Related Messages Research Articles

Mentor–Protégé Matching Regarding Communication Relates to Career Attitudes

In this study, we investigate the role of matching communication (i.e., relational messages received) in mentoring outcomes (mentor and protégé career attitudes). Specifically, we used data from a sample of 145 matched mentor–protégé dyads in a response surface analysis to show that matched relational messaging generally relates to mentors (and less consistently, protégés) reporting enhanced career satisfaction and career commitment. Furthermore, our findings are consistent with previous research showing that when relational messages (i.e., intimacy) or self-disclosure are matched at high or low (i.e., more extreme) levels, the mentor and protégé have the best outcomes. Additionally, beneficial mentor outcomes were maximized when levels of seriousness were matched at a moderate level. These results suggest that both levels of relational messaging, as well as the degree to which mentors and protégé match on these constructs influences mentoring outcomes. Study limitations, future directions for research, and implications for career development are discussed.

Categorizing E-cigarette-related tweets using BERT topic modeling

BackgroundSocial media platforms are critical channels for promoting e-cigarettes, particularly among youth, making analysis of their vast and diverse content essential for public health interventions. Prevalence rates of e-cigarette use are high and evidence suggests that social media are popular forums that promote e-cigarette use through direct and indirect marketing techniques. The volume and diverse nature of e-cigarette-related information on social media is challenging and may obfuscate public health prevention messaging. Traditional hand-coding methods are labor-intensive and limit scalability. In contrast, unsupervised machine learning approaches, such as topic modeling, allow for efficient analysis of large datasets, uncovering patterns and trends that manual methods cannot achieve at scale. The present study focused on ascertaining the extent to which themes and topics in tweets related to e-cigarettes can be successfully rendered into useful homogenous units using machine learning. A better understanding of current depictions and discussions around e-cigarette products and use on social media can inform public health counter messaging and policy interventions. MethodsWe used topic modeling (BERTopic) to iteratively derive vape-related tweet clusters and calculate the importance of particular words to these groupings. We conducted a qualitative content analysis to study clustered tweets. We also sought to determine the geographic locations of e-cigarette conversations using automated geoparsing methods, which translate toponyms in textual data into geographic identifiers, to attempt to infer the location of tweets. ResultsWe were able to successfully identify >100,000 tweets in broad thematic categories in English and Spanish. Our correlation and inter-topic map analysis of the machine-derived topics, which examines the relationships between topics, indicated that most of the topics were unique (correlation value < 0.5) and did not overlap with each other. We identified six topics: Flavors and Disposable Vapes, Cannabis, Vape Shops and Refillable Vapes, Vape Culture, Anti-vaping and Quitting, and Spanish Tweets and Vaping Nicotine. Further analysis of these topics using qualitative methods identified themes within each topic. For example, Category 6 (Spanish Tweets and Vaping Nicotine) included four topics focused on the health risks of vaping, personal motivations for vaping, and the regulation of vaping products. Using geoparsing, which automatically detects location information, we found that the United States had the highest number of tweets related to vaping. Discussion/conclusionResults underscore the possibility of leveraging BERTopic modeling to reduce large quantities of data to comprehensively describe and categorize myriad e-cigarette related messages to which social media users are exposed. This data reduction approach can be applied to various social media platforms to describe and categorize e-cigarette posts and thereby triangulate and validate findings. Thematic content analysis of the topics identified through this technique requires supervision and human inputs. Our approach provides a comprehensive understanding of the evolving e-cigarette discourse, informing public health counter-messaging and policy interventions. Moreover, findings support the need for regulation, such as reducing appealing flavors and suggest that social media can be used effectively to support public health messaging (i.e., quitting messages).

Body Type and Body Dissatisfaction in Instagram Posts by Dietetics and Nursing Students

Students in health professions seeking to establish professional credibility through their appearance could be at greater risk of body dissatisfaction. Instagram posts featuring #rd2be (n = 865) or #nursingstudent (n=1610) were reviewed for body type and body image messages. A greater proportion of #rd2be posts contained images of thin or muscular bodies and text with body image–related messages than #nursingstudent posts. Posts with #nursingstudent had a higher prevalence of text expressing dissatisfaction with weight, fat, or muscle than posts with #rd2be. Results suggest that nursing and dietetics students may grapple with body image while establishing their professional identity.

