Analyzing Reddit Forums Specific to Abortion That Yield Diverse Dialogues Pertaining to Medical Information Seeking and Personal Worldviews: Data Mining and Natural Language Processing Comparative Study.

BACKGROUND: Attitudes toward abortion have historically been characterized via dichotomized labels, yet research suggests that these labels do not appropriately encapsulate beliefs on abortion. Rather, contexts, circumstances, and lived experiences often shape views on abortion into more nuanced and complex perspectives. Qualitative data have also been shown to underpin belief systems regarding abortion. Social media, as a form of qualitative data, could reveal how attitudes toward abortion are communicated publicly in web-based spaces. Furthermore, in some cases, social media can also be leveraged to seek health information. OBJECTIVE: This study applies natural language processing and social media mining to analyze Reddit (Reddit, Inc) forums specific to abortion, including r/Abortion (the largest subreddit about abortion) and r/AbortionDebate (a subreddit designed to discuss and debate worldviews on abortion). Our analytical pipeline intends to identify potential themes within the data and the affect from each post. METHODS: We applied a neural network-based topic modeling pipeline (BERTopic) to uncover themes in the r/Abortion (n=2151) and r/AbortionDebate (n=2815) subreddits. After deriving the optimal number of topics per subreddit using an iterative coherence score calculation, we performed a sentiment analysis using the Valence Aware Dictionary and Sentiment Reasoner to assess positive, neutral, and negative affect and an emotion analysis using the Text2Emotion lexicon to identify potential emotionality per post. Differences in affect and emotion by subreddit were compared. RESULTS: The iterative coherence score calculation revealed 10 topics for both r/Abortion (coherence=0.42) and r/AbortionDebate (coherence=0.35). Topics in the r/Abortion subreddit primarily centered on information sharing or offering a source of social support; in contrast, topics in the r/AbortionDebate subreddit centered on contextualizing shifting or evolving views on abortion across various ethical, moral, and legal domains. The average compound Valence Aware Dictionary and Sentiment Reasoner scores for the r/Abortion and r/AbortionDebate subreddits were 0.01 (SD 0.44) and -0.06 (SD 0.41), respectively. Emotionality scores were consistent across the r/Abortion and r/AbortionDebate subreddits; however, r/Abortion had a marginally higher average fear score of 0.36 (SD 0.39). CONCLUSIONS: Our findings suggest that people posting on abortion forums on Reddit are willing to share their beliefs, which manifested in diverse ways, such as sharing abortion stories including how their worldview changed, which critiques the value of dichotomized abortion identity labels, and information seeking. Notably, the style of discourse varied significantly by subreddit. r/Abortion was principally leveraged as an information and outreach source; r/AbortionDebate largely centered on debating across various legal, ethical, and moral abortion domains. Collectively, our findings suggest that abortion remains an opaque yet politically charged issue for people and that social media can be leveraged to understand views and circumstances surrounding abortion.

Digital Epidemiology of Prescription Drug References on X (Formerly Twitter): Neural Network Topic Modeling and Sentiment Analysis.

BACKGROUND: Data from the social media platform X (formerly Twitter) can provide insights into the types of language that are used when discussing drug use. In past research using latent Dirichlet allocation (LDA), we found that tweets containing "street names" of prescription drugs were difficult to classify due to the similarity to other colloquialisms and lack of clarity over how the terms were used. Conversely, "brand name" references were more amenable to machine-driven categorization. OBJECTIVE: This study sought to use next-generation techniques (beyond LDA) from natural language processing to reprocess X data and automatically cluster groups of tweets into topics to differentiate between street- and brand-name data sets. We also aimed to analyze the differences in emotional valence between the 2 data sets to study the relationship between engagement on social media and sentiment. METHODS: We used the Twitter application programming interface to collect tweets that contained the street and brand name of a prescription drug within the tweet. Using BERTopic in combination with Uniform Manifold Approximation and Projection and k-means, we generated topics for the street-name corpus (n=170,618) and brand-name corpus (n=245,145). Valence Aware Dictionary and Sentiment Reasoner (VADER) scores were used to classify whether tweets within the topics had positive, negative, or neutral sentiments. Two different logistic regression classifiers were used to predict the sentiment label within each corpus. The first model used a tweet's engagement metrics and topic ID to predict the label, while the second model used those features in addition to the top 5000 tweets with the largest term-frequency-inverse document frequency score. RESULTS: Using BERTopic, we identified 40 topics for the street-name data set and 5 topics for the brand-name data set, which we generalized into 8 and 5 topics of discussion, respectively. Four of the general themes of discussion in the brand-name corpus referenced drug use, while 2 themes of discussion in the street-name corpus referenced drug use. From the VADER scores, we found that both corpora were inclined toward positive sentiment. Adding the vectorized tweet text increased the accuracy of our models by around 40% compared with the models that did not incorporate the tweet text in both corpora. CONCLUSIONS: BERTopic was able to classify tweets well. As with LDA, the discussion using brand names was more similar between tweets than the discussion using street names. VADER scores could only be logically applied to the brand-name corpus because of the high prevalence of non-drug-related topics in the street-name data. Brand-name tweets either discussed drugs positively or negatively, with few posts having a neutral emotionality. From our machine learning models, engagement alone was not enough to predict the sentiment label; the added context from the tweets was needed to understand the emotionality of a tweet.

