IQOS marketing strategies in the USA before and after US FDA modified risk tobacco product authorisation.

INTRODUCTION: Given that the US Food and Drug Administration (FDA) authorised reduced exposure messaging to be used in IQOS marketing, we examined: (1) IQOS ad content; (2) advertising media channels and (3) changes in advertising efforts over time. METHODS: We conducted a mixed-methods study regarding IQOS ad content (headlines, themes, images), ad occurrence characteristics (including content, media channels and adspend) in the USA from August 2019 to April 2021 using Numerator advertising/marketing data. RESULTS: Across 24 unique ads and 591 occurrences (84.6% online, 8.0% print, 7.4% mobile), there was $4 902 110 total adspend (98.9% allocated to print). Across unique ads, occurrences and adspend, prominent themes included 'real tobacco' (45.8%, 47.4%, 30.1%, respectively), less odour/ash (29.2%, 21.0%, 29.9%) and switching from cigarettes (25.0%, 19.5%, 69.4%), and images mainly featured the product alone (58.2%, 61.4%, 99.5%) or with women (25.0%, 19.1%, 0.3%). Per occurrences and adspend, the most prominent media channel themes (eg, magazine/website topics) were technology (19.3%, 10.6%), women's fashion (18.1%, 26.2%), weather/news (9.0%, 15.3%) and entertainment/pop culture/gaming (8.5%, 23.1%). Ad themes appearing only post-FDA authorisation included switching from traditional cigarettes, same-day/home-delivery, convenience (eg, use indoors), reduced exposure to some dangerous substances, science/research and distinction from e-cigarettes. Overall adspend per occurrence increased postauthorisation (p=0.016); the highest adspend per unique ad (69.3% of total) focused on ads featuring reduced exposure. CONCLUSIONS: Regulatory efforts must be informed by ongoing surveillance of IQOS marketing efforts and its impacts, particularly how specific consumer subgroups (eg, tobacco non-users, women, young people) are impacted by marketing exposure.

Facebook Pages, the "Disneyland" Measles Outbreak, and Promotion of Vaccine Refusal as a Civil Right, 2009-2019.

Objectives. To understand changes in how Facebook pages frame vaccine opposition.Methods. We categorized 204 Facebook pages expressing vaccine opposition, extracting public posts through November 20, 2019. We analyzed posts from October 2009 through October 2019 to examine if pages' content was coalescing.Results. Activity in pages promoting vaccine choice as a civil liberty increased in January 2015, April 2016, and January 2019 (t[76] = 11.33 [P < .001]; t[46] = 7.88 [P < .001]; and t[41] = 17.27 [P < .001], respectively). The 2019 increase was strongest in pages mentioning US states (t[41] = 19.06; P < .001). Discussion about vaccine safety decreased (rs[119] = -0.61; P < .001) while discussion about civil liberties increased (rs[119] = 0.33; Py < .001]). Page categories increasingly resembled one another (civil liberties: rs[119] = -0.50 [P < .001]; alternative medicine: rs[84] = -0.77 [P < .001]; conspiracy theories: rs[119] = -0.46 [P < .001]; morality: rs[106] = -0.65 [P < .001]; safety and efficacy: rs[119] = -0.46 [P < .001]).Conclusions. The "Disneyland" measles outbreak drew vaccine opposition into the political mainstream, followed by promotional campaigns conducted in pages framing vaccine refusal as a civil right. Political mobilization in state-focused pages followed in 2019.Public Health Implications. Policymakers should expect increasing attempts to alter state legislation associated with vaccine exemptions, potentially accompanied by fiercer lobbying from specific celebrities.

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.

The Twitter Social Mobility Index: Measuring Social Distancing Practices With Geolocated Tweets.