Mitigation of Adversarial Attacks in 5G Networks with a Robust Intrusion Detection System Based on Extremely Randomized Trees and Infinite Feature Selection

Intrusion Detection Systems (IDSs) are an important tool to mitigate cybersecurity threats in the ICT infrastructures. Preferable properties of the IDSs are the optimization of the attack detection accuracy and the minimization of the computing resources and time. A signification portion of IDSs presented in the research literature is based on Machine Learning (ML) and Deep Learning (DL) elements, but they may be prone to adversarial attacks, which may undermine the overall performance of the IDS algorithm. This paper proposes a novel IDS focused on the detection of cybersecurity attacks in 5G networks, which addresses in a simple but effective way two specific adversarial attacks: (1) tampering of the labeled set used to train the ML algorithm, (2) modification of the features in the training data set. The approach is based on the combination of two algorithms, which have been introduced recently in the research literature. The first algorithm is the Extremely Randomized Tree (ERT) algorithm, which enhances the capability of Decision Tree (DT) and Random Forest (RF) algorithms to perform classification in data sets, which are unbalanced and of large size as IDS data sets usually are (legitimate traffic messages are more numerous than attack related messages). The second algorithm is the recently introduced Infinite Feature Selection algorithm, which is used to optimize the choice of the hyper-parameter defined in the approach and improve the overall computing efficiency. The result of the application of the proposed approach on a recently published 5G IDS data set proves its robustness against adversarial attacks with different degrees of severity calculated as the percentage of the tampered data set samples.

The Role of e-Cigarette Packaging as a Health Communications Tool: A Focus Group Study With Adolescents and Adults in England and Scotland.

In the United Kingdom, e-cigarette and refill packaging must display a nicotine addiction warning. This study explored how this message is perceived, responses to alternative on-pack messages, and other options for using e-cigarette packaging to discourage youth and people who neither smoke nor use e-cigarettes while encouraging smokers to switch. Between August and September 2022, 16 focus groups (n = 70) were conducted to explore these topics with adolescents (n = 31, aged 11-17 years) and adults (n = 39, nonsmokers, smokers that use e-cigarettes, smokers that do not use e-cigarettes) in England and Scotland. While several participants thought the current nicotine addiction warning could help increase awareness of nicotine addiction, most reported that it failed to capture attention and was not a deterrent. Alternative messages shown on packs (about harm, toxicity, wellness, litter, or relative risk) received mixed responses. Relative risk messages were perceived as most beneficial for smokers switching but also thought to potentially encourage uptake among nonsmokers. Some participants considered certain harm and toxicity messages to potentially dissuade uptake. Participants proposed several ideas to reduce the appeal of e-cigarette packaging and devices to deter youth uptake, including more prominent warnings, standardized packaging, and devices that are plain or include health messages. Packaging can play a crucial role in communicating product and health messages to different consumer groups. Further consideration of how packaging and labeling can meet the needs of non-nicotine users while simultaneously reaching those who may benefit from using e-cigarettes to stop smoking is warranted. While some viewed the nicotine addiction warning required on e-cigarettes and refill packaging in the United Kingdom as helpful in raising awareness of nicotine addiction, it did not resonate with most of our sample of adolescents and adults. The findings suggest that e-cigarette packaging could be better used to encourage smokers to switch to a less harmful alternative, with relative risk messages showing promise. Furthermore, strengthening on-pack messaging (eg increasing salience and rotating messages) and reducing the appeal of packaging (eg drab colors) and devices (eg including warnings) may help increase awareness of e-cigarette harms while deterring use among adolescents and nonsmokers.

Men's Willingness to Receive Text Messages and Talk with an HIV counselor from the National HIV Hotline in Tanzania for Support with Linkage to Care Following HIV self-testing.