Toward more rigorous and informative nutritional epidemiology: The rational space between dismissal and defense of the status quo.

To date, nutritional epidemiology has relied heavily on relatively weak methods including simple observational designs and substandard measurements. Despite low internal validity and other sources of bias, claims of causality are made commonly in this literature. Nutritional epidemiology investigations can be improved through greater scientific rigor and adherence to scientific reporting commensurate with research methods used. Some commentators advocate jettisoning nutritional epidemiology entirely, perhaps believing improvements are impossible. Still others support only normative refinements. But neither abolition nor minor tweaks are appropriate. Nutritional epidemiology, in its present state, offers utility, yet also needs marked, reformational renovation. Changing the status quo will require ongoing, unflinching scrutiny of research questions, practices, and reporting-and a willingness to admit that "good enough" is no longer good enough. As such, a workshop entitled "Toward more rigorous and informative nutritional epidemiology: the rational space between dismissal and defense of the status quo" was held from July 15 to August 14, 2020. This virtual symposium focused on: (1) Stronger Designs, (2) Stronger Measurement, (3) Stronger Analyses, and (4) Stronger Execution and Reporting. Participants from several leading academic institutions explored existing, evolving, and new better practices, tools, and techniques to collaboratively advance specific recommendations for strengthening nutritional epidemiology.

Deep learning for topical trend discovery in online discourse about Pre-Exposure Prophylaxis (PrEP).

Pre-Exposure Prophylaxis (PrEP) interventions are increasingly prevalent on social media. These data can be mined for insights about PrEP that may not be as apparent in surveys including personal musings about PrEP and barriers/facilitators to PrEP uptake. This study explores online discourse about PrEP using an interdisciplinary public health and computational informatics approach. We collected (N = 4,020) tweets using Twitter's Application Programming Interface (API). These data underwent a three-step neural network/deep learning process to identify clusters within these tweets and relative similarity/dissimilarity between clusters. We identified 25 distinct clusters from our original collection of tweets. These clusters represent general information about PrEP, how PrEP is communicated among diverse groups, and potential pockets of misinformation and disinformation regarding PrEP. Specific clusters of interest include discussions of medication side effects, social perception of PrEP usage, and concerns with costs and barriers to access of PrEP interventions. Our approach revealed diverse ways PrEP is contextualized online. Importantly this information can be leveraged to identify points of possible intervention for disinformation and misinformation about PrEP.

Results and Methodological Implications of the Digital Epidemiology of Prescription Drug References Among Twitter Users: Latent Dirichlet Allocation (LDA) Analyses.

