Wildlife Photos on Social Media: A Quantitative Content Analysis of Conservation Organisations' Instagram Images.
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ABSTRACT:
SUBMITTER: Shaw MN
PROVIDER: S-EPMC9311588 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BACKGROUND:"Fitspiration" (also known as "fitspo") aims to inspire individuals to exercise and be healthy, but emerging research indicates exposure can negatively impact female body image. Fitspiration is frequently accessed on social media; however, it is currently unclear the degree to which messages about body image and exercise differ by gender of the subject. OBJECTIVE:The aim of our study was to conduct a content analysis to identify the characteristics of fitspiration content posted across social media and whether this differs according to subject gender. METHODS:Content tagged with #fitspo across Instagram, Facebook, Twitter, and Tumblr was extracted over a composite 30-minute period. All posts were analyzed by 2 independent coders according to a codebook. RESULTS:Of the 415/476 (87.2%) relevant posts extracted, most posts were on Instagram (360/415, 86.8%). Most posts (308/415, 74.2%) related thematically to exercise, and 81/415 (19.6%) related thematically to food. In total, 151 (36.4%) posts depicted only female subjects and 114/415 (27.5%) depicted only male subjects. Female subjects were typically thin but toned; male subjects were often muscular or hypermuscular. Within the images, female subjects were significantly more likely to be aged under 25 years (P<.001) than the male subjects, to have their full body visible (P=.001), and to have their buttocks emphasized (P<.001). Male subjects were more likely to have their face visible in the post (P=.005) than the female subjects. Female subjects were more likely to be sexualized than the male subjects (P=.002). CONCLUSIONS:Female #fitspo subjects typically adhered to the thin or athletic ideal, and male subjects typically adhered to the muscular ideal. Future research and interventional efforts should consider the potential objectifying messages in fitspiration, as it relates to both female and male body image.
| S-EPMC5390113 | biostudies-literature
Project description:BackgroundThe content produced by individuals on various social media platforms has been successfully used to identify mental illness, including depression. However, most of the previous work in this area has focused on user-generated content, that is, content created by the individual, such as an individual's posts and pictures. In this study, we explored the predictive capability of community-generated content, that is, the data generated by a community of friends or followers, rather than by a sole individual, to identify depression among social media users.ObjectiveThe objective of this research was to evaluate the utility of community-generated content on social media, such as comments on an individual's posts, to predict depression as defined by the clinically validated Patient Health Questionnaire-8 (PHQ-8) assessment questionnaire. We hypothesized that the results of this research may provide new insights into next generation of population-level mental illness risk assessment and intervention delivery.MethodsWe created a Web-based survey on a crowdsourcing platform through which participants granted access to their Instagram profiles as well as provided their responses to PHQ-8 as a reference standard for depression status. After data quality assurance and postprocessing, the study analyzed the data of 749 participants. To build our predictive model, linguistic features were extracted from Instagram post captions and comments, including multiple sentiment scores, emoji sentiment analysis results, and meta-variables such as the number of likes and average comment length. In this study, 10.4% (78/749) of the data were held out as a test set. The remaining 89.6% (671/749) of the data were used to train an elastic-net regularized linear regression model to predict PHQ-8 scores. We compared different versions of this model (ie, a model trained on only user-generated data, a model trained on only community-generated data, and a model trained on the combination of both types of data) on a test set to explore the utility of community-generated data in our predictive analysis.ResultsThe 2 models, the first trained on only community-generated data (area under curve [AUC]=0.71) and the second trained on a combination of user-generated and community-generated data (AUC=0.72), had statistically significant performances for predicting depression based on the Mann-Whitney U test (P=.03 and P=.02, respectively). The model trained on only user-generated data (AUC=0.63; P=.11) did not achieve statistically significant results. The coefficients of the models revealed that our combined data classifier effectively amalgamated both user-generated and community-generated data and that the 2 feature sets were complementary and contained nonoverlapping information in our predictive analysis.ConclusionsThe results presented in this study indicate that leveraging community-generated data from social media, in addition to user-generated data, can be informative for predicting depression among social media users.
