Breast Cancer messaging in Vietnam: an online media content analysis.
Ontology highlight
ABSTRACT: Background
Breast cancer incidence is increasing in Vietnam with studies indicating low levels of knowledge and awareness and late presentation. While there is a growing body of literature on challenges faced by women in accessing breast cancer services, and for delivering care, no studies have sought to analyse breast cancer messaging in the Vietnamese popular media. The aim of this study was to investigate and understand the content of messages concerning breast cancer in online Vietnamese newspapers in order to inform future health promotional content.Methods
This study describes a mixed-methods media content analysis that counted and ranked frequencies for media content (article text, themes and images) related to breast cancer in six Vietnamese online news publications over a twelve month period.Results
Media content (n?=?129 articles & n?=?237 images) sampled showed that although information is largely accurate, there is a marked lack of stories about Vietnamese women's personal experiences. Such stories could help bridge the gap between what information about breast cancer is presented in the Vietnamese media, and what women in Vietnam understand about breast cancer risk factors, symptoms, screening and treatment.Conclusions
Given findings from other studies indicating low levels of knowledge and women with breast cancer experiencing stigma and prejudice, more nuanced and in-depth narrative-focused messaging may be required.
SUBMITTER: Jenkins C
PROVIDER: S-EPMC7304170 | biostudies-literature | 2020 Jun
REPOSITORIES: biostudies-literature
Publications
Breast Cancer messaging in Vietnam: an online media content analysis.
Jenkins Chris C Ha Dinh Thu DT Lan Vu Tuyet VT Van Minh Hoang H Lohfeld Lynne L Murphy Paul P Ha Le Thi Hai LTH
BMC public health 20200619 1
<h4>Background</h4>Breast cancer incidence is increasing in Vietnam with studies indicating low levels of knowledge and awareness and late presentation. While there is a growing body of literature on challenges faced by women in accessing breast cancer services, and for delivering care, no studies have sought to analyse breast cancer messaging in the Vietnamese popular media. The aim of this study was to investigate and understand the content of messages concerning breast cancer in online Vietna ...[more]
Similar Datasets
Project description:BACKGROUND:The content that cancer patients and their relatives (ie, posters) share in online cancer communities has been researched in various ways. In the past decade, researchers have used automated analysis methods in addition to manual coding methods. Patients, providers, researchers, and health care professionals can learn from experienced patients, provided that their experience is findable. OBJECTIVE:The aim of this study was to systematically review all relevant literature that analyzes user-generated content shared within online cancer communities. We reviewed the quality of available research and the kind of content that posters share with each other on the internet. METHODS:A computerized literature search was performed via PubMed (MEDLINE), PsycINFO (5 and 4 stars), Cochrane Central Register of Controlled Trials, and ScienceDirect. The last search was conducted in July 2017. Papers were selected if they included the following terms: (cancer patient) and (support group or health communities) and (online or internet). We selected 27 papers and then subjected them to a 14-item quality checklist independently scored by 2 investigators. RESULTS:The methodological quality of the selected studies varied: 16 were of high quality and 11 were of adequate quality. Of those 27 studies, 15 were manually coded, 7 automated, and 5 used a combination of methods. The best results can be seen in the papers that combined both analytical methods. The number of analyzed posts ranged from 200 to 1,500,000; the number of analyzed posters ranged from 75 to 90,000. The studies analyzing large numbers of posts mainly related to breast cancer, whereas those analyzing small numbers were related to other types of cancers. A total of 12 studies involved some or entirely automatic analysis of the user-generated content. All the authors referred to two main content categories: informational support and emotional support. In all, 15 studies reported only on the content, 6 studies explicitly reported on content and social aspects, and 6 studies focused on emotional changes. CONCLUSIONS:In the future, increasing amounts of user-generated content will become available on the internet. The results of content analysis, especially of the larger studies, give detailed insights into patients' concerns and worries, which can then be used to improve cancer care. To make the results of such analyses as usable as possible, automatic content analysis methods will need to be improved through interdisciplinary collaboration.
