2021 were scraped from the "Ganja Mamas" forum on Whattoexpect.com. Posts were analyzed if they discussed use of cannabis during pregnancy or lactation and children. A qualitative coding structure was developed from a literature review on PCU and was refined for inclusion of emergent topics. Posts were evaluated by two coders using applied thematic analysis and were assessed using an open coding process to identify key topics. Associated codes were grouped into themes.

RESULTS

Posters (a) discussed the negative and positive impact of PCU on child physical, cognitive, and socioemotional development; (b) garnered information about PCU from sources other than medical providers; and (c) discussed harm-reduction approaches to reduce impacts of PCU on child health.

CONCLUSIONS

There is a need for stigma-free support around PCU decision-making for people who select into discussion forums designed for communication and support around parental cannabis use. This forum presents a fruitful opportunity for intervention to encourage health-promoting behaviors through the provision of evidence-based information.

Using Twitter to Understand COVID-19 Vaccine-Related Ageism During the Pandemic

BACKGROUND AND OBJECTIVES

During the rollout of coronavirus 2019 (COVID-19) vaccines, older adults in high-income countries were often prioritized for inoculation in efforts to reduce COVID-19-related mortality. However, this prioritization may have contributed to intergenerational tensions and ageism, particularly with the limited supply of COVID-19 vaccines. This study examines Twitter discourse to understand vaccine-related ageism during the COVID-19 pandemic to inform future vaccination policies and practices to reduce ageism.

RESEARCH DESIGN AND METHODS

We collected 1,369 relevant tweets on Twitter using the Twint application in Python from December 8, 2020, to December 31, 2021. Tweets were analyzed using thematic analysis, and steps were taken to ensure rigor.

RESULTS

Our research identified four main themes including (a) blame and hostility: "It's all their fault"; (b) incompetence and misinformation: "clueless boomer"; (c) ageist political slander; and (d) combatting ageism: advocacy and accessibility.

DISCUSSION AND IMPLICATIONS

Our findings exposed issues of victim-blaming, hate speech, pejorative content, and ageist political slander that is deepening the divide of intergenerational conflict. Although a subset of tweets countered negative outcomes and demonstrated intergenerational solidarity, our findings suggest that ageism may have contributed to COVID-19 vaccine hesitancy among older adults. Consequently, urgent action is needed to counter vaccine misinformation, prohibit aggressive messaging, and promote intergenerational unity during the COVID-19 pandemic and beyond.

Deciphering Influence on Social Media: A Comparative Analysis of Influential Account Detection Metrics in the Context of Tobacco Promotion

Influencer marketing spending in the United States was expected to surpass $6 billion in 2023. This marketing tactic poses a public health threat, as research suggests it has been utilized to undercut decades of public health progress-such as gains made against tobacco use among adolescents. Public health and public opinion researchers need practical tools to capture influential accounts on social media. Utilizing X (formerly Twitter) little cigar and cigarillo (LCC) data, we compared seven influential account detection metrics to help clarify our understanding of the functions of existing metrics and the nature of social media discussion of tobacco products. Results indicate that existing influential account detection metrics are non-harmonic and time-sensitive, capturing distinctly different users and categorically different user types. Our results also reveal that these metrics capture distinctly different conversations among influential social media accounts. Our findings suggest that public health and public opinion researchers hoping to conduct analyses of influential social media accounts need to understand each metric's benefits and limitations and utilize more than one influential account detection metric to increase the likelihood of producing valid and reliable research.

Exploring Perceptions About Paracetamol, Tramadol, and Codeine on Twitter Using Machine Learning: Quantitative and Qualitative Observational Study

BACKGROUND

Paracetamol, codeine, and tramadol are commonly used to manage mild pain, and their availability without prescription or medical consultation raises concerns about potential opioid addiction.

OBJECTIVE

This study aims to explore the perceptions and experiences of Twitter users concerning these drugs.

METHODS

We analyzed the tweets in English or Spanish mentioning paracetamol, tramadol, or codeine posted between January 2019 and December 2020. Out of 152,056 tweets collected, 49,462 were excluded. The content was categorized using a codebook, distinguishing user types (patients, health care professionals, and institutions), and classifying medical content based on efficacy and adverse effects. Scientific accuracy and nonmedical content themes (commercial, economic, solidarity, and trivialization) were also assessed. A total of 1000 tweets for each drug were manually classified to train, test, and validate machine learning classifiers.

RESULTS

Of classifiable tweets, 42,840 mentioned paracetamol and 42,131 mentioned weak opioids (tramadol or codeine). Patients accounted for 73.10% (60,771/83,129) of the tweets, while health care professionals and institutions received the highest like-tweet and tweet-retweet ratios. Medical content distribution significantly differed for each drug (P<.001). Nonmedical content dominated opioid tweets (23,871/32,307, 73.9%), while paracetamol tweets had a higher prevalence of medical content (33,943/50,822, 66.8%). Among medical content tweets, 80.8% (41,080/50,822) mentioned drug efficacy, with only 6.9% (3501/50,822) describing good or sufficient efficacy. Nonmedical content distribution also varied significantly among the different drugs (P<.001).

CONCLUSIONS

Patients seeking relief from pain are highly interested in the effectiveness of drugs rather than potential side effects. Alarming trends include a significant number of tweets trivializing drug use and recreational purposes, along with a lack of awareness regarding side effects. Monitoring conversations related to analgesics on social media is essential due to common illegal web-based sales and purchases without prescriptions.

Characterizing Twitter chatter about temporary alcohol abstinence during "Dry January"

With roots as a public health campaign in the United Kingdom, "Dry January" is a temporary alcohol abstinence initiative encouraging participants to abstain from alcohol use during the month of January. Dry January has become a cultural phenomenon, gaining increasing news media attention and social media engagement. Given the utility of capturing naturalistic discussions around health topics on social media, we examined Twitter chatter about Dry January and associated temporary abstinence experiences. Public tweets were collected containing the search terms "dry january" or "dryjanuary" posted between 15 December and 15 February across 3 years (2020-2). A random subsample stratified by year (n = 3145) was pulled for manual content analysis by trained coders. Final codebook accounted for user sentiment toward Dry January, user account type, and themes related to Dry January participation. Engagement metadata (e.g. likes) were also collected. Though user sentiment was mixed, most tweets expressed positive or neutral sentiment toward Dry January (74.7%). Common themes included encouragement and support for Dry January participation (14.1%), experimentation with and promotion of nonalcoholic drinks (14.0%), and benefits derived from Dry January participation (10.4%). While there is promise in the movement to promote positive alcohol-related behavior change, increased efforts to deliver the campaign within a public health context are needed. Health communication campaigns designed to inform participants about evidence-based treatment and recovery support services proven to help people quit or cut down on their drinking are likely to maximize benefits.

Miscommunication about the US federal Tobacco 21 law: a content analysis of Twitter discussions

OBJECTIVE

Tobacco 21 is a law that sets the minimum legal sales age of tobacco products to 21. On 20 December 2019, the USA passed a federal Tobacco 21 law. The objective of this study is to explore Twitter discussions about the federal Tobacco 21 law in the USA leading up to enacted.

METHODS

Twitter messages about Tobacco 21 posted between September and December 2019 were collected via RITHM software. A 2% sample of all collected tweets were double coded by independent coders using a content analysis approach.

RESULTS

Findings included three content categories of tweets (news, youth and young adults and methods of avoiding the law) with eight subcodes. Most news tweets incorrectly described the law as a purchase law (54.7%). However, Tobacco 21 is in fact a sales law-it only includes penalties for tobacco retailers who sell to under-age purchasers. About one-fourth (27%) of the tweets involved youth and young adults, with some claiming the law would reduce youth smoking and others doubting its ability to limit youth access to tobacco products. Few tweets (2.5%) mentioned methods of circumventing the policy, such as having an older peer purchase tobacco.

CONCLUSIONS

As several countries explore raising their age of sale of tobacco laws to 21, they should couple policy enactment with clear and accurate communication about the law. Compliance agencies at all levels (eg, local, regional, national) can use social media to identify policy loopholes and support vulnerable populations throughout the policy implementation process.