BACKGROUND: Social media is an important information source for a growing subset of the population and can likely be leveraged to provide insight into the evolving drug overdose epidemic. Twitter can provide valuable insight into trends, colloquial information available to potential users, and how networks and interactivity might influence what people are exposed to and how they engage in communication around drug use. OBJECTIVE: This exploratory study was designed to investigate the ways in which unsupervised machine learning analyses using natural language processing could identify coherent themes for tweets containing substance names. METHODS: This study involved harnessing data from Twitter, including large-scale collection of brand name (N=262,607) and street name (N=204,068) prescription drug-related tweets and use of unsupervised machine learning analyses (ie, natural language processing) of collected data with data visualization to identify pertinent tweet themes. Latent Dirichlet allocation (LDA) with coherence score calculations was performed to compare brand (eg, OxyContin) and street (eg, oxys) name tweets. RESULTS: We found people discussed drug use differently depending on whether a brand name or street name was used. Brand name categories often contained political talking points (eg, border, crime, and political handling of ongoing drug mitigation strategies). In contrast, categories containing street names occasionally referenced drug misuse, though multiple social uses for a term (eg, Sonata) muddled topic clarity. CONCLUSIONS: Content in the brand name corpus reflected discussion about the drug itself and less often reflected personal use. However, content in the street name corpus was notably more diverse and resisted simple LDA categorization. We speculate this may reflect effective use of slang terminology to clandestinely discuss drug-related activity. If so, straightforward analyses of digital drug-related communication may be more difficult than previously assumed. This work has the potential to be used for surveillance and detection of harmful drug use information. It also might be used for appropriate education and dissemination of information to persons engaged in drug use content on Twitter.

Misinformation and Public Health Messaging in the Early Stages of the Mpox Outbreak: Mapping the Twitter Narrative With Deep Learning.

BACKGROUND: Shortly after the worst of the COVID-19 pandemic, an outbreak of mpox introduced another critical public health emergency. Like the COVID-19 pandemic, the mpox outbreak was characterized by a rising prevalence of public health misinformation on social media, through which many US adults receive and engage with news. Digital misinformation continues to challenge the efforts of public health officials in providing accurate and timely information to the public. We examine the evolving topic distributions of social media narratives during the mpox outbreak to map the tension between rapidly diffusing misinformation and public health communication. OBJECTIVE: This study aims to observe topical themes occurring in a large-scale collection of tweets about mpox using deep learning. METHODS: We leveraged a data set comprised of all mpox-related tweets that were posted between May 7, 2022, and July 23, 2022. We then applied Sentence Bidirectional Encoder Representations From Transformers (S-BERT) to the content of each tweet to generate a representation of its content in high-dimensional vector space, where semantically similar tweets will be located closely together. We projected the set of tweet embeddings to a 2D map by applying principal component analysis and Uniform Manifold Approximation Projection (UMAP). Finally, we group these data points into 7 topical clusters using k-means clustering and analyze each cluster to determine its dominant topics. We analyze the prevalence of each cluster over time to evaluate longitudinal thematic changes. RESULTS: Our deep-learning pipeline revealed 7 distinct clusters of content: (1) cynicism, (2) exasperation, (3) COVID-19, (4) men who have sex with men, (5) case reports, (6) vaccination, and (7) World Health Organization (WHO). Clusters that largely communicated erroneous or irrelevant information began earlier and grew faster, reaching a wider audience than later communications by official instances and health officials. CONCLUSIONS: Within a few weeks of the first reported mpox cases, an avalanche of mostly false, misleading, irrelevant, or damaging information started to circulate on social media. Official institutions, including the WHO, acted promptly, providing case reports and accurate information within weeks, but were overshadowed by rapidly spreading social media chatter. Our results point to the need for real-time monitoring of social media content to optimize responses to public health emergencies.

Using Natural Language Processing to Explore "Dry January" Posts on Twitter: Longitudinal Infodemiology Study.