| S-EPMC5904449 | biostudies-literature
Project description:BackgroundThe health belief model explains that individual intentions and motivation of health behaviors are mostly subject to external cues to action, such as from interpersonal communications and media consumptions. The concept of mobilizing information (MI) refers to a type of mediated information that could call individuals to carry out particular health actions. Different media channels, especially digital media outlets, play an essential role as a health educator to disseminate cancer health information and persuade and mobilize cancer prevention in the community. However, little is known about calls to action (or MI) in online cancer news, especially from Asian media outlets.ObjectiveThis study aimed at analyzing cancer news articles that contain MI and their news components on the selected Malaysian English and Chinese newspapers with online versions.MethodsThe Star Online and Sin Chew Online were selected for analysis because the two newspaper websites enjoy the highest circulation and readership in the English language and the Chinese language streams, respectively. Two bilingual coders searched the cancer news articles based on sampling keywords and then read and coded each news article accordingly. Five coding variables were conceptualized from previous studies (ie, cancer type, news source, news focus, cancer risk factors, and MI), and a good consistency using Cohen kappa was built between coders. Descriptive analysis was used to examine the frequency and percentage of each coding item; chi-square test (confidence level at 95%) was applied to analyze the differences between two newspaper websites, and the associations between variables and the presence of MI were examined through binary logistic regression.ResultsAmong 841 analyzed news articles, 69.6% (585/841) presented MI. News distributions were unbalanced throughout the year in both English and Chinese newspaper websites; some months occupied peaks (ie, February and October), but cancer issues and MI for cancer prevention received minimal attention in other months. The news articles from The Star Online and Sin Chew Online were significantly different in several news components, such as the MI present rates (χ2=9.25, P=.003), providing different types of MI (interactive MI: χ2=12.08, P=.001), interviewing different news sources (government agency: χ2=12.05, P=.001), concerning different news focus (primary cancer prevention: χ2=10.98, P=.001), and mentioning different cancer risks (lifestyle risks: χ2=7.43, P=.007). Binary logistic regression results reported that online cancer news articles were more likely to provide MI when interviewing nongovernmental organizations, focusing on topics related to primary cancer prevention, and highlighting lifestyle risks (odds ratio [OR] 2.77, 95% CI 1.89-4.05; OR 97.70, 95% CI 46.97-203.24; OR 186.28; 95% CI 44.83-773.96; P=.001, respectively).ConclusionsThis study provided new understandings regarding MI in cancer news coverage. This could wake and trigger individuals' preexisting attitudes and intentions on cancer prevention. Thus, health professionals, health journalists, and health campaign designers should concentrate on MI when distributing health information to the community.
| S-EPMC8277372 | biostudies-literature
Project description:ObjectiveTo characterise how online media coverage of journal articles on cancer funded by the US government varies by cancer type and stage of the cancer control continuum and to compare the disease prevalence rates with the amount of funded research published for each cancer type and with the amount of media attention each receives.DesignA cross-sectional study.SettingThe United States.ParticipantsThe subject of analysis was 11?436 journal articles on cancer funded by the US government published in 2016. These articles were identified via PubMed and characterised as receiving online media attention based on data provided by Altmetric.Results16.8% (n=1925) of articles published on US government-funded research were covered in the media. Published journal articles addressed all common cancers. Frequency of journal articles differed substantially across the common cancers, with breast cancer (n=1284), lung cancer (n=630) and prostate cancer (n=586) being the subject of the most journal articles. Roughly one-fifth to one-fourth of journal articles within each cancer category received online media attention. Media mentions were disproportionate to actual burden of each cancer type (ie, incidence and mortality), with breast cancer articles receiving the most media mentions. Scientific articles also covered the stages of the cancer continuum to varying degrees. Across the 13 most common cancer types, 4.4% (n=206) of articles focused on prevention and control, 11.7% (n=550) on diagnosis and 10.7% (n=502) on therapy.ConclusionsFindings revealed a mismatch between prevalent cancers and cancers highlighted in online media. Further, journal articles on cancer control and prevention received less media attention than other cancer continuum stages. Media mentions were not proportional to actual public cancer burden nor volume of scientific publications in each cancer category. Results highlight a need for continued research on the role of media, especially online media, in research dissemination.