Twitter Sentiment About the US Federal Tobacco 21 Law: Mixed Methods Analysis

BACKGROUND

On December 20, 2019, the US "Tobacco 21" law raised the minimum legal sales age of tobacco products to 21 years. Initial research suggests that misinformation about Tobacco 21 circulated via news sources on Twitter and that sentiment about the law was associated with particular types of tobacco products and included discussions about other age-related behaviors. However, underlying themes about this sentiment as well as temporal trends leading up to enactment of the law have not been explored.

OBJECTIVE

This study sought to examine (1) sentiment (pro-, anti-, and neutral policy) about Tobacco 21 on Twitter and (2) volume patterns (number of tweets) of Twitter discussions leading up to the enactment of the federal law.

METHODS

We collected tweets related to Tobacco 21 posted between September 4, 2019, and December 31, 2019. A 2% subsample of tweets (4628/231,447) was annotated by 2 experienced, trained coders for policy-related information and sentiment. To do this, a codebook was developed using an inductive procedure that outlined the operational definitions and examples for the human coders to annotate sentiment (pro-, anti-, and neutral policy). Following the annotation of the data, the researchers used a thematic analysis to determine emergent themes per sentiment category. The data were then annotated again to capture frequencies of emergent themes. Concurrently, we examined trends in the volume of Tobacco 21-related tweets (weekly rhythms and total number of tweets over the time data were collected) and analyzed the qualitative discussions occurring at those peak times.

RESULTS

The most prevalent category of tweets related to Tobacco 21 was neutral policy (514/1113, 46.2%), followed by antipolicy (432/1113, 38.8%); 167 of 1113 (15%) were propolicy or supportive of the law. Key themes identified among neutral tweets were news reports and discussion of political figures, parties, or government involvement in general. Most discussions were generated from news sources and surfaced in the final days before enactment. Tweets opposing Tobacco 21 mentioned that the law was unfair to young audiences who were addicted to nicotine and were skeptical of the law's efficacy and importance. Methods used to evade the law were found to be represented in both neutral and antipolicy tweets. Propolicy tweets focused on the protection of youth and described the law as a sensible regulatory approach rather than a complete ban of all products or flavored products. Four spikes in daily volume were noted, 2 of which corresponded with political speeches and 2 with the preparation and passage of the legislation.

CONCLUSIONS

Understanding themes of public sentiment-as well as when Twitter activity is most active-will help public health professionals to optimize health promotion activities to increase community readiness and respond to enforcement needs including education for retailers and the general public.

"That's Pot Culture Right There": Purchasing Behaviors of People Who Use Cannabis Without a Medical Cannabis Card

Introduction

The legal landscape surrounding purchasing cannabis without a medical cannabis card (i.e., without MCC) is changing rapidly, affecting consumer access and purchasing behaviors. Cannabis purchasing behaviors are related to subsequent use and experiencing greater cannabis-related negative consequences. However, purchasing behaviors of individuals who use cannabis without MCC are understudied.

Methods

The current study analyzed qualitative data from focus groups with adults who use cannabis without MCC (n = 5 groups; 6-7 participants/group; n = 31 total participants). Focus groups followed a semi-structured agenda, and were audio recorded and transcribed. Two coders applied thematic analysis to summarize topics pertaining to cannabis purchasing attitudes and behaviors. Focus groups occurred in 2015 and 2016 in Rhode Island, when purchasing and use of cannabis without MCC was decriminalized but still considered illegal.

Results

On average, participants (72% male) were 26 years old (SD = 7.2) and reported using cannabis 5 days per week (SD = 2.1). Thematic analysis revealed three key themes related to cannabis purchasing behaviors: (1) regular purchasing routines (i.e., frequency, schedule, amount of purchases), (2) economic factors (i.e., financial circumstances), and (3) contextual factors (i.e., quality of cannabis, convenience/availability) were perceived to influence purchasing decisions. Dealers' recommendations affected participants' purchases, who also reported minimal legal concerns. Participants reported saving money and using more cannabis when buying in bulk.

Discussion

Purchasing behaviors were found to vary and were perceived to be affected by individual-level (e.g., routines) and contextual factors (e.g., availability) that, in turn, may impact use patterns. Future research should consider how factors (e.g., availability) that differ across contexts (e.g., location) and demographic groups interact to affect purchasing behaviors.

Viewer Reactions to EVALI Storylines on Popular Medical Dramas: A Thematic Analysis of Twitter Messages

Previous research has found an association between awareness of e-cigarette, or vaping, product-use associated lung injury (EVALI) and lower intention to use e-cigarettes among young people. This study utilized Twitter data to evaluate if the January 2020 depiction of EVALI on New Amsterdam, Chicago Med, and Grey's Anatomy-three popular primetime medical dramas-could be a potential innovative avenue to raise awareness of EVALI. We obtained tweets containing e-cigarette-related search strings from 1/21/2020 to 02/18/2020 and filtered these with storyline-specific keywords, resulting in 1,493 tweets for qualitative coding by two trained human coders. Content codes were informed by prior research, theories of narrative influence, and e-cigarette related outcomes. Of 641 (42.9%) relevant tweets, the most frequent content codes were perceived realism (n = 292, 45.6%) and negative response (n = 264, 41.2%). A common theme among these tweets was that storylines were unrealistic because none of the characters with EVALI used THC-containing products. Approximately 12% of tweets (n = 78) mentioned e-cigarette knowledge and 28 (4.4%) mentioned behavior, including quitting e-cigarettes because of viewing the storylines. Implications for health communication research utilizing social media data and maximizing the achievement of positive health-related outcomes for storylines depicting current health topics are discussed.

Toward a more expansive and inclusive definition of women's health: A content analysis of Twitter conversations

To develop a nuanced understanding of women's health on social media, we conducted a content analysis of Twitter data in early 2020, during the early days of the COVID-19 pandemic. Included tweets (N = 1,714) fell into 15 overarching themes. "Politics and Women's Health" was most discussed, demonstrating the politicization of women's health, followed by "Maternal, Reproductive, and Sexual Health." COVID-19 was a crosscutting issue for 12 themes, suggesting widespread effects on women's health. Overall, diverse conversations unfolded on social media, including variation geographically, highlighting the need for a more expansive and inclusive definition of women's health. This work supports further investigation into the role of politics and COVID-19 across women's health domains.

Analyzing Twitter Chatter About Tobacco Use Within Intoxication-related Contexts of Alcohol Use: "Can Someone Tell Me Why Nicotine is So Fire When You're Drunk?"

INTRODUCTION

Alcohol and tobacco are commonly used together. Social influences within online social networking platforms contribute to youth and young adult substance use behaviors. This study used a sample of alcohol- and tobacco-related tweets to evaluate: (1) sentiment toward co-use of alcohol and tobacco, (2) increased susceptibility to tobacco use when consuming alcohol, and (3) the role of alcohol in contributing to a failed attempt to quit tobacco use.

METHODS

Data were collected from the Twitter API from January 1, 2019 through December 31, 2019 using tobacco-related keywords (e.g., vape, ecig, smoking, juul*) and alcohol-related filters (e.g., drunk, blackout*). A total of 78,235 tweets were collected, from which a random subsample (n = 1,564) was drawn for coding. Cohen's Kappa values ranged from 0.66 to 0.99.

RESULTS

Most tweets were pro co-use of alcohol and tobacco (75%). One of every ten tweets reported increased susceptibility to tobacco use when intoxicated. Non-regular tobacco users reported cravings for and tobacco use when consuming alcohol despite disliking tobacco use factors such as the taste, smell, and/or negative health effects. Regular tobacco users reported using markedly higher quantities of tobacco when intoxicated. Individuals discussed the role of alcohol undermining tobacco cessation attempts less often (2.0%), though some who had quit smoking for prolonged periods of time reported reinitiating tobacco use during acute intoxication episodes.