BACKGROUND: Dry January, a temporary alcohol abstinence campaign, encourages individuals to reflect on their relationship with alcohol by temporarily abstaining from consumption during the month of January. Though Dry January has become a global phenomenon, there has been limited investigation into Dry January participants' experiences. One means through which to gain insights into individuals' Dry January-related experiences is by leveraging large-scale social media data (eg, Twitter chatter) to explore and characterize public discourse concerning Dry January. OBJECTIVE: We sought to answer the following questions: (1) What themes are present within a corpus of tweets about Dry January, and is there consistency in the language used to discuss Dry January across multiple years of tweets (2020-2022)? (2) Do unique themes or patterns emerge in Dry January 2021 tweets after the onset of the COVID-19 pandemic? and (3) What is the association with tweet composition (ie, sentiment and human-authored vs bot-authored) and engagement with Dry January tweets? METHODS: We applied natural language processing techniques to a large sample of tweets (n=222,917) containing the term "dry january" or "dryjanuary" posted from December 15 to February 15 across three separate years of participation (2020-2022). Term frequency inverse document frequency, k-means clustering, and principal component analysis were used for data visualization to identify the optimal number of clusters per year. Once data were visualized, we ran interpretation models to afford within-year (or within-cluster) comparisons. Latent Dirichlet allocation topic modeling was used to examine content within each cluster per given year. Valence Aware Dictionary and Sentiment Reasoner sentiment analysis was used to examine affect per cluster per year. The Botometer automated account check was used to determine average bot score per cluster per year. Last, to assess user engagement with Dry January content, we took the average number of likes and retweets per cluster and ran correlations with other outcome variables of interest. RESULTS: We observed several similar topics per year (eg, Dry January resources, Dry January health benefits, updates related to Dry January progress), suggesting relative consistency in Dry January content over time. Although there was overlap in themes across multiple years of tweets, unique themes related to individuals' experiences with alcohol during the midst of the COVID-19 global pandemic were detected in the corpus of tweets from 2021. Also, tweet composition was associated with engagement, including number of likes, retweets, and quote-tweets per post. Bot-dominant clusters had fewer likes, retweets, or quote tweets compared with human-authored clusters. CONCLUSIONS: The findings underscore the utility for using large-scale social media, such as discussions on Twitter, to study drinking reduction attempts and to monitor the ongoing dynamic needs of persons contemplating, preparing for, or actively pursuing attempts to quit or cut down on their drinking.

Multifaceted Scoping Review of Black/African American Transportation and Land Use Expert Recommendations on Activity-Friendly Routes to Everyday Destinations.

Promoting physical activity (PA) at the community level is a complex, multisector approach requiring researchers and practitioners to impact the individual, interpersonal, environment, and policy levels. One such strategy aiming to impact systems, policies, and environments is the Centers for Disease Control and Prevention's Activity Friendly Routes to Everyday Destinations (Routes to Destinations). This strategy specifically aims to connect pedestrian, bicycle, and public transportation systems with built environment and land use destinations. This article examines Black/African American transportation and land use experts' perspectives and concerns-across multiple mediums-around inequities that have discouraged PA among Black/African American persons specifically Routes to Destinations strategies. In March 2021, a multifaceted scoping review was conducted of peer-reviewed literature, gray literature, and social media authored by Black/African American transportation and land use experts focusing on policy, system, and environmental changes which promote or discourage equitable and inclusive access to physical activity. Themes from peer-reviewed and gray literature resources included: (1) Assessing Racism, Discrimination, and Segregation; (2) Addressing Equity and Inclusion Through Policy; (3) Community Engagement and Place-Based Interventions; (4) Infrastructure Changes; (5) Safety; and (6) Reporting Health Disparities. Twitter topic models suggested the main topics included elements of race/racism, equity, safety, infrastructure, and advancing social justice. Experts called for systemic and systematic change through new policies and implementation of existing policies as well as enhanced community inclusion in decision-making through ownership of policy and built environment change. Safety was discussed differently between peer-reviewed and gray literature and Twitter discussions indicating a publication bias.

On Mining Words: The Utility of Topic Models in Health Education Research and Practice.

Written language is the primary means by which scientific research findings are disseminated. Yet in the era of information overload, dissemination of a field of research may require additional efforts given the sheer volume of material available on any specific topic. Topic models are unsupervised natural language processing methods that analyze nonnumeric data (i.e., text data) in abundance. These tools aggregate, and make sense of, those data making them interpretable to interested audiences. In this perspective piece, we briefly describe topic models, including their purpose, function, and applicability for health education researchers and practitioners. We note how topic models can be applied in several contexts, including social media-based analyses, and mapping trends in scientific literature over time. As a tool for studying words, and patterns of words, topic models stand to improve our understanding of events prior and those occurring in the moment and help us look ahead into the future.

Social Media Insights Into US Mental Health During the COVID-19 Pandemic: Longitudinal Analysis of Twitter Data.