BACKGROUND: Social distancing is an important component of the response to the COVID-19 pandemic. Minimizing social interactions and travel reduces the rate at which the infection spreads and "flattens the curve" so that the medical system is better equipped to treat infected individuals. However, it remains unclear how the public will respond to these policies as the pandemic continues. OBJECTIVE: The aim of this study is to present the Twitter Social Mobility Index, a measure of social distancing and travel derived from Twitter data. We used public geolocated Twitter data to measure how much users travel in a given week. METHODS: We collected 469,669,925 tweets geotagged in the United States from January 1, 2019, to April 27, 2020. We analyzed the aggregated mobility variance of a total of 3,768,959 Twitter users at the city and state level from the start of the COVID-19 pandemic. RESULTS: We found a large reduction (61.83%) in travel in the United States after the implementation of social distancing policies. However, the variance by state was high, ranging from 38.54% to 76.80%. The eight states that had not issued statewide social distancing orders as of the start of April ranked poorly in terms of travel reduction: Arkansas (45), Iowa (37), Nebraska (35), North Dakota (22), South Carolina (38), South Dakota (46), Oklahoma (50), Utah (14), and Wyoming (53). We are presenting our findings on the internet and will continue to update our analysis during the pandemic. CONCLUSIONS: We observed larger travel reductions in states that were early adopters of social distancing policies and smaller changes in states without such policies. The results were also consistent with those based on other mobility data to a certain extent. Therefore, geolocated tweets are an effective way to track social distancing practices using a public resource, and this tracking may be useful as part of ongoing pandemic response planning.

Abstraction: An alternative neurocognitive account of recognition, prediction, and decision making.

Gilead et al. offer a thoughtful and much-needed treatment of abstraction. However, it fails to build on an extensive literature on abstraction, representational diversity, neurocognition, and psychopathology that provides important constraints and alternative evidence-based conceptions. We draw on conceptions in software engineering, socio-technical systems engineering, and a neurocognitive theory with abstract representations of gist at its core, fuzzy-trace theory.

To illuminate and motivate: A fuzzy-trace model of the spread of information online.

We propose, and test, a model of online media platform users' decisions to act on, and share, received information. Specifically, we focus on how mental representations of message content drive its spread. Our model is based on Fuzzy-Trace Theory (FTT), a leading theory of decision under risk. Per FTT, online content is mentally represented in two ways: verbatim (objective, but decontextualized, facts), and gist (subjective, but meaningful, interpretation). Although encoded in parallel, gist tends to drive behaviors more strongly than verbatim representations for most individuals. Our model uses factors derived from FTT to make predictions regarding which content is more likely to be shared, namely: a) different levels of mental representation, b) the motivational content of a message, c) difficulty of information processing (e.g., the ease with which a given message may be comprehended and, therefore, its gist extracted), and d) social values.

Malicious Actors on Twitter: A Guide for Public Health Researchers.

Social bots and other malicious actors have a significant presence on Twitter. It is increasingly clear that some of their activities can have a negative impact on public health. This guide provides an overview of the types of malicious actors currently active on Twitter by highlighting the characteristic behaviors and strategies employed. It covers both automated accounts (including traditional spambots, social spambots, content polluters, and fake followers) and human users (primarily trolls). It also addresses the unique threat of state-sponsored trolls. We utilize examples from our own research on vaccination to illustrate. The diversity of malicious actors and their multifarious goals adds complexity to research efforts that use Twitter. Bots are now part of the social media landscape, and although it may not be possible to stop their influence, it is vital that public health researchers and practitioners recognize the potential harms and develop strategies to address bot- and troll-driven messages.

Weaponized Health Communication: Twitter Bots and Russian Trolls Amplify the Vaccine Debate.