CONCLUSIONS

Tobacco cessation interventions may benefit from including alcohol-focused components designed to educate participants about the association between increased susceptibility to tobacco use when consuming alcohol and the role of alcohol in undermining tobacco cessation attempts.

IMPLICATIONS

Sentiment toward co-use of alcohol and tobacco on Twitter is largely positive. Individuals reported regret about using tobacco, or using more than intended, when intoxicated. Those who had quit smoking or vaping for prolonged periods of time reported reinitiating tobacco use when consuming alcohol. While social media-based tobacco cessation interventions like the Truth Initiative's "Ditch the Juul" campaign demonstrate potential to change tobacco use behaviors, these campaigns may benefit from including alcohol-focused components designed to educate participants about the association between increased susceptibility to tobacco use when consuming alcohol and the role of alcohol in undermining tobacco cessation attempts.

Exploring Factors That Predict Marketing of e-Cigarette Products on Twitter: Infodemiology Approach Using Time Series

Background

Electronic nicotine delivery systems (known as electronic cigarettes or e-cigarettes) increase risk for adverse health outcomes among naïve tobacco users, particularly youth and young adults. This vulnerable population is also at risk for exposed brand marketing and advertisement of e-cigarettes on social media. Understanding predictors of how e-cigarette manufacturers conduct social media advertising and marketing could benefit public health approaches to addressing e-cigarette use.

Objective

This study documents factors that predict changes in daily frequency of commercial tweets about e-cigarettes using time series modeling techniques.

Methods

We analyzed data on the daily frequency of commercial tweets about e-cigarettes collected between January 1, 2017, and December 31, 2020. We fit the data to an autoregressive integrated moving average (ARIMA) model and unobserved components model (UCM). Four measures assessed model prediction accuracy. Predictors in the UCM include days with events related to the US Food and Drug Administration (FDA), non-FDA-related events with significant importance such as academic or news announcements, weekday versus weekend, and the period when JUUL maintained an active Twitter account (ie, actively tweeting from their corporate Twitter account) versus when JUUL stopped tweeting.

Results

When the 2 statistical models were fit to the data, the results indicate that the UCM was the best modeling technique for our data. All 4 predictors included in the UCM were significant predictors of the daily frequency of commercial tweets about e-cigarettes. On average, brand advertisement and marketing of e-cigarettes on Twitter was higher by more than 150 advertisements on days with FDA-related events compared to days without FDA events. Similarly, more than 40 commercial tweets about e-cigarettes were, on average, recorded on days with important non-FDA events compared to days without such events. We also found that there were more commercial tweets about e-cigarettes on weekdays than on weekends and more commercial tweets when JUUL maintained an active Twitter account.

Conclusions

e-Cigarette companies promote their products on Twitter. Commercial tweets were significantly more likely to be posted on days with important FDA announcements, which may alter the narrative about information shared by the FDA. There remains a need for regulation of digital marketing of e-cigarette products in the United States.

Consumer perceptions of telehealth for mental health or substance abuse: a Twitter-based topic modeling analysis

Objective

The objective of this study is to understand the primary topics of consumer discussion on Twitter associated with telehealth for mental health or substance abuse for prepandemic versus during-pandemic time-periods, using a state-of-the-art machine learning (ML) natural language processing (NLP) method.

Materials and Methods

The primary methodological phases of this project were: (1) collecting, cleaning, and filtering data (tweets) from January 2014 to June 2021, (2) describing the final corpus, (3) running and optimizing Bidirectional Encoder Representations from Transformers (BERT; using BERTopic in Python) models, and (4) human refinement of topic model results and thematic classification of topics.

Results

The number of tweets in this context increased by 4 times during the pandemic (2017 tweets prepandemic vs 8672 tweets during the pandemic). During the pandemic topics were more frequently mental health related than substance abuse related. Top during-pandemic topics were therapy, suicide, pain (associated with burnout and drinking), and mental health diagnoses such as ADHD and autism. Anxiety was a key topic of discussion both pre- and during the pandemic.

Discussion

Telehealth for mental health and substance abuse is being discussed more frequently online, which implies growing demand. Given the topics extracted as proxies for demand, the most demand is currently for telehealth for mental health primarily, especially for children, parents, and therapy for those with anxiety or depression, and substance abuse secondarily.

Conclusions

Scarce telehealth resources can be allocated more efficiently if topics of consumer discussion are included in resource allocation decision- and policy-making processes.

Telehealth for mental health and substance abuse is being discussed more frequently online. To determine what aspects of telehealth for mental health and/or substance abuse were being discussed most on Twitter, both before the pandemic and during the pandemic, we downloaded relevant tweets and ran a specialized machine learning model that extracts the most popular keywords from tweets as well as combines similar keywords into overall topics. We find 33 relevant topics prepandemic and 32 relevant topics during the pandemic to be relevant in this context. Given the topics extracted as proxies for demand, the most demand is currently for telehealth for mental health primarily, especially for children, parents, and therapy for those with anxiety or depression, and substance abuse secondarily. We also find that therapy and therapists were the top areas of discussion in regard to telehealth for mental health and/or substance abuse during the pandemic. These results can be applied to telehealth decision-making processes. In particular, scarce telehealth resources can be allocated more efficiently, particularly to those who currently need or want them most, if topics of consumer discussion are included in resource allocation decision- and policy-making processes.

Discussions and Misinformation About Electronic Nicotine Delivery Systems and COVID-19: Qualitative Analysis of Twitter Content

Background

Misinformation and conspiracy theories related to COVID-19 and electronic nicotine delivery systems (ENDS) are increasing. Some of this may stem from early reports suggesting a lower risk of severe COVID-19 in nicotine users. Additionally, a common conspiracy is that the e-cigarette or vaping product use–associated lung injury (EVALI) outbreak of 2019 was actually an early presentation of COVID-19. This may have important public health ramifications for both COVID-19 control and ENDS use.

Objective

Twitter is an ideal tool for analyzing real-time public discussions related to both ENDS and COVID-19. This study seeks to collect and classify Twitter messages (“tweets”) related to ENDS and COVID-19 to inform public health messaging.

Methods

Approximately 2.1 million tweets matching ENDS-related keywords were collected from March 1, 2020, through June 30, 2020, and were then filtered for COVID-19–related keywords, resulting in 67,321 original tweets. A 5% (n=3366) subsample was obtained for human coding using a systematically developed codebook. Tweets were coded for relevance to the topic and four overarching categories.

Results

A total of 1930 (57.3%) tweets were coded as relevant to the research topic. Half (n=1008, 52.2%) of these discussed a perceived association between ENDS use and COVID-19 susceptibility or severity, with 42.4% (n=818) suggesting that ENDS use is associated with worse COVID-19 symptoms. One-quarter (n=479, 24.8%) of tweets discussed the perceived similarity/dissimilarity of COVID-19 and EVALI, and 13.8% (n=266) discussed ENDS use behavior. Misinformation and conspiracy theories were present throughout all coding categories.

Conclusions

Discussions about ENDS use and COVID-19 on Twitter frequently highlight concerns about the susceptibility and severity of COVID-19 for ENDS users; however, many contain misinformation and conspiracy theories. Public health messaging should capitalize on these concerns and amplify accurate Twitter messaging.

Puff Bars, Tobacco Policy Evasion, and Nicotine Dependence: Content Analysis of Tweets

BACKGROUND

Puff Bars are e-cigarettes that continued marketing flavored products by exploiting the US Food and Drug Administration exemption for disposable devices.

OBJECTIVE

This study aimed to examine discussions related to Puff Bar on Twitter to identify tobacco regulation and policy themes as well as unanticipated outcomes of regulatory loopholes.

METHODS

Of 8519 original tweets related to Puff Bar collected from July 13, 2020, to August 13, 2020, a random 20% subsample (n=2661) was selected for qualitative coding of topics related to nicotine dependence and tobacco policy.