Tanzania faces a significant burden of HIV, with particular challenges in reaching men and ensuring timely linkage to care. To address these issues, HIV self-testing (HIVST) has been implemented to increase HIV testing and the National HIV Hotlines are being considered as a strategy to facilitate linkage to care. This study aimed to assess the willingness of Tanzanian men to receive support from use the National HIV Hotline via mobile phones for HIVST and linkage to care. Data from 505 men from the baseline survey of a cluster-randomized controlled trial conducted in June 2019 with 18 social networks or "camps" in Dar es Salaam, Tanzania. Participants were 18-year-old or older male camp members who were HIV-negative at the time of enrolment. Logistic regression models were used to assess factors associated with men's comfort with talking with an HIV counselor over the phone. There were 505 heterosexual male participants enrolled in the study with an average age of 29 years. Logistic regression demonstrated that comfortability texting a friend about HIV self-testing (OR =3.37, 95% CI [1.97 - 5.76], being comfortable texting a friend about HIV (OR = 3.84, 95% CI [2.20 - 6.72], previous history of receiving HIV related text messages (aOR = 0.55, 95% CI [0.31 - 0.99] were significantly associated with men's s comfortability talking to a HIV counselor on the National HIV Hotline following HIVST. The factors such as participants' comfortability texting friend about HIVST (OR = 2.52, 95% CI [1.49 - 4.25]) and comfortability texting friend about HIV (OR = 2.96, 95% CI [1.83 - 4.80] were significantly associated with the probability of participant's comfortability receiving text message from HIV counselor following HIVST. These findings suggest an effort to develop and implement a user-friendly digital health intervention that promote comfortability, address private concerns, and deliver tailored support and information to individuals following HIV self-testing.

Willingness to use PrEP among PrEP naïve men who have sex with men: a meta-analysis

ABSTRACT This study aimed to measure the pooled estimate of willingness to use HIV pre-exposure prophylaxis (PrEP) (WTUP) among PrEP-naïve United States (U.S.)-based men who have sex with men (MSM). PubMed, Embase, Web of Science, CINAHL, and PsycINFO were searched. The search strategy contained the keyword willingness and interest and the MeSH terms for HIV and PrEP. Articles were included if they were published between January 2005 and May 2022, reported quantitative data on WTUP among PrEP-naïve US-based MSM, and were available as full text in English. Meta-analysis was conducted to assess the pooled effect size of WTUP prevalence using a random-effects model, heterogeneity in the pooled estimate was assessed, and subgroup analyzes were conducted. Fifteen studies were included based on the inclusion and exclusion criteria. Meta-analysis revealed a pooled prevalence proportion for WTUP of 0.58 (95% CI 0.54–0.61) (or 58 out of 100) among PrEP-naïve MSM. High inter-study heterogeneity (Q = 548.10, df = 19, p < 0.01, I2 = 96.53, τ2 = 0.09) was observed. Age of the study sample and region where the data were collected significantly moderated the pooled WTUP estimate. Age-appropriate PrEP related messaging and a focus on HIV priority areas of the U.S. would be important strategies to improve WTUP among MSM in the U.S. moving forward.

The value of future focus and experiential ad appeals in strengthening the effect of brand-pandemic fit on eWOM

Since the outbreak of COVID-19 in March 2020 brands have quickly adapted the content of their advertising to include pandemic related social and health messages. This paper explores the relationship of fit between the advertised brand and the pandemic as a potential influence on consumers’ eWOM intentions in an advanced and emerging market. Inspired by metacognitive fluency theory, we propose and find that pandemic adapted advertisements for brands that are high on brand-pandemic fit enhance consumers’ eWOM intentions in both markets. This relationship is stronger for experiential ad appeals and future focus in the ads. In terms of temporal focus, building on construal level theory, we find that the effect of fit on eWOM is increased by using future focused ads. Together, the results establish relationships between brand-pandemic fit of advertisements, ad liking, ad-appeals, future focus, and eWOM intentions. We conclude with a discussion of how brand advertising can facilitate societal well-being as high fit brands help individuals solve pandemic related challenges and generate more consumer engagement via eWOM intentions.