BACKGROUND: The COVID-19 pandemic led to unprecedented mitigation efforts that disrupted the daily lives of millions. Beyond the general health repercussions of the pandemic itself, these measures also present a challenge to the world's mental health and health care systems. Considering that traditional survey methods are time-consuming and expensive, we need timely and proactive data sources to respond to the rapidly evolving effects of health policy on our population's mental health. Many people in the United States now use social media platforms such as Twitter to express the most minute details of their daily lives and social relations. This behavior is expected to increase during the COVID-19 pandemic, rendering social media data a rich field to understand personal well-being. OBJECTIVE: This study aims to answer three research questions: (1) What themes emerge from a corpus of US tweets about COVID-19? (2) To what extent did social media use increase during the onset of the COVID-19 pandemic? and (3) Does sentiment change in response to the COVID-19 pandemic? METHODS: We analyzed 86,581,237 public domain English language US tweets collected from an open-access public repository in three steps. First, we characterized the evolution of hashtags over time using latent Dirichlet allocation (LDA) topic modeling. Second, we increased the granularity of this analysis by downloading Twitter timelines of a large cohort of individuals (n=354,738) in 20 major US cities to assess changes in social media use. Finally, using this timeline data, we examined collective shifts in public mood in relation to evolving pandemic news cycles by analyzing the average daily sentiment of all timeline tweets with the Valence Aware Dictionary and Sentiment Reasoner (VADER) tool. RESULTS: LDA topics generated in the early months of the data set corresponded to major COVID-19-specific events. However, as state and municipal governments began issuing stay-at-home orders, latent themes shifted toward US-related lifestyle changes rather than global pandemic-related events. Social media volume also increased significantly, peaking during stay-at-home mandates. Finally, VADER sentiment analysis scores of user timelines were initially high and stable but decreased significantly, and continuously, by late March. CONCLUSIONS: Our findings underscore the negative effects of the pandemic on overall population sentiment. Increased use rates suggest that, for some, social media may be a coping mechanism to combat feelings of isolation related to long-term social distancing. However, in light of the documented negative effect of heavy social media use on mental health, social media may further exacerbate negative feelings in the long-term for many individuals. Thus, considering the overburdened US mental health care structure, these findings have important implications for ongoing mitigation efforts.

Does Religiosity Delay Adolescent Alcohol Initiation? A Long-Term Analysis (2008-2015) of Nationally Representatives Sample of 12th Graders.

Objective: Underage alcohol consumption is associated with deleterious consequences, with earlier initiation leading to increased likelihood of alcohol misuse and dependence later in life. Religiosity represents a protective factor, such that those with increased religiosity delay alcohol initiation. Herein, we test the association between religiosity and alcohol initiation across several distinct national samples of high school seniors in the United States. Method: To assess long-term associations between alcohol initiation and religiosity, we utilized latent growth curve modeling and simple mean plots to conduct a secondary data analysis on 8 years (2008-2015) of the Monitoring the Future Survey (n = 20,099). Results: When compared with the baseline model, which posited a consistent age of initiation of approximately 9th grade χ2 (n = 18,224, df = 31) = 33.70, p <.34, CFI = .000, TLI = 1.00, RMSEA = .006 (90% CI: .00, .017), religiosity plays an equally consistent role in delaying age of initiation by two grade levels, or three calendar years χ2 (n = 17,978, df = 159.116) = 159.17, p<.0001, CFI = .848, TLI = .834, RMSEA = .017 (90% CI: .00, .03). When means were parsed out by religiosity level and gender, religion was a stronger protector against alcohol age of initiation for females than males. These associations were constant over the 8-year period and across multiple nationally representative samples of adolescents. Conclusions: Religiosity delays alcohol initiation for adolescents. Prevention programs should seek to identify which components of religiosity are most impactful, and subsequently develop and incorporate programmatic aspects that leverage these factors.

Social Media Discourse Related to Caregiving for Older Adults Living With Alzheimer Disease and Related Dementias: Computational and Qualitative Study.