OBJECTIVES: To understand how Twitter bots and trolls ("bots") promote online health content. METHODS: We compared bots' to average users' rates of vaccine-relevant messages, which we collected online from July 2014 through September 2017. We estimated the likelihood that users were bots, comparing proportions of polarized and antivaccine tweets across user types. We conducted a content analysis of a Twitter hashtag associated with Russian troll activity. RESULTS: Compared with average users, Russian trolls (χ2(1) = 102.0; P < .001), sophisticated bots (χ2(1) = 28.6; P < .001), and "content polluters" (χ2(1) = 7.0; P < .001) tweeted about vaccination at higher rates. Whereas content polluters posted more antivaccine content (χ2(1) = 11.18; P < .001), Russian trolls amplified both sides. Unidentifiable accounts were more polarized (χ2(1) = 12.1; P < .001) and antivaccine (χ2(1) = 35.9; P < .001). Analysis of the Russian troll hashtag showed that its messages were more political and divisive. CONCLUSIONS: Whereas bots that spread malware and unsolicited content disseminated antivaccine messages, Russian trolls promoted discord. Accounts masquerading as legitimate users create false equivalency, eroding public consensus on vaccination. Public Health Implications. Directly confronting vaccine skeptics enables bots to legitimize the vaccine debate. More research is needed to determine how best to combat bot-driven content.

Discordance Between Human Papillomavirus Twitter Images and Disparities in Human Papillomavirus Risk and Disease in the United States: Mixed-Methods Analysis.

BACKGROUND: Racial and ethnic minorities are disproportionately affected by human papillomavirus (HPV)-related cancer, many of which could have been prevented with vaccination. Yet, the initiation and completion rates of HPV vaccination remain low among these populations. Given the importance of social media platforms for health communication, we examined US-based HPV images on Twitter. We explored inconsistencies between the demographics represented in HPV images and the populations that experience the greatest burden of HPV-related disease. OBJECTIVE: The objective of our study was to observe whether HPV images on Twitter reflect the actual burden of disease by select demographics and determine to what extent Twitter accounts utilized images that reflect the burden of disease in their health communication messages. METHODS: We identified 456 image tweets about HPV that contained faces posted by US users between November 11, 2014 and August 8, 2016. We identified images containing at least one human face and utilized Face++ software to automatically extract the gender, age, and race of each face. We manually annotated the source accounts of these tweets into 3 types as follows: government (38/298, 12.8%), organizations (161/298, 54.0%), and individual (99/298, 33.2%) and topics (news, health, and other) to examine how images varied by message source. RESULTS: Findings reflected the racial demographics of the US population but not the disease burden (795/1219, 65.22% white faces; 140/1219, 11.48% black faces; 71/1219, 5.82% Asian faces; and 213/1219, 17.47% racially ambiguous faces). Gender disparities were evident in the image faces; 71.70% (874/1219) represented female faces, whereas only 27.89% (340/1219) represented male faces. Among the 11-26 years age group recommended to receive HPV vaccine, HPV images contained more female-only faces (214/616, 34.3%) than males (37/616, 6.0%); the remainder of images included both male and female faces (365/616, 59.3%). Gender and racial disparities were present across different image sources. Faces from government sources were more likely to depict females (n=44) compared with males (n=16). Of male faces, 80% (12/15) of youth and 100% (1/1) of adults were white. News organization sources depicted high proportions of white faces (28/38, 97% of female youth and 12/12, 100% of adult males). Face++ identified fewer faces compared with manual annotation because of limitations with detecting multiple, small, or blurry faces. Nonetheless, Face++ achieved a high degree of accuracy with respect to gender, race, and age compared with manual annotation. CONCLUSIONS: This study reveals critical differences between the demographics reflected in HPV images and the actual burden of disease. Racial minorities are less likely to appear in HPV images despite higher rates of HPV incidence. Health communication efforts need to represent populations at risk better if we seek to reduce disparities in HPV infection.

Categorical Risk Perception Drives Variability in Antibiotic Prescribing in the Emergency Department: A Mixed Methods Observational Study.