| S-EPMC6302231 | biostudies-literature
Project description:BackgroundThe human papillomavirus (HPV) vaccine is a major advancement in cancer prevention and this primary prevention tool has the potential to reduce and eliminate HPV-associated cancers; however, the safety and efficacy of vaccines in general and the HPV vaccine specifically have come under attack, particularly through the spread of misinformation on social media. The popular social media platform Instagram represents a significant source of exposure to health (mis)information; 1 in 3 US adults use Instagram.ObjectiveThe objective of this analysis was to characterize pro- and anti-HPV vaccine networks on Instagram, and to describe misinformation within the anti-HPV vaccine network.MethodsFrom April 2018 to December 2018, we collected publicly available English-language Instagram posts containing hashtags #HPV, #HPVVaccine, or #Gardasil using Netlytic software (n=16,607). We randomly selected 10% of the sample and content analyzed relevant posts (n=580) for text, image, and social media features as well as holistic attributes (eg, sentiments, personal stories). Among antivaccine posts, we organized elements of misinformation within four broad dimensions: 1) misinformation theoretical domains, 2) vaccine debate topics, 3) evidence base, and 4) health beliefs. We conducted univariate, bivariate, and network analyses on the subsample of posts to quantify the role and position of individual posts in the network.ResultsCompared to provaccine posts (324/580, 55.9%), antivaccine posts (256/580, 44.1%) were more likely to originate from individuals (64.1% antivaccine vs 25.0% provaccine; P<.001) and include personal narratives (37.1% vs 25.6%; P=.003). In the antivaccine network, core misinformation characteristics included mentioning #Gardasil, purporting to reveal a lie (ie, concealment), conspiracy theories, unsubstantiated claims, and risk of vaccine injury. Information/resource posts clustered around misinformation domains including falsification, nanopublications, and vaccine-preventable disease, whereas personal narrative posts clustered around different domains of misinformation, including concealment, injury, and conspiracy theories. The most liked post (6634 likes) in our full subsample was a positive personal narrative post, created by a non-health individual; the most liked post (5604 likes) in our antivaccine subsample was an informational post created by a health individual.ConclusionsIdentifying characteristics of misinformation related to HPV vaccine on social media will inform targeted interventions (eg, network opinion leaders) and help sow corrective information and stories tailored to different falsehoods.
| S-EPMC7746500 | biostudies-literature
Project description:Understanding the visual discrepancy and heterogeneity of different places is of great interest to architectural design, urban design and tourism planning. However, previous studies have been limited by the lack of adequate data and efficient methods to quantify the visual aspects of a place. This work proposes a data-driven framework to explore the place-informative scenes and objects by employing deep convolutional neural network to learn and measure the visual knowledge of place appearance automatically from a massive dataset of photos and imagery. Based on the proposed framework, we compare the visual similarity and visual distinctiveness of 18 cities worldwide using millions of geo-tagged photos obtained from social media. As a result, we identify the visual cues of each city that distinguish that city from others: other than landmarks, a large number of historical architecture, religious sites, unique urban scenes, along with some unusual natural landscapes have been identified as the most place-informative elements. In terms of the city-informative objects, taking vehicles as an example, we find that the taxis, police cars and ambulances are the most place-informative objects. The results of this work are inspiring for various fields-providing insights on what large-scale geo-tagged data can achieve in understanding place formalization and urban design.
| S-EPMC6458415 | biostudies-literature
Project description:Numerous studies show the importance of social understanding in addressing multifaceted conservation issues. Building on a conservation planning framework, this study examines the social dimensions of wildlife conservation in Kinabatangan, Sabah, Malaysia. It employs a qualitative approach by conducting in-depth, semi-structured interviews with sixty informants drawn from local community members, government officials, tourism operators, non-government organizations, and the private sector. Our results show that the incidence of human-wildlife conflicts has reduced in the region, but that conflicts among stakeholders themselves about wildlife still remain a significant threat for attaining successful conservation outcomes. Further stakeholder perceptions of increased wildlife numbers often contrast with actual counts returned by periodical surveys conducted by conservation agencies, e.g., showing a 30% decline of orangutans and a 29% decline of gibbon abundance. This shows that evidence-based conservation messages have not been communicated well. The study has implications for enhancing social values among conservation players, promoting local community empowerment and revising conservation awareness programs.
| S-EPMC8996913 | biostudies-literature
Project description:PurposeTo determine the content posted by sports medicine orthopaedic surgeons on Instagram and Twitter and to evaluate the associated engagement.MethodsData were collected from sports medicine orthopaedic surgeons' Instagram profiles and Twitter profiles between December 2020 and June 2022. The median number of total number of likes, comments, and retweets was calculated. The content of posts from both platforms was divided into the following categories: personal life, research, patient testimony, day in the life, pathway to becoming a physician, clinical cases, medical facts, and team coverage.ResultsData from 71 Instagram profiles and 39 Twitter profiles were used in this study. A total of 1,193 posts were identified on Instagram and 1,284 posts were identified on Twitter. The personal life category had the greatest number of posts on Instagram (303, 25.4%), whereas the medical facts category had the greatest number of posts on Twitter (251, 19.5%). Pathway to becoming a physician had the greatest median number of likes on both Instagram (97.5, range 48-2,467) and Twitter (19, range 0-50) and the greatest median number of comments on Instagram at 16 (range 1-203). The team coverage category on Instagram had the greatest percentage of likes per follower at 9.9%. A significantly greater percentage of orthopaedic surgeons posted about day in the life content, medical facts, and research on Twitter in comparison with Instagram. Instagram resulted in significantly more social media engagement than Twitter in all 8 categories (P < .05).ConclusionsInstagram resulted in significantly more social media engagement across all categories in comparison with Twitter with team coverage, personal life, and pathway to becoming a physician being the most popular categories.Clinical relevanceThe information learned in this study may help sports surgeons understand how they may best utilize social media to engage with others and enhance their clinical practice.