BACKGROUND: In the United States, caregivers of people living with Alzheimer disease and Alzheimer disease-related dementias (AD/ADRD) provide >16 billion hours of unpaid care annually. These caregivers experience high levels of stress and burden related to the challenges associated with providing care. Social media is an emerging space for individuals to seek various forms of support. OBJECTIVE: We aimed to explore the primary topics of conversation on the social media site Reddit related to AD/ADRD. We then aimed to explore these topics in depth, specifically examining elements of social support and behavioral symptomology discussed by users. METHODS: We first generated an unsupervised topic model from 6563 posts made to 2 dementia-specific subreddit forums (r/Alzheimers and r/dementia). Then, we conducted a manual qualitative content analysis of a random subset of these data to further explore salient themes in the corpus. RESULTS: The topic model with the highest overall coherence score (0.38) included 10 topics, including caregiver burden, anxiety, support-seeking, and AD/ADRD behavioral symptomology. Qualitative analyses provided added context, wherein users sought emotional and informational support for many aspects of the care experience, including assistance in making key care-related decisions. Users expressed challenging and complex emotions on Reddit, which may be taboo to express in person. CONCLUSIONS: Reddit users seek many different forms of support, including emotional and specific informational support, from others on the internet. Users expressed a variety of concerns, challenges, and behavioral symptoms to manage as part of the care experience. The unique (ie, anonymous and moderated) nature of the forum allowed for a safe space to express emotions free from documented caregiver stigma. Additional support structures are needed to assist caregivers of people living with AD/ADRD.

Negative affect variability differs between anxiety and depression on social media.

OBJECTIVE: Negative affect variability is associated with increased symptoms of internalizing psychopathology (i.e., depression, anxiety). The Contrast Avoidance Model (CAM) suggests that individuals with anxiety avoid negative emotional shifts by maintaining pathological worry. Recent evidence also suggests that the CAM can be applied to major depression and social phobia, both characterized by negative affect changes. Here, we compare negative affect variability between individuals with a variety of anxiety and depression diagnoses by measuring the levels and degree of change in the sentiment of their online communications. METHOD: Participants were 1,853 individuals on Twitter who reported that they had been clinically diagnosed with an anxiety disorder (A cohort, n = 896) or a depressive disorder (D cohort, n = 957). Mean negative affect (NA) and negative affect variability were calculated using the Valence Aware Dictionary for Sentiment Reasoning (VADER), an accurate sentiment analysis tool that scores text in terms of its negative affect content. RESULTS: Findings showed differences in negative affect variability between the D and A cohort, with higher levels of NA variability in the D cohort than the A cohort, U = 367210, p < .001, r = 0.14, d = 0.25. Furthermore, we found that A and D cohorts had different average NA, with the D cohort showing higher NA overall, U = 377368, p < .001, r = 0.12, d = 0.21. LIMITATIONS: Our sample is limited to individuals who disclosed their diagnoses online, which may involve bias due to self-selection and stigma. Our sentiment analysis of online text may not completely capture all nuances of individual affect. CONCLUSIONS: Individuals with depression diagnoses showed a higher degree of negative affect variability compared to individuals with anxiety disorders. Our findings support the idea that negative affect variability can be measured using computational approaches on large-scale social media data and that social media data can be used to study naturally occurring mental health effects at scale.

A theory-based assessment of mpox: Findings from a nationally representative survey of U.S. adults.

The purpose of this research was to examine individual differences related to fear of, perceived susceptibility to, and perceived severity of mpox as well as mpox knowledge, fear, perceived susceptibility, and perceived severity as predictors of vaccine intention in a national survey of U.S. adults (aged ≥18 years). Address-based sampling (ABS) methods were used to ensure full coverage of all households in the nation, reflecting the 2021 March Supplement of the Current Population Survey. Internet-based surveys were self-administered by Ipsos between September 16-26, 2022. N = 1018 participants completed the survey. The survey included items, based partially on the Health Belief Model, assessing vaccine intention (1 item; responses from 1 [Definitely not] to 5 [Definitely]), fear of mpox (7-item scale; α = .89; theoretical mean = 7-35), perceived susceptibility to mpox (3-item scale; α = .85; theoretical mean = 3-15), and perceived severity of mpox (4-item scale; α = .65; theoretical mean = 4-20). Higher scores indicate greater fear, susceptibility, and severity. One-way ANOVAs were run to examine mean score differences by demographic groups (e.g., gender, race/ethnicity, sexual orientation), and multiple regression analyses assessed the relationship between predictors (mpox knowledge, susceptibility/severity, fear) and a single outcome (vaccination intention), while controlling for demographic covariates. Sampling weights were applied to all analyses. Only 1.8% (n = 18) of respondents reported having received the mpox vaccine. While mpox vaccine intention was low (M = 2.09, SD = 0.99), overall differences between racial/ethnic, sexual orientation, education, and household income groups were statistically significant. Fear of mpox was very low (M = 13.13, SD = 5.33), and there were overall statistically significant differences in both fear and perceived severity among gender, race/ethnicity, sexual orientation, education, and household income groups. While respondents reported not feeling very susceptible to mpox (M = 5.77, SD = 2.50), they generally rated mpox as just above the theoretical mean in terms of severity (M = 11.01, SD = 2.85). Mpox knowledge, fear, severity, and susceptibility, as well as race/ethnicity, were all statistically significant predictors of intention to vaccinate, with susceptibility representing the strongest predictor. Overall, Americans' vaccination for mpox/vaccine intent was low. Gay/lesbian and racial/ethnic minority respondents felt more susceptible to and viewed mpox more severely, compared with heterosexual and White respondents, respectively. These data may be used to tailor risk and prevention (e.g., vaccination) interventions, as cases continue to surge in the current global mpox outbreak. Greater perceptions of susceptibility, severity, and fear about mpox exist largely among minority populations. While public health messaging to promote mpox vaccination can focus on improving knowledge, as well as addressing fear and perceived severity of, and susceptibility to, mpox, such messages should be carefully crafted to prevent disproportionate negative effects on marginalized communities.