BACKGROUND: Adherence to evidence-based antibiotic therapy guidelines for treatment of upper respiratory tract infections (URIs) varies widely among clinicians. Understanding this variability is key for reducing inappropriate prescribing. OBJECTIVE: To measure how emergency department (ED) clinicians' perceptions of antibiotic prescribing risks affect their decision-making. DESIGN: Clinician survey based on fuzzy-trace theory, a theory of medical decision-making, combined with retrospective data on prescribing outcomes for URI/pneumonia visits in two EDs. The survey predicts the categorical meanings, or gists, that individuals derive from given information. PARTICIPANTS: ED physicians, residents, and physician assistants (PAs) who completed surveys and treated patients with URI/pneumonia diagnoses between August 2014 and December 2015. MAIN MEASURES: Gists derived from survey responses and their association with rates of antibiotic prescribing per visit. KEY RESULTS: Of 4474 URI/pneumonia visits, 2874 (64.2%) had an antibiotic prescription. However, prescribing rates varied from 7% to 91% for the 69 clinicians surveyed (65.2% response rate). Clinicians who framed therapy-prescribing decisions as a categorical choice between continued illness and possibly beneficial treatment ("why not take a risk?" gist, which assumes antibiotic therapy is essentially harmless) had higher rates of prescribing (OR 1.28 [95% CI, 1.06-1.54]). Greater agreement with the "antibiotics may be harmful" gist was associated with lower prescribing rates (OR 0.81 [95% CI, 0.67-0.98]). CONCLUSIONS: Our results indicate that clinicians who perceive prescribing as a categorical choice between patients remaining ill or possibly improving from therapy are more likely to prescribe antibiotics. However, this strategy assumes that antibiotics are essentially harmless. Clinicians who framed decision-making as a choice between potential harms from therapy and continued patient illness (e.g., increased appreciation of potential harms) had lower prescribing rates. These results suggest that interventions to reduce inappropriate prescribing should emphasize the non-negligible possibility of serious side effects.

The Emergence and Collapse of Knowledge Boundaries.

The dynamics of knowledge transfer is an important topic for engineering managers. In this paper, we study knowledge boundaries - barriers to knowledge transfer - in groups of experts, using topic modeling, a natural language processing technique, applied to transcript data from the U.S. Food and Drug Administration's Circulatory Systems Advisory Panel. As predicted by prior theory, we find that knowledge boundaries emerge as the group faces increasingly challenging problems. Beyond this theory, we find that knowledge boundaries cease to structure communications between communities of practice when the group's expert ability is insufficient to solve its task, such as in the presence of high novelty. We conjecture that the amount of expert knowledge that the group can collectively bring to bear is a determining factor in boundary formation. This implies that some of the factors underlying knowledge boundary formation may aid - rather than hinder - knowledge aggregation. We briefly explore this conjecture using qualitative exploration of several relevant meetings. Finally, we discuss implications of these results for organizations attempting to leverage their expertise given the state of their collective knowledge.

Twitter social mobility data reveal demographic variations in social distancing practices during the COVID-19 pandemic.

The COVID-19 pandemic demonstrated the importance of social distancing practices to stem the spread of the virus. However, compliance with public health guidelines was mixed. Understanding what factors are associated with differences in compliance can improve public health messaging since messages could be targeted and tailored to different population segments. We utilize Twitter data on social mobility during COVID-19 to reveal which populations practiced social distancing and what factors correlated with this practice. We analyze correlations between demographic and political affiliation with reductions in physical mobility measured by public geolocation tweets. We find significant differences in mobility reduction between these groups in the United States. We observe that males, Asian and Latinx individuals, older individuals, Democrats, and people from higher population density states exhibited larger reductions in movement. Furthermore, our study also unveils meaningful insights into the interactions between different groups. We hope these findings will provide evidence to support public health policy-making.

The role of mental representation in sharing misinformation online.