RESULTS

Of the human-coded tweets, 2123 (80.2%) were coded as relevant to Puff Bar as the main topic. Of those tweets, 698 (32.9%) discussed tobacco policy, including flavors (n=320, 45.9%), regulations (n=124, 17.8%), purchases (n=117, 16.8%), and other products (n=110, 15.8%). Approximately 22% (n=480) of the tweets referenced dependence, including lack of access (n=273, 56.9%), appetite suppression (n=59, 12.3%), frequent use (n=47, 9.8%), and self-reported dependence (n=110, 22.9%).

CONCLUSIONS

This study adds to the growing evidence base that the US Food and Drug Administration ban of e-cigarette flavors did not reduce interest, but rather shifted the discussion to brands utilizing a loophole that allowed flavored products to continue to be sold in disposable devices. Until comprehensive tobacco policy legislation is developed, new products or loopholes will continue to supply nicotine demand.

Policy and Behavior: Comparisons between Twitter Discussions about the US Tobacco 21 Law and Other Age-Related Behaviors

To combat the e-cigarette epidemic among young audiences, a federal law was passed in the US that raised the minimum legal sales age of tobacco to 21 years (commonly known as Tobacco 21). Little is known about sentiment toward this law. Thus, the purpose of our study was to systematically explore trends about Tobacco 21 discussions and comparisons to other age-restriction behaviors on Twitter. Twitter data (n = 4628) were collected from September to December of 2019 that were related to Tobacco 21. A random subsample of identified tweets was used to develop a codebook. Two trained coders independently coded all data, with strong inter-rater reliability (κ = 0.71 to 0.93) found for all content categories. Associations between sentiment and content categories were calculated using χ2 analyses. Among relevant tweets (n = 955), the most common theme—the disjunction between ages for military enlistment and tobacco use—was found in 17.8% of all tweets. Anti-policy sentiment was strongly associated with the age of military enlistment, alcohol, voting, and adulthood (p < 0.001 for all). Opposition to Tobacco 21 propagates on social media because the US federal law does not exempt military members. However, the e-cigarette epidemic may have fueled some support for this law.

Assessing open science and citizen science in addictions and substance use research: A scoping review

BACKGROUND

The EU promotes 'Open Science' as a public good. Complementary to its implementation is Citizen Science, which redefines the relationship between the scientific community, civic society and the individual. Open Science and Citizen Science poses challenges for the substance use and addictions research community but may provide positive opportunities for future European addiction research. This paper explores both current barriers and potential facilitators for the implementation of Open Science and Citizen Science in substance use and addictions research.

METHODOLOGY

A scoping review was used to examine barriers and facilitators identified in the substance use and addiction research literature for the adoption of Open Science and Citizen Science.

RESULTS

'Technical' facilitators included the pre-registration of study protocols; publication of open-source datasets; open peer review and online tools. 'Motivational' facilitators included enhanced reputation; embracing co-creation; engaged citizenship and gamification. 'Economic' facilitators included the use of free tools and balanced remuneration of crowdworkers. 'Political' facilitators included better informed debates through the 'triple helix' approach and trust-generating transparency. 'Legal' facilitators included epidemiologically informed law enforcement; better policy surveillance and the validation of other datasets. 'Ethical' facilitators included the 'democratisation of science' and opportunities to explore new concepts of ethics in addiction research.

CONCLUSION

Open Science and Citizen Science in substance use and addictions research may provide a range of benefits in relation to the democratisation of science; transparency; efficiency and the reliability/validity of data. However, its implementation raises a range of research integrity and ethical issues that need be considered. These include issues related to participant recruitment; privacy; confidentiality; security; cost and industry involvement. Progressive journal policies to support Open Science practices; a shift in researcher norms; the use of free tools and the greater availability of methodological and ethical standards are likely to increase adoption in the field.

Mediterranean Diet Social Network Impact along 11 Years in the Major US Media Outlets: Thematic and Quantitative Analysis Using Twitter

Background: Media outlets influence social attitudes toward health. Thus, it is important that they share contents which promote healthy habits. The Mediterranean diet (MedDiet) is associated with lower cardiovascular disease risk. Analysis of tweets has become a tool for understanding perceptions on health issues. Methods: We investigated tweets posted between January 2009 and December 2019 by 25 major US media outlets about MedDiet and its components as well as the retweets and likes generated. In addition, we measured the sentiment analysis of these tweets and their dissemination. Results: In total, 1608 tweets, 123,363 likes and 48,946 retweets about MedDiet or its components were analyzed. Dairy (inversely weighted in MedDiet scores) accounted for 45.0% of the tweets (723/1608), followed by nuts 19.7% (317/1608). MedDiet, as an overall dietary pattern, generated only 9.8% (157/1608) of the total tweets, while olive oil generated the least number of tweets. Twitter users’ response was quantitatively related to the number of tweets posted by these US media outlets, except for tweets on olive oil and MedDiet. None of the MedDiet components analyzed was more likely to be liked or retweeted than the MedDiet itself. Conclusions: The US media outlets analyzed showed reduced interest in MedDiet as a whole, while Twitter users showed greater interest in the overall dietary pattern than in its particular components.

E-Cigarette-Related Nicotine Misinformation on Social Media

Background. Twitter provides an opportunity to examine misperceptions about nicotine and addiction as they pertain to electronic nicotine delivery systems (ENDS). The purpose of this study was to systematically examine a sample of ENDS-related tweets that presented information about nicotine or addiction for the presence of potential misinformation.Methods. A total of 10.1 million ENDS-related tweets were obtained from April 2018 through March 2019 and were filtered for unique tweets containing keywords for nicotine and addiction. A subsample (n = 3,116) were human coded for type of account (individual, group, commercial, or news) and presence of potential misinformation.Results. Of tweets that presented ENDS-related nicotine or addiction information (n = 904), 41.7% (n = 377) contained potential misinformation coded as anti-vaping exaggeration, pro-vaping exaggeration, nicotine is not addictive or is never harmful, or unproven health benefits.Conclusions. Anti-vaping exaggeration tweets distorted or embellished claims about ENDS nicotine and addiction; pro-vaping exaggeration tweets misinterpreted results from scientific studies. Misinformation that nicotine is not addictive or is never harmful or has unproven health benefits appeared less but are potentially problematic. ENDS-related messaging should be designed to be easily understood by the public and monitored to detect the spread of misinterpretation or misinformation on social media.

A Content Analysis of Reddit Users' Perspectives on Reasons for Not Following Through with a Suicide Attempt

Despite a growing understanding of the triggers for suicidal thoughts and behavior, little is known about the mechanisms that prevent people from killing themselves. The goal of the present study was to use publicly available Reddit data to better understand the reasons that people give for not following through with a potentially lethal suicide attempt. Threads containing key terms (e.g., "kill yourself") within the subreddit /r/AskReddit were collected and all top posts from these threads were thematically coded. Across the posts collected, 11 different themes were identified; friends and family, curiosity and optimism about the future, spite, purpose, transience, hobbies, animals/pets, fear of survival, fear of pain, death and/or the afterlife, apathy/laziness, and intervention. Some additional themes were captured in an "other" category, and a twelfth theme, use of pharmaceutical drugs, was identified, but not discussed. These findings provide a broad overview about the proximal protective factors that directly stopped people from making a suicide attempt. They also illustrate the potential for Reddit as platform through which to better understand factors that may help to identify and support those in suicidal crisis. Such insight may help to inform intervention and prevention strategies for suicide and those in suicidal crisis.

Classification of Twitter Vaping Discourse Using BERTweet: Comparative Deep Learning Study (Preprint)

Background

Objective

Methods

Results

Conclusions

Are bottle shops using Twitter to increase advertising or encourage drinking during COVID-19?

OBJECTIVE

Preliminary reports suggested that liquor retailers used COVID-19 to promote alcohol through sponsored posts on Facebook and Instagram. To further understand the advertising practices during this period, we aimed to determine whether packaged liquor retailers increased their posts during COVID-19 or used COVID-19 to promote alcohol on Twitter.