Misstatements, misperceptions, and mistakes in controlling for covariates in observational research.

We discuss 12 misperceptions, misstatements, or mistakes concerning the use of covariates in observational or nonrandomized research. Additionally, we offer advice to help investigators, editors, reviewers, and readers make more informed decisions about conducting and interpreting research where the influence of covariates may be at issue. We primarily address misperceptions in the context of statistical management of the covariates through various forms of modeling, although we also emphasize design and model or variable selection. Other approaches to addressing the effects of covariates, including matching, have logical extensions from what we discuss here but are not dwelled upon heavily. The misperceptions, misstatements, or mistakes we discuss include accurate representation of covariates, effects of measurement error, overreliance on covariate categorization, underestimation of power loss when controlling for covariates, misinterpretation of significance in statistical models, and misconceptions about confounding variables, selecting on a collider, and p value interpretations in covariate-inclusive analyses. This condensed overview serves to correct common errors and improve research quality in general and in nutrition research specifically.

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

BACKGROUND: Misinformation is known to affect norms, attitudes, and intentions to engage with healthy behaviors. Evidence strongly supports that Spanish speakers may be particularly affected by misinformation and its outcomes, yet current insights into the scope and scale of misinformation is primarily ethnocentric, with greater emphasis on English-language design. OBJECTIVE: This study applies Natural Language Processing (NLP) to analyze a corpus of English/Spanish tweets about vaccines, broadly defined, for misinformation indicators. METHODS: We analyzed NEnglish = 247,140 and NSpanish = 104,445 tweets using Latent Dirichlet Allocation (LDA) topic models with Coherence score calculation (model fit) with a Mallet adjustment (topic optimization). We used informal coding to name computer-identified topics and compare misinformation scope and scale between languages. RESULTS: The LDA analysis yielded a 12-topic solution for English and a 14-topic solution for Spanish. Both corpora contained overlapping misinformation, including uncertainty of research guiding policy recommendations or standing in support of antivax movements. However, the Spanish data were positioned in a global context, where misinformation was directed at government equity and disparate vaccine distribution. CONCLUSION: Our findings support that misinformation is a global issue. However, misinformation may vary depending on culture and language. As such, tailored strategies to combat misinformation in digital planes are strongly encouraged.

"I Haven't Been Diagnosed, but I Should Be"-Insight Into Self-diagnoses of Common Mental Health Disorders: Cross-sectional Study.