| S-EPMC10792685 | biostudies-literature
Project description:BackgroundAlthough organ transplantation is a very effective clinical solution to save the lives of patients suffering from organ failure, the supply of donated organs still cannot meet its growing demand. Educating the society about organ donation is a critical success factor in increasing donation rates, especially in countries that require potential donors to proactively register and opt-in (e.g., Germany). While social media has emerged as an effective tool for disseminating health information, recent evidence suggests that published organ donation content (both online and offline), aimed at raising awareness, still lacks effectiveness. To develop recommendations for optimizing organ donation messaging via social media, this study not only examines the current state of organ donation communication on Instagram, but also identifies factors that contribute to message effectiveness.MethodsWe conducted a retrospective content analysis to in-depth assess organ donation-related content published on Instagram in Germany between January and March 2022. Systematic coding allowed to identify common themes, sentiments, and communication strategies, which were analyzed for their effectiveness using linear regression analyses.ResultsOf the 500 organ donation posts, 57% were published by institutional authors while the remainder was shared by private accounts. Most content was aimed at the general population and shared neutral (80%) or positive sentiments (17%). Transformative messages, positive emotions, posts published by the transplant recipient and the image of a human served as predictors for post effectiveness measured in terms of likes (p < 0.001) and comments (p < 0.01). Sharing personal experiences (p < 0.01) and highlighting the meaning of organ donations (p < 0.05) resulted in significantly higher audience engagement than any other topic discussed.ConclusionOur findings highlight the need for health officials to work closely with organ transplant recipients to publicly advocate for organ donations by sharing personal and transformative messages. The high share of posts published by transplant recipients indicates a certain openness to share personal experiences with broad audiences. Different message characteristics served as predictors for message effectiveness (i.e., increased audience engagement) which can likely be extrapolated to other health-related use cases (e.g., cancer screening).
| S-EPMC10176822 | biostudies-literature
Project description:BackgroundVaccination misinformation is associated with serious public health consequences, such as a decrease in vaccination rates and a risk of disease outbreaks. Although social media offers organisations promoting vaccination unparalleled opportunities to promote evidence and counterbalance misinformation, we know relatively little about their internal workings. The aim of this paper is to explore the strategies, perspectives and experiences of communicators working within such organisations as they promote vaccination and respond to misinformation on social media.MethodsUsing qualitative methods, we purposively sampled 21 participants responsible for routine social media activity and strategy from Australian organisations actively promoting vaccination on social media, including government health departments, local health services, advocacy groups, professional associations and technical/scientific organisations. We conducted semi-structured, in-depth interviews to explore their perspectives and practices. Applying Risk Communication principles as a lens, we used Framework Analysis to explore the data both inductively and deductively.ResultsOrganisations promoting vaccination face multiple challenges on social media, including misinformation, anti-science sentiment, a complex vaccination narrative and anti-vaccine activists. They developed a range of sophisticated strategies in response, including communicating with openness in an evidence-informed way; creating safe spaces to encourage audience dialogue; fostering community partnerships; and countering misinformation with care.ConclusionsWe recommend that communicators consider directly countering misinformation because of the potential influence on their silent audience, i.e. those observing but not openly commenting, liking or sharing posts. Refutations should be straightforward, succinct and avoid emphasizing misinformation. Communicators should consider pairing scientific evidence with stories that speak to audience beliefs and values. Finally, organisations could enhance vaccine promotion and their own credibility on social media by forming strong links with organisations sharing similar values and goals.
| S-EPMC6806569 | biostudies-literature
Project description:ObjectivesTo explore the nature of images tagged as self-harm on popular social media sites and what this might tell us about how these sites are used.DesignA visual content and thematic analysis of a sample of 602 images captured from Twitter, Instagram and Tumblr.ResultsOver half the images tagged as self-harm had no explicit representation of self-harm. Where there was explicit representation, self-injury was the most common; none of these portrayed images of graphic or shocking self-injury. None of the images we captured specifically encouraged self-harm or suicide and there was no image that could be construed as sensationalising self-harm.Four themes were found across the images: communicating distress, addiction and recovery, gender and the female body, identity and belonging.ConclusionsFindings suggest that clinicians should not be overly anxious about what is being posted on social media. Although we found a very few posts suggesting self-injury was attractive, there were no posts that could be viewed as actively encouraging others to self-harm. Rather, the sites were being used to express difficult emotions in a variety of creative ways, offering inspiration to others through the form of texts or shared messages about recovery.
| S-EPMC6367987 | biostudies-literature
Project description:Social media may provide new insight into our understanding of substance use and addiction. In this study, we developed a deep-learning method to automatically classify individuals' risk for alcohol, tobacco, and drug use based on the content from their Instagram profiles. In total, 2287 active Instagram users participated in the study. Deep convolutional neural networks for images and long short-term memory (LSTM) for text were used to extract predictive features from these data for risk assessment. The evaluation of our approach on a held-out test set of 228 individuals showed that among the substances we evaluated, our method could estimate the risk of alcohol abuse with statistical significance. These results are the first to suggest that deep-learning approaches applied to social media data can be used to identify potential substance use risk behavior, such as alcohol use. Utilization of automated estimation techniques can provide new insights for the next generation of population-level risk assessment and intervention delivery.
| S-EPMC6333814 | biostudies-literature