Geospatial vaccine misinformation risk on social media: Online insights from an English/Spanish natural language processing (NLP) analysis of vaccine-related tweets

Effects of childhood environments on the discernment of health misinformation

The wide dissemination of COVID-19 and other health misinformation poses a significant threat to individuals' well-being. We investigated how two key features of childhood environments, uncertainty and harshness, influence individuals' ability to distinguish COVID-19 and other health-related truths from misinformation (i.e., accuracy discernment and sharing discernment). Across four studies (including two preregistered studies, total N = 4874), we found that greater childhood uncertainty was associated with worse accuracy discernment and sharing discernment, whereas greater childhood harshness was associated with better accuracy discernment. We also found that the associations between childhood environments and discernment were mediated by analytic thinking (Studies 1-3). Furthermore, recalling or imagining uncertain childhood events led to a decrease in sharing discernment (Study 4). These findings offer insight into how childhood environments influence the ability to discern truth from falsehood on social media later in life, which may contribute to the establishment of corresponding interventions to combat the negative impact of misinformation on public health.

Context matters: How to research vaccine attitudes and uptake after the COVID-19 crisis

The pandemic dramatically accelerated research on vaccine attitudes and uptake, a field which mobilizes researchers from the social sciences and humanities as well as biomedical and public health disciplines. The field has the potential to contribute much more, but the growth in research and the deeper connections between disciplines brings challenges as well as opportunities. This perspective article assesses the recent development of the field, exploring progress whilst emphasizing that not enough attention has been paid to national and local contexts. This lack of contextual attention limits the progress of research and hinders our capacity to learn from the COVID-19 crisis. We suggest three concrete responses: building and recognizing new publishing formats for reporting and synthesizing studies at a country level; establishing country-level interdisciplinary networks to connect research and praxis; and strengthening international comparative survey work by enhancing the focus on local contextual factors.

The CareVirtue Digital Journal for Family and Friend Caregivers of People Living With Alzheimer Disease and Related Dementias: Exploratory Topic Modeling and User Engagement Study

BACKGROUND

As Alzheimer disease (AD) and AD-related dementias (ADRD) progress, individuals increasingly require assistance from unpaid, informal caregivers to support them in activities of daily living. These caregivers may experience high levels of financial, mental, and physical strain associated with providing care. CareVirtue is a web-based tool created to connect and support multiple individuals across a care network to coordinate care activities and share important information, thereby reducing care burden.

OBJECTIVE

This study aims to use a computational informatics approach to thematically analyze open text written by AD/ADRD caregivers in the CareVirtue platform. We then explore relationships between identified themes and use patterns.

METHODS

We analyzed journal posts (n=1555 posts; 170,212 words) generated by 51 unique users of the CareVirtue platform. Latent themes were identified using a neural network approach to topic modeling. We calculated a sentiment score for each post using the Valence Aware Dictionary and Sentiment Reasoner. We then examined relationships between identified topics; semantic sentiment; and use-related data, including post word count and self-reported mood.

RESULTS

We identified 5 primary topics in users' journal posts, including descriptions of specific events, professional and medical care, routine daily activities, nighttime symptoms, and bathroom/toileting issues. This 5-topic model demonstrated adequate fit to the data, having the highest coherence score (0.41) among those tested. We observed group differences across these topics in both word count and semantic sentiment. Further, posts made in the evening were both longer and more semantically positive than other times of the day.

CONCLUSIONS

Users of the CareVirtue platform journaled about a variety of different topics, including generalized experiences and specific behavioral symptomology of AD/ADRD, suggesting a desire to record and share broadly across the care network. Posts were the most positive in the early evening when the tool was used habitually, rather than when writing about acute events or symptomology. We discuss the value of embedding informatics-based tools into digital interventions to facilitate real-time content delivery.

Accuracy of a large language model in distinguishing anti- and pro-vaccination messages on social media: The case of human papillomavirus vaccination

Objective

Vaccination has engendered a spectrum of public opinions, with social media acting as a crucial platform for health-related discussions. The emergence of artificial intelligence technologies, such as large language models (LLMs), offers a novel opportunity to efficiently investigate public discourses. This research assesses the accuracy of ChatGPT, a widely used and freely available service built upon an LLM, for sentiment analysis to discern different stances toward Human Papillomavirus (HPV) vaccination.

Methods

Messages related to HPV vaccination were collected from social media supporting different message formats: Facebook (long format) and Twitter (short format). A selection of 1,000 human-evaluated messages was input into the LLM, which generated multiple response instances containing its classification results. Accuracy was measured for each message as the level of concurrence between human and machine decisions, ranging between 0 and 1.

Results

Average accuracy was notably high when 20 response instances were used to determine the machine decision of each message: .882 (SE = .021) and .750 (SE = .029) for anti- and pro-vaccination long-form; .773 (SE = .027) and .723 (SE = .029) for anti- and pro-vaccination short-form, respectively. Using only three or even one instance did not lead to a severe decrease in accuracy. However, for long-form messages, the language model exhibited significantly lower accuracy in categorizing pro-vaccination messages than anti-vaccination ones.

Conclusions

ChatGPT shows potential in analyzing public opinions on HPV vaccination using social media content. However, understanding the characteristics and limitations of a language model within specific public health contexts remains imperative.

No time to lie: Examining the identity of pro-vaccination and anti-vaccination supporters through user-generated content

OBJECTIVE

This study delves into the social identity of pro-vaccination and anti-vaccination supporters, emphasizing an understanding of the values that shape these distinct identities. Furthermore, the research highlights that user-generated content pertaining to vaccines offers valuable insights into the underlying personal values of both pro-vaccination and anti-vaccination groups.

METHOD

We constructed a textual dataset based on 142,596 tweets. This data was analyzed in three steps. First, the linguistic characteristics of the textual data, together with the underlying personal values of the text creators, were identified using LIWC software. Second, the identified personal values were used as an input for the moderation analysis, which examined the relationship between personal values and social identity for pro- and anti-vaccination groups. Finally, an automated, in-depth text analysis was conducted in Mathematica to understand the narratives created by both groups.

RESULTS

The study findings indicate that both pro-vaccination and anti-vaccination supporters display characteristics of subcultures with distinct group identities. Consequently, based on the results, there is a need for more tailored public health communication strategies that address these two groups separately.

CONCLUSIONS

Understanding how users create health-related content based on their personal values is crucial. Acknowledging and appreciating the diverse personal values and identities within different groups in the vaccination discourse can inform health communication efforts, aligning these efforts with the specific values of each group. This targeted communication is vital for effectively conveying relevant peer-reviewed health information amid the abundance of health-related user-generated content.