METHODS

'Tweets' (Twitter posts) from all packaged liquor retailers in NSW written since 2018 were collected. Tweets written during the first COVID-19 lockdown period were coded for: references of COVID-19, types of marketing message, use of links to online stores and use of an alcohol-related 'meme'.

RESULTS

There was no evidence of increased tweet frequency, however, some COVID-specific alcohol advertising was detected that leveraged the pandemic (4.0%) or referencing the pandemic without explicitly promoting alcohol (12.0%). The most popular market messages used in the tweets were encouraging alcohol use (15.4%) and easy access to alcohol at home (9.5%).

CONCLUSIONS

At least on Twitter, there was no marked increase in posts from packaged liquor retailers in NSW and only some tweets used COVID-19 to promote alcohol. Implications for public health: The use of COVID-specific alcohol marketing on social media raises important considerations for legislative and regulatory requirements, particularly during major health events such as a pandemic.

Areas of Interest and Attitudes towards the Pharmacological Treatment of Attention Deficit Hyperactivity Disorder: Thematic and Quantitative Analysis Using Twitter

We focused on tweets containing hashtags related to ADHD pharmacotherapy between 20 September and 31 October 2019. Tweets were classified as to whether they described medical issues or not. Tweets with medical content were classified according to the topic they referred to: side effects, efficacy, or adherence. Furthermore, we classified any links included within a tweet as either scientific or non-scientific. We created a dataset of 6568 tweets: 4949 (75.4%) related to stimulants, 605 (9.2%) to non-stimulants and 1014 (15.4%) to alpha-2 agonists. Next, we manually analyzed 1810 tweets. In the end, 481 (48%) of the tweets in the stimulant group, 218 (71.9%) in the non-stimulant group and 162 (31.9%) in the alpha agonist group were considered classifiable. Stimulants accumulated the majority of tweets. Notably, the content that generated the highest frequency of tweets was that related to treatment efficacy, with alpha-2 agonist-related tweets accumulating the highest proportion of positive consideration. We found the highest percentages of tweets with scientific links in those posts related to alpha-2 agonists. Stimulant-related tweets obtained the highest proportion of likes and were the most disseminated within the Twitter community. Understanding the public view of these medications is necessary to design promotional strategies aimed at the appropriate population.

Analyzing Twitter Data to Evaluate People's Attitudes towards Public Health Policies and Events in the Era of COVID-19

Policymakers and relevant public health authorities can analyze people’s attitudes towards public health policies and events using sentiment analysis. Sentiment analysis focuses on classifying and analyzing text sentiments. A Twitter sentiment analysis has the potential to monitor people’s attitudes towards public health policies and events. Here, we explore the feasibility of using Twitter data to build a surveillance system for monitoring people’s attitudes towards public health policies and events since the beginning of the COVID-19 pandemic. In this study, we conducted a sentiment analysis of Twitter data. We analyzed the relationship between the sentiment changes in COVID-19-related tweets and public health policies and events. Furthermore, to improve the performance of the early trained model, we developed a data preprocessing approach by using the pre-trained model and early Twitter data, which were available at the beginning of the pandemic. Our study identified a strong correlation between the sentiment changes in COVID-19-related Twitter data and public health policies and events. Additionally, the experimental results suggested that the data preprocessing approach improved the performance of the early trained model. This study verified the feasibility of developing a fast and low-human-effort surveillance system for monitoring people’s attitudes towards public health policies and events during a pandemic by analyzing Twitter data. Based on the pre-trained model and early Twitter data, we can quickly build a model for the surveillance system.

Analysis of Media Outlets on Women's Health: Thematic and Quantitative Analyses Using Twitter

Background: Media outlets influence social attitudes toward health habits. The analysis of tweets has become a tool for health researchers.

Objective: The objective of this study was to investigate the distribution of tweets about women's health and the interest generated among Twitter users.

Methods: We investigated tweets posted by 25 major U.S. media outlets about pre-menopausal and post-menopausal women's health between January 2009 and December 2019 as well as the retweets generated. In addition, we measured the sentiment analysis of these tweets as well as their potential dissemination.

Results: A total of 376 tweets were analyzed. Pre-menopausal women's health accounted for most of the tweets (75.3%). Contraception was the main focus of the tweets, while a very limited number were related to infertility (1.4%). With regard to medical content, the effectiveness of contraceptive methods was the most frequent topic (46.2%). However, tweets related to side effects achieved the highest retweet-to-tweet ratio (70.3). The analysis of sentiments showed negative perceptions on tubal ligation.

Conclusions: The U.S. media outlets analyzed are more interested in pre-menopausal than in post-menopausal women health and focused their content on contraception, while Twitter users showed greater interest in side effects.

The Use of Social Media for Health Research Purposes: Scoping Review

BACKGROUND

As social media are increasingly used worldwide, more and more scientists are relying on them for their health-related projects. However, social media features, methodologies, and ethical issues are unclear so far because, to our knowledge, there has been no overview of this relatively young field of research.

OBJECTIVE

This scoping review aimed to provide an evidence map of the different uses of social media for health research purposes, their fields of application, and their analysis methods.

METHODS

We followed the scoping review methodologies developed by Arksey and O'Malley and the Joanna Briggs Institute. After developing search strategies based on keywords (eg, social media, health research), comprehensive searches were conducted in the PubMed/MEDLINE and Web of Science databases. We limited the search strategies to documents written in English and published between January 1, 2005, and April 9, 2020. After removing duplicates, articles were screened at the title and abstract level and at the full text level by two independent reviewers. One reviewer extracted data, which were descriptively analyzed to map the available evidence.

RESULTS

After screening 1237 titles and abstracts and 407 full texts, 268 unique papers were included, dating from 2009 to 2020 with an average annual growth rate of 32.71% for the 2009-2019 period. Studies mainly came from the Americas (173/268, 64.6%, including 151 from the United States). Articles used machine learning or data mining techniques (60/268) to analyze the data, discussed opportunities and limitations of the use of social media for research (59/268), assessed the feasibility of recruitment strategies (45/268), or discussed ethical issues (16/268). Communicable (eg, influenza, 40/268) and then chronic (eg, cancer, 24/268) diseases were the two main areas of interest.

CONCLUSIONS

Since their early days, social media have been recognized as resources with high potential for health research purposes, yet the field is still suffering from strong heterogeneity in the methodologies used, which prevents the research from being compared and generalized. For the field to be fully recognized as a valid, complementary approach to more traditional health research study designs, there is now a need for more guidance by types of applications of social media for health research, both from a methodological and an ethical perspective.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.1136/bmjopen-2020-040671.

[Contrasting Misinformation and Real-Information Dissemination Network Structures on Social Media During a Health Emergency]

Objetivos.

Elaborar un esquema operativo integral para detectar la información errónea principal sobre el zika distribuida en Twitter® en el 2016; reconstruir las redes por las que se difunde información mediante retuiteo; contrastar la información verídica frente a la errónea con diversos parámetros; e investigar cómo se difundió en las redes sociales la información errónea sobre el zika durante la epidemia.

Métodos.

Revisamos sistemáticamente los 5 000 tuits más retuiteados con información sobre el zika en inglés, definimos “información errónea” a partir de la evidencia, buscamos tuits que tuvieran información errónea y conformamos un grupo equiparable de tuits con información verídica. Elaboramos un algoritmo para reconstruir las redes de retuiteo de 266 tuits con información errónea y 458 tuits equiparables con información verídica. Calculamos y comparamos nueve parámetros para caracterizar la estructura de las redes a varios niveles, entre los dos grupos.

Resultados.

En los nueve parámetros se aprecian diferencias estadísticamente significativas entre el grupo de información verídica y el de información errónea. La información errónea en general se difunde mediante estructuras más sofisticadas que la información verídica. También hay una considerable variabilidad intragrupal.

Conclusiones.

Las redes de difusión de la información errónea sobre el zika en Twitter fueron sustancialmente diferentes que las de información verídica, lo cual indica que la información errónea se sirve de mecanismos de difusión distintos. Nuestro estudio permitirá formar una comprensión más holística de los desafíos que plantea la información errónea sobre salud en las redes sociales.