BACKGROUND: In recent years, social media has become a rich source of mental health data. However, there is a lack of web-based research on the accuracy and validity of self-reported diagnostic information available on the web. OBJECTIVE: An analysis of the degree of correspondence between self-reported diagnoses and clinical indicators will afford researchers and clinicians higher levels of trust in social media analyses. We hypothesized that self-reported diagnoses would correspond to validated disorder-specific severity questionnaires across 2 large web-based samples. METHODS: The participants of study 1 were 1123 adults from a national Qualtrics panel (mean age 34.65, SD 12.56 years; n=635, 56.65% female participants,). The participants of study 2 were 2237 college students from a large university in the Midwest (mean age 19.08, SD 2.75 years; n=1761, 75.35% female participants). All participants completed a web-based survey on their mental health, social media use, and demographic information. Additionally, the participants reported whether they had ever been diagnosed with a series of disorders, with the option of selecting "Yes"; "No, but I should be"; "I don't know"; or "No" for each condition. We conducted a series of ANOVA tests to determine whether there were differences among the 4 diagnostic groups and used post hoc Tukey tests to examine the nature of the differences. RESULTS: In study 1, for self-reported mania (F3,1097=2.75; P=.04), somatic symptom disorder (F3,1060=26.75; P<.001), and alcohol use disorder (F3,1097=77.73; P<.001), the pattern of mean differences did not suggest that the individuals were accurate in their self-diagnoses. In study 2, for all disorders but bipolar disorder (F3,659=1.43; P=.23), ANOVA results were consistent with our expectations. Across both studies and for most conditions assessed, the individuals who said that they had been diagnosed with a disorder had the highest severity scores on self-report questionnaires, but this was closely followed by individuals who had not been diagnosed but believed that they should be diagnosed. This was especially true for depression, generalized anxiety, and insomnia. For mania and bipolar disorder, the questionnaire scores did not differentiate individuals who had been diagnosed from those who had not. CONCLUSIONS: In general, if an individual believes that they should be diagnosed with an internalizing disorder, they are experiencing a degree of psychopathology similar to those who have already been diagnosed. Self-reported diagnoses correspond well with symptom severity on a continuum and can be trusted as clinical indicators, especially in common internalizing disorders such as depression and generalized anxiety disorder. Researchers can put more faith into patient self-reports, including those in web-based experiments such as social media posts, when individuals report diagnoses of depression and anxiety disorders. However, replication and further study are recommended.

Comment on Marsigliante et al. Effects on Children's Physical and Mental Well-Being of a Physical-Activity-Based School Intervention Program: A Randomized Study. Int. J. Environ. Res. Public Health 2023, 20, 1927.

We conducted a critical review of the article "Effects on Children's Physical and Mental Well-Being of a Physical-Activity-Based School Intervention Program: A Randomized Study", published in the International Journal of Environmental Research and Public Health in 2023 as part of the Special Issue "Psychomotricity and Physical Education in School Health". We identified multiple mistakes in the statistical analyses applied. First, the authors claim to have found a statistically significant association between the proposed intervention and change in body composition (body mass index (BMI) percentiles, relative fat mass, and BMI classes) by way of exhibiting differences in nominal significance between the pre- and post-intervention changes within the control and intervention groups, instead of exhibiting a significant difference between groups. Furthermore, the analysis described fails to account for clustering and nesting in the data. The reporting of the statistical methods and results include multiple elements that are variously incorrect, incoherent, or impossible. Revised statistical analyses are proposed which can render the study's methods valid and its results substantiated, whereas the current methods and results are invalid and unsubstantiated, respectively.

Neutral or Framed? A Sentiment Analysis of 2019 Abortion Laws.

Introduction: This study employs sentiment analysis (SA) to examine the semantic structures of restrictive and protective abortion bills enacted in 2019. SA is a Natural Language Processing (NLP) technique that uses automation to extract affective indicators (emotive language) from text data. Assessing these indicators can help identify whether legal texts are framed, or intentionally biased in their wording. Identifying framing is important for understanding potentially biased interpretations of these laws. Methods: We identified a sample of 2019 abortion bills using the legislative tracking tool Legiscan and included those that met specified criteria (N = 19 bills). We categorized each bill as restrictive (n = 12) or protective (n = 7). We ran aggregate (i.e., all bills) and separate (protective × restrictive) SA, generating scores that we interpreted qualitatively (higher scores indicated predominance of positive wording). Results: In the aggregate analysis, 56% of text comprised negative terms (44% positive). Restrictive bills contained more negative language than protective bills (67% vs 58%). Although SA scores varied from -222 to +13, two laws scored 0, indicating neutrality. For comparison, the US Constitution's score equaled 1. Conclusion: Our findings confirm SA is useful to examine legal documents for language biases. The abortion bills we assessed seem framed along political ideologies, although the sample provided evidence that neutral wording is possible. Policy Implications: With the recent additions of conservative-leaning Justices to the US Supreme Court, Roe v. Wade is again at the center of partisan conflict. Thus, how abortion laws are framed draws further implications for how they may be interpreted when challenged in the court system.