Disentangled Relational Graph Neural Network with Contrastive Learning for knowledge graph completion

Learning disentangled entity representations has garnered significant attention in the field of knowledge graph completion (KGC). However, the existing methods inherently overlook the indicative role of relations and the correlation between latent factors and relations, leading to suboptimal entity representations for KGC tasks. In the current study, we introduce the Disentangled Relational Graph Neural Network with Contrastive Learning (DRGCL) method, designed to acquire disentangled entity representations guided by relations. In particular, we first devise the factor-aware relational message aggregation approach to learn entity representations under each semantic subspace and obtain latent factor representations by attention mechanisms. Subsequently, we propose a discrimination objective for factor-subspace pairs using a contrastive learning approach, which compels the factor representations to distinctly capture the information associated with different latent factors and promote the consistency between factor representations and semantic subspaces. Through disentanglement, our model can generate relation-aware scores tailored to the provided scenario. Extensive experiments have been conducted on three benchmark datasets and the results demonstrate the superiority of our method compared with strong baseline models.

Greenfluencing Through the Power of Emotions? Impact of Message Frames and Emotionally Matching Background Music on the Effectiveness of Influencers’ Environmental Communication

Social media influencers have become increasingly important in persuading people to become environmentally sensitive. As “greenfluencers” have been studied primarily in the context of advertising, it is crucial to investigate the mechanisms and effects of their non-product–related environmental messages. In two studies, we tested different message frames, a well-known persuasion strategy, in the context of Instagram reels (short audiovisual videos), where user-added background music is a prominent feature. In a 2 × 2 between-subjects online experiment ( N = 240), we manipulated the message frame (gain vs. loss) and the background music (positive vs. negative emotional valence) and found that loss frames reduced behavioral intention via negative emotions. A second 2 × 2 between-subjects online experiment ( N = 207) replicated these findings with an older sample and provided additional evidence that gain frames should be used in environmental communication. Contrary to our assumptions, we found no effect of the emotional valence of music in either study.

Attachment style to body dissatisfaction, restrained eating, and disordered eating in adolescent girls: Differential weight communication mediators

Body dissatisfaction and eating behaviors are disproportionately elevated amongst adolescent girls. Family relationships represent a context in which adolescent girls’ body image issues emerge. Thus, we integrated attachment and confirmation theories to examine whether weight related supportive messages (i.e., acceptance and challenge) mediated the relationship between attachment style (i.e., anxiety and avoidance) and body image outcomes (i.e., body dissatisfaction (BD), restrained eating (RE), and disordered eating (DE)). Acceptance refers to weight related support that is characterized by warmth and accepting messages; challenge refers to weight related support that is characterized by instrumental assistance and problem-solving messages. A sample of 106 adolescent girls, ages 11 to 21, completed self-report measures on attachment (Relationship Structures Questionnaire), acceptance and challenge (weight related Parental Behavior Questionnaire), body dissatisfaction (Eating Disorders Inventory), and eating behaviors (Dutch Eating Behavior Questionnaire; Eating Disorder Diagnostic Scale). Attachment anxiety directly related to all body image outcomes, whereas attachment avoidance only directly related to RE. Attachment avoidance only related to BD and DE indirectly through perceived acceptance. Those with high anxiety endorsed lower perceived challenge, whereas avoidant individuals endorsed lower perceived acceptance and challenge. We concluded that attachment dimension characteristics to be either hyperactivated regarding relationships (i.e., anxiety), or deactivated and distant (i.e., avoidance), are associated with how individuals perceive support, and in turn, are related to body image and eating behavior outcomes. Further, we clarify the differential roles of acceptance and challenge. Although both supportive, acceptance encompasses a layer of warmth that is more meaningful in body image, particularly for avoidantly attached individuals. By integrating qualities of attachment and supportive weight communication, we reveal that potential pathways of attachment to body image and eating behaviors.