#DoctorsSpeakUp: Lessons learned from a pro-vaccine Twitter event

BACKGROUND

In response to growing anti-vaccine activism on social media, the #DoctorsSpeakUp event was designed to promote pro-vaccine advocacy. This study aimed to analyze Twitter content related to the event to determine (1) characteristics of the Twitter users who authored these tweets, (2) the proportion of tweets expressing pro-vaccine compared to anti-vaccine sentiment, and (3) the content of these tweets.

METHODS

Data were collected using Twitter's Filtered Streams Interface, and included all publicly available tweets with the "#DoctorsSpeakUp" hashtag on March 5, 2020, the day of the event. Two independent coders assessed a 5% subsample of original tweets (n = 966) using a thematic content analysis approach. Cohen's κ ranged 0.71-1.00 for all categories. Chi-square and Fisher's exact tests were used to examine associations between tweet sentiment, type of account, and tweet content (personal narrative and/or statement about research or science). Accounts were analyzed for likelihood of being a bot (i.e. automated account) using Botometer.

RESULTS

Of 847 (87.7%) relevant tweets, 244 (28.8%) were authored by a Twitter user that identified as a parent and 68 (8.0%) by a user that identified as a health professional. With regard to sentiment, 167 (19.7%) were coded as pro-vaccine and 668 (78.9%) were coded as anti-vaccine. Tweet sentiment was significantly associated with type of account (p < 0.001) and tweet content (p = 0.001). Of the 575 unique users in our dataset, 31 (5.4%) were classified as bots using Botometer.

CONCLUSIONS

Our results suggest a highly coordinated response of devoted anti-vaccine antagonists in response to the #DoctorsSpeakUp event. These findings can be used to help vaccine advocates leverage social media more effectively to promote vaccines. Specifically, it would be valuable to ensure that pro-vaccine messages consider hashtag use and pre-develop messages that can be launched and promoted by pro-vaccine advocates.

[Medical applications of social networks. Specific aspects of the COVID-19 pandemic]

For years, social networks have been incorporated into the day-to-day of the majority of the population. In this context, a new area of knowledge in medicine has been developed: infodemiology. It is defined as the evaluation, with the objective of improving public health, of health-related information that users upload to the network. In addition, social networks offer many possibilities for conducting public health campaigns, accessing patients, or carrying out treatment interventions.

Contrasting Misinformation and Real-Information Dissemination Network Structures on Social Media During a Health Emergency

Objectives. To provide a comprehensive workflow to identify top influential health misinformation about Zika on Twitter in 2016, reconstruct information dissemination networks of retweeting, contrast mis- from real information on various metrics, and investigate how Zika misinformation proliferated on social media during the Zika epidemic.Methods. We systematically reviewed the top 5000 English-language Zika tweets, established an evidence-based definition of "misinformation," identified misinformation tweets, and matched a comparable group of real-information tweets. We developed an algorithm to reconstruct retweeting networks for 266 misinformation and 458 comparable real-information tweets. We computed and compared 9 network metrics characterizing network structure across various levels between the 2 groups.Results. There were statistically significant differences in all 9 network metrics between real and misinformation groups. Misinformation network structures were generally more sophisticated than those in the real-information group. There was substantial within-group variability, too.Conclusions. Dissemination networks of Zika misinformation differed substantially from real information on Twitter, indicating that misinformation utilized distinct dissemination mechanisms from real information. Our study will lead to a more holistic understanding of health misinformation challenges on social media.

Adapting and Extending a Typology to Identify Vaccine Misinformation on Twitter

Objectives. To adapt and extend an existing typology of vaccine misinformation to classify the major topics of discussion across the total vaccine discourse on Twitter.Methods. Using 1.8 million vaccine-relevant tweets compiled from 2014 to 2017, we adapted an existing typology to Twitter data, first in a manual content analysis and then using latent Dirichlet allocation (LDA) topic modeling to extract 100 topics from the data set.Results. Manual annotation identified 22% of the data set as antivaccine, of which safety concerns and conspiracies were the most common themes. Seventeen percent of content was identified as provaccine, with roughly equal proportions of vaccine promotion, criticizing antivaccine beliefs, and vaccine safety and effectiveness. Of the 100 LDA topics, 48 contained provaccine sentiment and 28 contained antivaccine sentiment, with 9 containing both.Conclusions. Our updated typology successfully combines manual annotation with machine-learning methods to estimate the distribution of vaccine arguments, with greater detail on the most distinctive topics of discussion. With this information, communication efforts can be developed to better promote vaccines and avoid amplifying antivaccine rhetoric on Twitter.

Limited Role of Bots in Spreading Vaccine-Critical Information Among Active Twitter Users in the United States: 2017-2019

Objectives. To examine the role that bots play in spreading vaccine information on Twitter by measuring exposure and engagement among active users from the United States.Methods. We sampled 53 188 US Twitter users and examined who they follow and retweet across 21 million vaccine-related tweets (January 12, 2017-December 3, 2019). Our analyses compared bots to human-operated accounts and vaccine-critical tweets to other vaccine-related tweets.Results. The median number of potential exposures to vaccine-related tweets per user was 757 (interquartile range [IQR] = 168-4435), of which 27 (IQR = 6-169) were vaccine critical, and 0 (IQR = 0-12) originated from bots. We found that 36.7% of users retweeted vaccine-related content, 4.5% retweeted vaccine-critical content, and 2.1% retweeted vaccine content from bots. Compared with other users, the 5.8% for whom vaccine-critical tweets made up most exposures more often retweeted vaccine content (62.9%; odds ratio [OR] = 2.9; 95% confidence interval [CI] = 2.7, 3.1), vaccine-critical content (35.0%; OR = 19.0; 95% CI = 17.3, 20.9), and bots (8.8%; OR = 5.4; 95% CI = 4.7, 6.3).Conclusions. A small proportion of vaccine-critical information that reaches active US Twitter users comes from bots.

The Impacts of Air Pollution on Mental Health: Evidence from the Chinese University Students

A growing number of developing countries have experienced worsening air pollution, which has been shown to cause significant health problems. However, few studies have explored the impact of air pollution on the mental health of university students, particularly in the Chinese context. In order to address this gap, through a large-scale cross-sectional survey, this study aims to examine the effects of air pollution on final-year Chinese university undergraduates' (due to graduate in 2020) mental health by employing multivariable logistic regression. Our findings show that, first, although normal air quality is not strongly associated with lower levels of negative mental health, there is a strong link between poor air quality and higher levels of negative mental health. More specifically, life satisfaction hedonic unhappiness and depression measured by the Centre for Epidemiological Studies' Depression scale (CES-D) are statistically associated with air pollution. In addition, we also found that gender is a significant factor, as males had more than 1.6 times greater odds of increased mental health problems compared to their female counterparts. Place of birth also plays a significant role in participants' mental health. Moreover, undergraduates with urban household registration experienced significant levels of hedonic unhappiness and depression on the CES-D scale. Finally, we found that there is an association between respondents' economic situation and their mental health too. Overall, this study contributes to the research on air pollution management and mental health intervention, particularly in relation to student groups. The undergraduate curriculum should provide more guidance and suggestions on promoting mental health and establishing positive attitudes to life and academic study of the final year students, under the context of air pollution in China.

Machine Learning Classifiers for Twitter Surveillance of Vaping: Comparative Machine Learning Study

BACKGROUND

Twitter presents a valuable and relevant social media platform to study the prevalence of information and sentiment on vaping that may be useful for public health surveillance. Machine learning classifiers that identify vaping-relevant tweets and characterize sentiments in them can underpin a Twitter-based vaping surveillance system. Compared with traditional machine learning classifiers that are reliant on annotations that are expensive to obtain, deep learning classifiers offer the advantage of requiring fewer annotated tweets by leveraging the large numbers of readily available unannotated tweets.

OBJECTIVE

This study aims to derive and evaluate traditional and deep learning classifiers that can identify tweets relevant to vaping, tweets of a commercial nature, and tweets with provape sentiments.