Fuzzy-trace theory (FTT) posits that people share misinformation online if it promotes gist mental representations, cuing motivationally relevant values. Most people value the truth. Thus, per FTT, people decide to share messages that they perceive as true. FTT also predicts that messages will be more effective if they communicate a simple gist. We test these predictions by examining the roles of mental representation and epistemic quality in decisions to share misinformative articles on Facebook across two experiments and two correlational studies. In Studies 1 and 2, we use Facebook data to test the hypothesis that gist proxies in text are associated with online sharing. In Study 3, we experimentally manipulate subjects' exposure to a gist-based intervention that explains why a misinformative article is false, a simple debunk stating only that the article is false (but not explaining why) and a verbatim condition providing relevant detailed information but allowing subjects to draw their own conclusions. We found that the gist condition decreased intentions to share misinformation. Finally, in Study 4, we replicated this finding and showed that the gist condition also reduces misinformation endorsement. Results provide support for FTT's predictions regarding reducing sharing and endorsement of misinformation on social media. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

User Experiences With a Moderated Facebook Group to Promote Vaccination.

PURPOSE: To examine user experiences in a moderated Facebook group intervention aimed at Coronavirus disease 2019 (COVID-19) vaccine promotion. DESIGN: Facebook group members were given 2-3 vaccination posts/day for 28 days (four weeks). Posts were aimed at educating about COVID-19 vaccination, soliciting concerns around COVID-19 vaccination, and engaging members. Participants were surveyed about their experience at four weeks. SETTING: Moderated Facebook group. PARTICIPANTS: Unvaccinated individuals who were randomized to the intervention group and completed four week follow-up (N = 216, 82.1%). METHOD: After four weeks, participants rated their experience in the Facebook group (eg, program satisfaction) and provided open-text responses about their satisfaction with the group. Free-text responses were dual coded and emergent themes were examined. RESULTS: On average, participants were 37.0 years old (SD = 10.3), majority female (70.9%), and white (79.7%). The majority of participants were satisfied with the group (76.7%), agreed that other people were friendly (M = 5.58/7), and felt safe discussing health information (M = 3.96/5). Open-text responses revealed that participants liked the program because they thought the information was useful (27.7%), other members were friendly (16.1%), and the group was a safe place (13.8%). While many responded that there was nothing they did not like about the program (37.6%), nearly one-third (31.9%) reported disliking the program because it appeared to be too much in favor of vaccination and because other members came across as rude (7.1%). Those with conservative political views were less likely to be satisfied with the group (P = .04). CONCLUSION: Facebook groups represent an acceptable way to engage participants to improve vaccination against COVID-19. Some aspects of the Facebook group could be improved for future iterations.

Empathic Engagement With the COVID-19 Vaccine Hesitant in Private Facebook Groups: A Randomized Trial.

BACKGROUND: Vaccine misinformation has been widely spread on social media, but attempts to combat it have not taken advantage of the attributes of social media platforms for health education. METHODS: The objective was to test the efficacy of moderated social media discussions about COVID-19 vaccines in private Facebook groups. Unvaccinated U.S. adults were recruited using Amazon's Mechanical Turk and randomized. In the intervention group, moderators posted two informational posts per day for 4 weeks and engaged in relationship-building interactions with group members. In the control group, participants received a referral to Facebook's COVID-19 Information Center. Follow-up surveys with participants (N = 478) were conducted 6 weeks post-enrollment. RESULTS: At 6 weeks follow-up, no differences were found in vaccination rates. Intervention participants were more likely to show improvements in their COVID-19 vaccination intentions (vs. stay same or decline) compared with control (p = .03). They also improved more in their intentions to encourage others to vaccinate for COVID-19. There were no differences in COVID-19 vaccine confidence or intentions between groups. General vaccine and responsibility to vaccinate were higher in the intervention compared with control. Most participants in the intervention group reported high levels of satisfaction. Participants engaged with content (e.g., commented, reacted) 11.8 times on average over the course of 4 weeks. CONCLUSIONS: Engaging with vaccine-hesitant individuals in private Facebook groups improved some COVID-19 vaccine-related beliefs and represents a promising strategy.

Correction: Twitter and Facebook posts about COVID-19 are less likely to spread misinformation compared to other health topics.

[This corrects the article DOI: 10.1371/journal.pone.0261768.].