Hierarchical and Incremental Structural Entropy Minimization for Unsupervised Social Event Detection

As a trending approach for social event detection, graph neural network (GNN)-based methods enable a fusion of natural language semantics and the complex social network structural information, thus showing SOTA performance. However, GNN-based methods can miss useful message correlations. Moreover, they require manual labeling for training and predetermining the number of events for prediction. In this work, we address social event detection via graph structural entropy (SE) minimization. While keeping the merits of the GNN-based methods, the proposed framework, HISEvent, constructs more informative message graphs, is unsupervised, and does not require the number of events given a priori. Specifically, we incrementally explore the graph neighborhoods using 1-dimensional (1D) SE minimization to supplement the existing message graph with edges between semantically related messages. We then detect events from the message graph by hierarchically minimizing 2-dimensional (2D) SE. Our proposed 1D and 2D SE minimization algorithms are customized for social event detection and effectively tackle the efficiency problem of the existing SE minimization algorithms. Extensive experiments show that HISEvent consistently outperforms GNN-based methods and achieves the new SOTA for social event detection under both closed- and open-set settings while being efficient and robust.

Organization Sustainability Communication by Fashion Brands: A Qualitative Analysis of Messaging and Engagement on Facebook and Instagram

Sustainability is a broad concept and sustainable consumption includes using products and services that not only meet consumer needs but also minimizes the use of natural resources. Brands use social media (SM) to engage with consumers as it enables the brand to build an emotional connection, hence a great platform to provide sustainability related messaging. Thus, using consumers’ online brand-related activities (COBRA) as the theoretical framework, the purpose of this study was to develop an understanding of how sustainable brands utilize SM platforms for organization sustainability communication. Instagram and Facebook were selected for their high-frequency usage and five sustainable fashion brands were evaluated. Seven hundred and twenty-seven sustainability-related posts and 66,821 comments were analyzed using a qualitative content analysis approach, an established and credible methodology. Three months of SM posts were compiled and examined. Data was coded systematically as deemed appropriate based on the qualitative content analysis approach and resulted in 20 sub-themes across eight thematic categories. The findings of this study provide implications for enhancing stakeholder engagement through (1) content strategy (e.g. video content in sustainability messaging), (2) imagery (e.g. the use of lifestyle content and influencers), and (3) messaging strategy (e.g. educational posts that provide insight into brands’ sustainability initiatives).

EKGDR: An End-to-End Knowledge Graph-Based Method for Computational Drug Repurposing.

The lengthy and expensive process of developing new drugs from scratch, coupled with a high failure rate, has prompted the emergence of drug repurposing/repositioning as a more efficient and cost-effective approach. This approach involves identifying new therapeutic applications for existing approved drugs, leveraging the extensive drug-related data already gathered. However, the diversity and heterogeneity of data, along with the limited availability of known drug-disease interactions, pose significant challenges to computational drug design. To address these challenges, this study introduces EKGDR, an end-to-end knowledge graph-based approach for computational drug repurposing. EKGDR utilizes the power of a drug knowledge graph, a comprehensive repository of drug-related information that encompasses known drug interactions and various categorization information, as well as structural molecular descriptors of drugs. EKGDR employs graph neural networks, a cutting-edge graph representation learning technique, to embed the drug knowledge graph (nodes and relations) in an end-to-end manner. By doing so, EKGDR can effectively learn the underlying causes (intents) behind drug-disease interactions and recursively aggregate and combine relational messages between nodes along different multihop neighborhood paths (relational paths). This process generates representations of disease and drug nodes, enabling EKGDR to predict the interaction probability for each drug-disease pair in an end-to-end manner. The obtained results demonstrate that EKGDR outperforms previous models in all three evaluation metrics: area under the receiver operating characteristic curve (AUROC = 0.9475), area under the precision-recall curve (AUPRC = 0.9490), and recall at the top-200 recommendations (Recall@200 = 0.8315). To further validate EKGDR's effectiveness, we evaluated the top-20 candidate drugs suggested for each of Alzheimer's and Parkinson's diseases.

"It's no use saying it in English": A qualitative study exploring community leaders' perceptions of the challenges and opportunities with translating and interpreting COVID-19 related public health messaging to reach ethnic minorities in Australia.