METHODS

We continuously collected tweets that matched vaping-related keywords over 2 months from August 2018 to October 2018. From this data set of tweets, a set of 4000 tweets was selected, and each tweet was manually annotated for relevance (vape relevant or not), commercial nature (commercial or not), and sentiment (provape or not). Using the annotated data, we derived traditional classifiers that included logistic regression, random forest, linear support vector machine, and multinomial naive Bayes. In addition, using the annotated data set and a larger unannotated data set of tweets, we derived deep learning classifiers that included a convolutional neural network (CNN), long short-term memory (LSTM) network, LSTM-CNN network, and bidirectional LSTM (BiLSTM) network. The unannotated tweet data were used to derive word vectors that deep learning classifiers can leverage to improve performance.

RESULTS

LSTM-CNN performed the best with the highest area under the receiver operating characteristic curve (AUC) of 0.96 (95% CI 0.93-0.98) for relevance, all deep learning classifiers including LSTM-CNN performed better than the traditional classifiers with an AUC of 0.99 (95% CI 0.98-0.99) for distinguishing commercial from noncommercial tweets, and BiLSTM performed the best with an AUC of 0.83 (95% CI 0.78-0.89) for provape sentiment. Overall, LSTM-CNN performed the best across all 3 classification tasks.

CONCLUSIONS

We derived and evaluated traditional machine learning and deep learning classifiers to identify vaping-related relevant, commercial, and provape tweets. Overall, deep learning classifiers such as LSTM-CNN had superior performance and had the added advantage of requiring no preprocessing. The performance of these classifiers supports the development of a vaping surveillance system.

Identifying Influential Factors in the Discussion Dynamics of Emerging Health Issues on Social Media: Computational Study

Background

Social media has become a major resource for observing and understanding public opinions using infodemiology and infoveillance methods, especially during emergencies such as disease outbreaks. For public health agencies, understanding the driving forces of web-based discussions will help deliver more effective and efficient information to general users on social media and the web.

Objective

The study aimed to identify the major contributors that drove overall Zika-related tweeting dynamics during the 2016 epidemic. In total, 3 hypothetical drivers were proposed: (1) the underlying Zika epidemic quantified as a time series of case counts; (2) sporadic but critical real-world events such as the 2016 Rio Olympics and World Health Organization’s Public Health Emergency of International Concern (PHEIC) announcement, and (3) a few influential users’ tweeting activities.

Methods

All tweets and retweets (RTs) containing the keyword Zika posted in 2016 were collected via the Gnip application programming interface (API). We developed an analytical pipeline, EventPeriscope, to identify co-occurring trending events with Zika and quantify the strength of these events. We also retrieved Zika case data and identified the top influencers of the Zika discussion on Twitter. The influence of 3 potential drivers was examined via a multivariate time series analysis, signal processing, a content analysis, and text mining techniques.

Results

Zika-related tweeting dynamics were not significantly correlated with the underlying Zika epidemic in the United States in any of the four quarters in 2016 nor in the entire year. Instead, peaks of Zika-related tweeting activity were strongly associated with a few critical real-world events, both planned, such as the Rio Olympics, and unplanned, such as the PHEIC announcement. The Rio Olympics was mentioned in >15% of all Zika-related tweets and PHEIC occurred in 27% of Zika-related tweets around their respective peaks. In addition, the overall tweeting dynamics of the top 100 most actively tweeting users on the Zika topic, the top 100 users receiving most RTs, and the top 100 users mentioned were the most highly correlated to and preceded the overall tweeting dynamics, making these groups of users the potential drivers of tweeting dynamics. The top 100 users who retweeted the most were not critical in driving the overall tweeting dynamics. There were very few overlaps among these different groups of potentially influential users.

Conclusions

Using our proposed analytical workflow, EventPeriscope, we identified that Zika discussion dynamics on Twitter were decoupled from the actual disease epidemic in the United States but were closely related to and highly influenced by certain sporadic real-world events as well as by a few influential users. This study provided a methodology framework and insights to better understand the driving forces of web-based public discourse during health emergencies. Therefore, health agencies could deliver more effective and efficient web-based communications in emerging crises.

Eating Disorder Awareness Campaigns: Thematic and Quantitative Analysis Using Twitter

Background

Health awareness initiatives are frequent but their efficacy is a matter of controversy. We have investigated the effect of the Eating Disorder Awareness Week and Wake Up Weight Watchers campaigns on Twitter.

Objective

We aimed to examine whether the Eating Disorder Awareness Week and Wake Up Weight Watchers initiatives increased the volume and dissemination of Twitter conversations related to eating disorders and investigate what content generates the most interest on Twitter.

Methods

Over a period of 12 consecutive days in 2018, we collected tweets containing the hashtag #wakeupweightwatchers and hashtags related to Eating Disorder Awareness Week (#eatingdisorderawarenessweek, #eatingdisorderawareness, or #EDAW), with the hashtag #eatingdisorder as a control. The content of each tweet was rated as medical, testimony, help offer, awareness, pro-ana, or anti-ana. We analyzed the number of retweets and favorites generated, as well as the potential reach and impact of the hashtags and the characteristics of contributors.

Results

The number of #wakeupweightwatchers tweets was higher than that of Eating Disorder Awareness Week and #eatingdisorder tweets (3900, 2056, and 1057, respectively). The content of tweets was significantly different between the hashtags analyzed (P<.001). Medical content was lower in the awareness campaigns. Awareness and help offer content were lower in #wakeupweightwatchers tweets. Retweet and favorite ratios were highest in #wakeupweightwatchers tweets. Eating Disorder Awareness Week achieved the highest impact, and very influential contributors participated.

Conclusions

Both awareness campaigns effectively promoted tweeting about eating disorders. The majority of tweets did not promote any specific preventive or help-seeking behaviors.

Understanding Discussions of Health Issues on Twitter: A Visual Analytic Study

Social media allows for the exploration of online discussions of health issues outside of traditional health spaces. Twitter is one of the largest social media platforms that allows users to post short comments (i.e., tweets). The unrestricted access to opinions and a large user base makes Twitter a major source for collection and quick dissemination of some health information. Health organizations, individuals, news organizations, businesses, and a host of other entities discuss health issues on Twitter. However, the enormous number of tweets presents challenges to those who seek to improve their knowledge of health issues. For instance, it is difficult to understand the overall sentiment on a health issue or the central message of the discourse. For Twitter to be an effective tool for health promotion, stakeholders need to be able to understand, analyze, and appraise health information and discussions on this platform. The purpose of this paper is to examine how a visual analytic study can provide insight into a variety of health issues on Twitter. Visual analytics enhances the understanding of data by combining computational models with interactive visualizations. Our study demonstrates how machine learning techniques and visualizations can be used to analyze and understand discussions of health issues on Twitter. In this paper, we report on the process of data collection, analysis of data, and representation of results. We present our findings and discuss the implications of this work to support the use of Twitter for health promotion.

The use of internet analytics by a Canadian provincial chiropractic regulator to monitor, evaluate and remediate misleading claims regarding specific health conditions, pregnancy, and COVID-19

BACKGROUND

Internet analytics are increasingly being integrated into public health regulation. One specific application is to monitor compliance of website and social media activity with respect to jurisdictional regulations. These data may then identify breaches of compliance and inform disciplinary actions. Our study aimed to evaluate the novel use of internet analytics by a Canadian chiropractic regulator to determine their registrants compliance with three regulations related to specific health conditions, pregnancy conditions and most recently, claims of improved immunity during the COVID-19 crisis.

METHODS

A customized internet search tool (Market Review Tool, MRT) was used by the College of Chiropractors of British Columbia (CCBC), Canada to audit registrants websites and social media activity. The audits extracted words whose use within specific contexts is not permitted under CCBC guidelines. The MRT was first used in October of 2018 to identify words related to specific health conditions. The MRT was again used in December 2019 for words related to pregnancy and most recently in March 2020 for words related to COVID-19. In these three MRT applications, potential cases of word misuse were evaluated by the regulator who then notified the practitioner to comply with existing regulations by a specific date. The MRT was then used on that date to determine compliance. Those found to be non-compliant were referred to the regulator's inquiry committee. We mapped this process and reported the outcomes with permission of the regulator.

