How Visual Aesthetics and Calorie Density Predict Food Image Popularity on Instagram: A Computer Vision Analysis.

Social media have become an important source where people are exposed to visual representations of foods. This study aims to understand what content factors contribute to the popularity of food images on Instagram. We collected 53,894 images from 90 popular food influencer accounts on Instagram over two years. Applying computer vision methods, we investigated the effects of visual aesthetics and calorie density of foods on audience engagement (i.e. likes, comments) as well as if the effects of visual aesthetics varied by calorie density. Our results showed that both visual aesthetics and calorie density were important predictors of image popularity. The use of arousing, warm colors such as red, orange, and yellow, feature complexity, and repetition predicted higher likes, whereas brightness, colorfulness, and compositional complexity acted reversely. A similar pattern was observed for comments. The calorie density of foods in images positively predicted likes and comments. Also, the effects of visual aesthetics varied by calorie content and were more pronounced for low-calorie images. Health practitioners who plan to harness the power of social media to encourage certain dietary behaviors should take visual aesthetics into account when designing persuasive messages and campaigns.

Give me liberty or give me COVID-19: How social dominance orientation, right-wing authoritarianism, and libertarianism explain Americans' reactions to COVID-19.

While previous research has revealed an ideological divide in Americans' perceptions of COVID-19, specific ideological components can additionally explain public reactions to the pandemic. With two surveys-one sample of crowdsourced workers (N = 482) and a nationally representative sample of American adults (N = 7449)-this research investigates how multiple ideological facets simultaneously predict individuals' reactions to COVID-19. Results demonstrate that social dominance orientation and libertarianism are two important ideological sources that predict more dismissal of COVID-19 and less support for government measures. Right-wing authoritarianism was negatively correlated with COVID-19 concern and support for government actions, but suppression effects could exist. The effects of ideological variables were largely consistent when trust in science was considered. This study highlights the role of specific ideological components in contributing to the political divide regarding attitudes toward the COVID-19 pandemic beyond the liberal-conservative identification.

Politics of COVID-19 vaccine mandates: Left/right-wing authoritarianism, social dominance orientation, and libertarianism.

Mandatory and punitive vaccination policies, such as requiring vaccination certificates for public activities and firing employees who refuse vaccination, have raised considerable objections. With a sample of U.S. crowdsourced workers (N = 983), this study investigates how four ideologies-left-wing authoritarianism (LWA), right-wing authoritarianism (RWA), social dominance orientation (SDO), and libertarianism-explain vaccine acceptance and attitudes toward vaccine policies. Results show that LWA predicts higher vaccine acceptance and support for COVID-19 vaccine mandates and the punishment of unvaccinated individuals, whereas libertarianism and RWA show negative relationships. SDO is linked to opposition to vaccine mandates. This study underscores the role of specific ideological components in shaping attitudes toward vaccine policies while also contributing to the arguments that LWA and libertarianism have important implications for studying sociopolitical attitudes.

Fitspiration on Instagram: Identifying Topic Clusters in User Comments to Posts with Objectification Features.

This study aimed to examine features of objectified images in popular fitspiration accounts on social media, identify the most prevalent user discussion topics about these images, and investigate the linkages between specific objectification cues and discussion topics. We employed content analysis to identify gender-specific objectification elements in fitspiration images (N = 2,000) on Instagram and unsupervised topic modeling to uncover topics in user comments (N = 35,263). Multilevel regressions were conducted to examine how gender and objectification features associate with the topics. Results revealed that one-third of the images were objectified. Objectification features such as sexual posing and the presence of objectified chest or abdomen were associated with the occurrence of two out of the three most prominent topic clusters (inspiration, body, and image-related discussions). We concluded that objectified fitspiration may distract users from health goals.

"Letter to my future self" as a device for assessing health education effectiveness.

OBJECTIVE: To examine whether a "letter to my future self" analyzed using structural topic modeling (STM) represents a useful technique in revealing how participants integrate educational content into planned future behaviors. METHODS: 453 club-sports athletes in a concussion-education randomized control study wrote two-paragraph letters describing what they hoped to remember after viewing one of three randomly assigned educational interventions. RESULTS: A six-topic solution revealed three topics related to the content of the education and three topics related to the participant behavioral takeaways. The content-related topics reflected the educational content viewed. The behavioral takeaway topics indicated that the Consequence-based education was more likely to generate the Concussion Seriousness[CS:23%] topic while Traditional(24%) and Consequence-based(20%) interventions were more likely to generate the Responsibility for Brain Health[BH] topic. Traditional(21%) and Revised-symptom(17%) interventions were more likely to generate the Awareness and Action topics. CONCLUSION: Unstructured user-generated data in the form of a "letter to my future self" analyzed using structural topic modeling provides a novel evaluation of the present and likely future impact of educational interventions. PRACTICE IMPLICATIONS: Patient educators can enhance the effectiveness of education through the application of these methods to the evaluation of and innovation in programs.

The role of ideological dimensions in shaping acceptance of facial recognition technology and reactions to algorithm bias.

Facial recognition technology has been introduced into various aspects of social life, yet it has raised concerns over its infringement of civil liberties and biases against minorities. This study investigates how three ideological dimensions-social dominance orientation, right-wing authoritarianism, and libertarianism-shape facial recognition acceptance. First, two surveys of crowdsourced workers (N = 891 and 587) demonstrated that the acceptance of facial recognition applications is positively associated with right-wing authoritarianism and negatively with libertarianism, whereas social dominance orientation shows little influence. Second, an experiment (N = 496) exposed participants to information about demographic biases in facial recognition applications. This message produced more opposition to facial recognition and this effect largely was not moderated by the three ideological dimensions. In summary, individuals' endorsement of various ideologies predicts their acceptance of facial recognition technology, but messages about algorithm biases in facial recognition can still shape their attitudes regardless of the preexisting ideologies.

The ideological divide in public perceptions of self-driving cars.

Applications in artificial intelligence such as self-driving cars may profoundly transform our society, yet emerging technologies are frequently faced with suspicion or even hostility. Meanwhile, public opinions about scientific issues are increasingly polarized along the ideological line. By analyzing a nationally representative panel in the United States, we reveal an emerging ideological divide in public reactions to self-driving cars. Compared with liberals and Democrats, conservatives and Republicans express more concern about autonomous vehicles and more support for restrictively regulating autonomous vehicles. This ideological gap is largely driven by social conservatism. Moreover, both familiarity with driverless vehicles and scientific literacy reduce respondents' concerns over driverless vehicles and support for regulation policies. Still, the effects of familiarity and scientific literacy are weaker among social conservatives, indicating that people may assimilate new information in a biased manner that promotes their worldviews.

The influence of weight-of-evidence strategies on audience perceptions of (un)certainty when media cover contested science.

Controversy in science news accounts attracts audiences and draws attention to important science issues. But sometimes covering multiple sides of a science issue does the audience a disservice. Counterbalancing a truth claim backed by strong scientific support with a poorly backed argument can unnecessarily heighten audience perceptions of uncertainty. At the same time, journalistic norms often constrain reporters to "get both sides of the story" even when there is little debate in the scientific community about which truth claim is most valid. In this study, we look at whether highlighting the way in which experts are arrayed across truth claims-a strategy we label "weight-of-evidence reporting"-can attenuate heightened perceptions of uncertainty that can result from coverage of conflicting claims. The results of our study suggest weight-of-evidence strategies can indeed play a role in reducing some of the uncertainty audiences may perceive when encountering lop-sided truth claims.