The Australian Government implemented a range of public health response strategies and communication approaches to reduce the spread of COVID-19; however, concerns have been raised around a failure to sufficiently consider culturally and linguistically diverse (CaLD) communities in these processes. This research aimed to understand the factors that have impacted COVID-19 communication and engagement efforts during the pandemic from the perspective of key CaLD community and faith-based leaders. A further aim was to understand the processes that could be adopted to support future communication strategies, including promoting pandemic-related vaccines. This study included 29 key informant interviews with community and faith-based leaders in New South Wales, Australia. The overwhelming message from community leaders was a sense of shared responsibility between their organisations and governments in communicating pertinent and accurate COVID-19 related information to CaLD communities. They expressed a sense of duty to keep their community members safe. However, community leaders and others shouldered significant costs related to resources and time that need to be acknowledged by governments in preparing for future disease outbreaks. They felt that governments should consider: 1) improving communication between governments and CaLD organisations; 2) responding to the specific CaLD needs with greater agility; 3) foregrounding social media in their communication strategy; 4) reinvesting in local public health units to know their population; 5) developing a health ambassadors model program; 6) preparing a hybrid model of translators/interpreters to fill the gap; and, 7) reimagining vaccine information campaigns to target CaLD communities better. Given the technical details about the COVID-19 virus conveyed in government information campaigns and the media, ensuring the most vulnerable populations, including people from CaLD backgrounds, access clear, concise and timely public health messaging from governments and community organisations requires further attention.

Integrating direct messaging with flood alerts and warnings: Insights into effectiveness from a registered public user population

AbstractDirect messaging involving simultaneous mass transmission of brief text or voice messages to large numbers of recipients has become a frontline method in flood hazard communications. Messages are intended to serve as cues, drawing recipients' attention to changing conditions, yet the actual effectiveness of direct messaging among recipient groups remains under‐examined. This article considers direct messaging within the Floodline public flood warning service in Scotland, implemented by the Scottish Environment Protection Agency (SEPA). Within Floodline, messaging is integrated with alerting and warning information, termed straightforwardly ‘Flood Alerts’ and ‘Flood Warnings’. Collaborating with SEPA, we conducted an online questionnaire survey of registered Floodline direct messaging recipients. In this article, our analysis focusses specifically on responses to three open‐ended questions included in this survey, with an iterative qualitative coding approach employed to interpret themes of meaning from the question responses. This analysis gives a clear indication that recipients value Floodline and direct messaging. However, there are also questions raised over the utility of Flood Alerts and related messaging, which we elaborate in the findings and discussion, along with the scope for adding content, linking to other information, and developing closer relationships. Changes being developed by SEPA align with several of these findings.

Label-Aware Chinese Event Detection with Heterogeneous Graph Attention Network

Event Detection (ED) aims to recognize instances of specified types of event triggers in text. Different from English ED, Chinese ED suffers from the problem of word-trigger mismatch due to the uncertain word boundaries. Existing approaches injecting word information into character-level models have achieved promising progress to alleviate this problem, but they are limited by two issues. First, the interaction between characters and lexicon words is not fully exploited. Second, they ignore the semantic information provided by event labels. We thus propose a novel architecture named Label enhanced Heterogeneous Graph Attention Networks (L-HGAT). Specifically, we transform each sentence into a graph, where character nodes and word nodes are connected with different types of edges, so that the interaction between words and characters is fully reserved. A heterogeneous graph attention networks is then introduced to propagate relational message and enrich information interaction. Furthermore, we convert each label into a trigger-prototype-based embedding, and design a margin loss to guide the model distinguish confusing event labels. Experiments on two benchmark datasets show that our model achieves significant improvement over a range of competitive baseline methods.

Peace and Values Education as a Cross-Cutting Issue in Rwandan Schools: Teachers and Classroom-Based Perspective

Peace and values is one of the cross-cutting issues in the Rwandan primary and secondary school curriculum. Different organizations such as the Aegis Trust have trained teachers in integrating peace and values in the various subjects, they teach by training learners on how to build a culture of peace. This is very important to Rwanda as a post genocide. However, no research (at the best of our knowledge) has been conducted to investigate how and to what extent teachers integrate peace and values in various school subjects. This article reports on empirical research conducted in five primary schools in four provinces of Rwanda plus Kigali City to examine the teaching of peace and values in Rwandan schools. A qualitative approach was adopted for this research. Classroom observations, focus group discussions and semi-structured interviews were conducted to collect data. Thematic content analysis was used to analyse the data. The findings show that peace values are not part of teachers’ planning and are not explicitly referred to in classroom practices. This is mainly because teachers lack skills and knowledge to do so. However, some peace related messages are shared in extracurricular activities but these are counteracted by violent practices such as corporal punishment and bullying. This article concludes that teachers’ limited understanding of peace impedes the integration of peace culture in schools. Thus, it suggests that peace values should be explicitly and systematically integrated in the teaching of all subjects. It also calls for strong measures to end school violence in Rwandan schools by equipping teachers for positive management of learners’ behaviours.