RESULTS

In September 2018, 250 inappropriate mentions of specific health conditions were detected from approximately 1250 registrants with 2 failing to comply. The second scan for pregnancy related terms of approximately1350 practitioners revealed 83 inappropriate mentions. Following notification, all 83 cases were compliant within the specified timeframe. Regarding COVID-19 related words, 97 inappropriate mentions of the word "immune" were detected from 1350 registrants with 7 cases of non-compliance.

CONCLUSION

Internet analytics are an effective way for regulators to monitor internet activity to protect the public from misleading statements. The processes described were effective at bringing about rapid practitioner compliance. Given the increasing volume of internet activity by healthcare professionals, internet analytics are an important addition for health care regulators to protect the public they serve.

VARTTA: A Visual Analytics System for Making Sense of Real-Time Twitter Data

Through social media platforms, massive amounts of data are being produced. As a microblogging social media platform, Twitter enables its users to post short updates as “tweets” on an unprecedented scale. Once analyzed using machine learning (ML) techniques and in aggregate, Twitter data can be an invaluable resource for gaining insight into different domains of discussion and public opinion. However, when applied to real-time data streams, due to covariate shifts in the data (i.e., changes in the distributions of the inputs of ML algorithms), existing ML approaches result in different types of biases and provide uncertain outputs. In this paper, we describe VARTTA (Visual Analytics for Real-Time Twitter datA), a visual analytics system that combines data visualizations, human-data interaction, and ML algorithms to help users monitor, analyze, and make sense of the streams of tweets in a real-time manner. As a case study, we demonstrate the use of VARTTA in political discussions. VARTTA not only provides users with powerful analytical tools, but also enables them to diagnose and to heuristically suggest fixes for the errors in the outcome, resulting in a more detailed understanding of the tweets. Finally, we outline several issues to be considered while designing other similar visual analytics systems.

JUUL on Twitter: Analyzing Tweets About Use of a New Nicotine Delivery System

BACKGROUND

Initial reports suggest that JUUL, a popular e-cigarette, is being used in schools and other locations in which cigarette smoking is illegal or discouraged. However, there is little scholarly research documenting this. We aimed to make a systematic analysis of JUUL use themes and sentiment on Twitter.

METHODS

Data were collected from Twitter's Filtered Streams Application Programming Interface from April 12, 2018 to May 10, 2018. This yielded 67,934 tweets, from which a random sample of 2% was selected for coding. The final dataset included 1209 tweets. Inter-rater reliability ranged κ = 0.64-0.85.

RESULTS

The majority (71.5%) of tweets expressed positive sentiment toward JUUL. JUUL use in places where cigarette smoking is illegal or discouraged appeared in 111 tweets (9.2%); approximately one-third of these tweets referring to using the device in school. Nearly 20% of tweets mentioned using the device at home and/or directly in front of responsible adults.

CONCLUSIONS

This study confirms anecdotal reports of JUUL use in places where cigarette smoking is illegal or discouraged. Positive sentiment about use of JUUL suggests that the product is being normalized among young people. It may be valuable for educators to discuss the addictive nature of nicotine delivered through JUUL with younger populations.

Deep learning for pollen allergy surveillance from twitter in Australia

BACKGROUND

The paper introduces a deep learning-based approach for real-time detection and insights generation about one of the most prevalent chronic conditions in Australia - Pollen allergy. The popular social media platform is used for data collection as cost-effective and unobtrusive alternative for public health monitoring to complement the traditional survey-based approaches.

METHODS

The data was extracted from Twitter based on pre-defined keywords (i.e. 'hayfever' OR 'hay fever') throughout the period of 6 months, covering the high pollen season in Australia. The following deep learning architectures were adopted in the experiments: CNN, RNN, LSTM and GRU. Both default (GloVe) and domain-specific (HF) word embeddings were used in training the classifiers. Standard evaluation metrics (i.e. Accuracy, Precision and Recall) were calculated for the results validation. Finally, visual correlation with weather variables was performed.

RESULTS

The neural networks-based approach was able to correctly identify the implicit mentions of the symptoms and treatments, even unseen previously (accuracy up to 87.9% for GRU with GloVe embeddings of 300 dimensions).

CONCLUSIONS

The system addresses the shortcomings of the conventional machine learning techniques with manual feature-engineering that prove limiting when exposed to a wide range of non-standard expressions relating to medical concepts. The case-study presented demonstrates an application of 'black-box' approach to the real-world problem, along with its internal workings demonstration towards more transparent, interpretable and reproducible decision-making in health informatics domain.

Automatically Appraising the Credibility of Vaccine-Related Web Pages Shared on Social Media: A Twitter Surveillance Study

Background

Tools used to appraise the credibility of health information are time-consuming to apply and require context-specific expertise, limiting their use for quickly identifying and mitigating the spread of misinformation as it emerges.

Objective

The aim of this study was to estimate the proportion of vaccine-related Twitter posts linked to Web pages of low credibility and measure the potential reach of those posts.

Methods

Sampling from 143,003 unique vaccine-related Web pages shared on Twitter between January 2017 and March 2018, we used a 7-point checklist adapted from validated tools and guidelines to manually appraise the credibility of 474 Web pages. These were used to train several classifiers (random forests, support vector machines, and recurrent neural networks) using the text from a Web page to predict whether the information satisfies each of the 7 criteria. Estimating the credibility of all other Web pages, we used the follower network to estimate potential exposures relative to a credibility score defined by the 7-point checklist.

Results

The best-performing classifiers were able to distinguish between low, medium, and high credibility with an accuracy of 78% and labeled low-credibility Web pages with a precision of over 96%. Across the set of unique Web pages, 11.86% (16,961 of 143,003) were estimated as low credibility and they generated 9.34% (1.64 billion of 17.6 billion) of potential exposures. The 100 most popular links to low credibility Web pages were each potentially seen by an estimated 2 million to 80 million Twitter users globally.

Conclusions

The results indicate that although a small minority of low-credibility Web pages reach a large audience, low-credibility Web pages tend to reach fewer users than other Web pages overall and are more commonly shared within certain subpopulations. An automatic credibility appraisal tool may be useful for finding communities of users at higher risk of exposure to low-credibility vaccine communications.

I wake up and hit the JUUL: Analyzing Twitter for JUUL nicotine effects and dependence

BACKGROUND

JUUL-a novel electronic nicotine delivery system (ENDS)-comprises most of the ENDS market share. Additionally, JUUL has a high nicotine content and utilizes a patented nicotine salt formulation aimed to speed absorption. Many JUUL users are not aware of the nicotine content and therefore may not be expecting acute nicotine effects or potential for dependence. This study sought to analyze Twitter messages ("tweets") regarding nicotine, symptoms of dependence, and withdrawal related to JUUL use.

METHODS

Data were collected from Twitter's Filtered Streams interface 4/11-6/16/2018 by retrieving tweets matching the terms "juul," "juuls," and "juuling" that also used words consistent with nicotine effects, symptoms of dependence, and withdrawal. A random 5% subsample (n = 1986) was coded by 2 independent coders. Cohen's κ for inter-rater reliability ranged 0.62-1.00 for all coded variables. Tweets were assessed using a qualitative content analysis approach.

RESULTS

A total of 335 tweets mentioned dependence-related themes, including use upon waking and compulsion to use. A total of 189 tweets mentioned themes related to nicotine, with almost 15% of these tweets describing physical effects. Additionally, 42 tweets mentioned themes related to quitting JUUL and/or withdrawal from JUUL.

DISCUSSION

This qualitative analysis suggests that users of JUUL are experiencing symptoms of nicotine exposure and dependence. Considering the high nicotine content of JUUL and the rising popularity among young people, more research around initiation of and dependence on JUUL, as well as the impact of recent FDA policy changes, should be conducted.