COVID-19 related messaging, beliefs, information sources, and mitigation behaviors in Virginia: a cross-sectional survey in the summer of 2020.

Conflicting messages and misleading information related to the coronavirus (COVID-19) pandemic (SARS-CoV-2) have hindered mitigation efforts. It is important that trust in evidence-based public health information be maintained to effectively continue pandemic mitigation strategies. Officials, researchers, and the public can benefit from exploring how people receive information they believe and trust, and how their beliefs influence their behaviors. To gain insight and inform effective evidence-based public health messaging, we distributed an anonymous online cross-sectional survey from May to July, 2020 to Virginia residents, 18 years of age or older. Participants were surveyed about their perceptions of COVID-19, risk mitigation behaviors, messages and events they felt influenced their beliefs and behaviors, and where they obtained information that they trust. The survey also collected socio-demographic information, including gender, age, race, ethnicity, level of education, income, employment status, occupation, changes in employment due to the pandemic, political affiliation, sexual orientation, and zip code. Analyses included specific focus on the most effective behavioral measures: wearing a face mask and distancing in public. Among 3,488 respondents, systematic differences were observed in information sources that people trust, events that impacted beliefs and behaviors, and how behaviors changed by socio-demographics, political identity, and geography within Virginia. Characteristics significantly associated (p<0.025) with not wearing a mask in public included identifying as non-Hispanic white, male, Republican political identity, younger age, lower income, not trusting national science and health organizations, believing one or more non-evidence-based messages, and residing in Southwest Virginia in logistic regression. Similar, lesser in magnitude correlations, were observed for distancing in public. This study describes how information sources considered trustworthy vary across different populations and identities, and how these differentially correspond to beliefs and behaviors. This study can assist decision makers and the public to improve and effectively target public health messaging related to the ongoing COVID-19 pandemic and future public health challenges in Virginia and similar jurisdictions.

Effects of relational and instrumental messaging on human perception of rattlesnakes.

We tested the effects of relational and instrumental message strategies on US residents' perception of rattlesnakes-animals that tend to generate feelings of fear, disgust, or hatred but are nevertheless key members of healthy ecosystems. We deployed an online survey to social media users (n = 1,182) to describe perceptions of rattlesnakes and assess the change after viewing a randomly selected relational or instrumental video message. An 8-item, pre-and post-Rattlesnake Perception Test (RPT) evaluated perception variables along emotional, knowledge, and behavioral gradients on a 5-point Likert scale; the eight responses were combined to produce an Aggregate Rattlesnake Perception (ARP) score for each participant. We found that people from Abrahamic religions (i.e., Christianity, Judaism, Islam) and those identifying as female were associated with low initial perceptions of rattlesnakes, whereas agnostics and individuals residing in the Midwest region and in rural residential areas had relatively favorable perceptions. Overall, both videos produced positive changes in rattlesnake perception, although the instrumental video message led to a greater increase in ARP than the relational message. The relational message was associated with significant increases in ARP only among females, agnostics, Baby Boomers (age 57-75), and Generation-Z (age 18-25 to exclude minors). The instrumental video message was associated with significant increases in ARP, and this result varied by religious group. ARP changed less in those reporting prior experience with a venomous snake bite (to them, a friend, or a pet) than in those with no such experience. Our data suggest that relational and instrumental message strategies can improve people's perceptions of unpopular and potentially dangerous wildlife, but their effectiveness may vary by gender, age, religious beliefs, and experience. These results can be used to hone and personalize communication strategies to improve perceptions of unpopular wildlife species.