| S-EPMC6368156 | biostudies-literature
Project description:BackgroundSocial media is often used for health communication and can facilitate fast information exchange. Despite its increasing use, little is known about child health information sharing and engagement over social media.ObjectiveThe primary objectives of this study are to systematically describe the content of social media posts about child pain and sleep and identify the level of research evidence in these posts. The secondary objective is to examine user engagement with information shared over social media.MethodsTwitter, Instagram, and Facebook were searched by members of the research team over a 2-week period using a comprehensive search strategy. Codes were used to categorize the content of posts to identify the frequency of content categories shared over social media platforms. Posts were evaluated by content experts to determine the frequency of posts consistent with existing research evidence. User engagement was analyzed using Netlytic, a social network analysis program, to examine visual networks illustrating the level of user engagement.ResultsFrom the 2-week period, nearly 1500 pain-related and 3800 sleep-related posts were identified and analyzed. Twitter was used most often to share knowledge about child pain (639/1133, 56.40% of posts), and personal experiences for child sleep (2255/3008, 75.00% of posts). For both topics, Instagram posts shared personal experiences (53/68, 78% pain; 413/478, 86.4% sleep), Facebook group posts shared personal experiences (30/49, 61% pain; 230/345, 66.7% sleep) and Facebook pages shared knowledge (68/198, 34.3% pain; 452/1026, 44.05% sleep). Across platforms, research evidence was shared in 21.96% (318/1448) of pain- and 9.16% (445/4857) of sleep-related posts; 5.38% (61/1133) of all pain posts and 2.82% (85/3008) of all sleep posts shared information inconsistent with the evidence, while the rest were absent of evidence. User interactions were indirect, with mostly one-way, rather than reciprocal conversations.ConclusionsSocial media is commonly used to discuss child health, yet the majority of posts do not contain research evidence, and user engagement is primarily one-way. These findings represent an opportunity to expand engagement through open conversations with credible sources. Research and health care communities can benefit from incorporating specific information about evidence within social media posts to improve communication with the public and empower users to distinguish evidence-based content better. Together, these findings have identified potential gaps in social media communication that may be informative targets to guide future strategies for improving the translation of child health evidence over social media.
| S-EPMC6715344 | biostudies-literature
Project description:We investigated the content of liberal and conservative news media Facebook posts on race and ethnic health disparities. A total of 3,327,360 liberal and conservative news Facebook posts from the United States (US) from January 2015 to May 2022 were collected from the Crowd Tangle platform and filtered for race and health-related keywords. Qualitative content analysis was conducted on a random sample of 1750 liberal and 1750 conservative posts. Posts were analyzed for a continuum of hate speech using a newly developed method combining faceted Rasch item response theory with deep learning. Across posts referencing Asian, Black, Latinx, Middle Eastern, and immigrants/refugees, liberal news posts had lower hate scores compared to conservative posts. Liberal news posts were more likely to acknowledge and detail the existence of racial/ethnic health disparities, while conservative news posts were more likely to highlight the negative consequences of protests, immigration, and the disenfranchisement of Whites. Facebook posts from liberal and conservative news focus on different themes with fewer discussions of racial inequities in conservative news. Investigating the discourse on race and health in social media news posts may inform our understanding of the public's exposure to and knowledge of racial health disparities, and policy-level support for ameliorating these disparities.
| S-EPMC9960309 | biostudies-literature
Project description:We show that malicious COVID-19 content, including racism, disinformation, and misinformation, exploits the multiverse of online hate to spread quickly beyond the control of any individual social media platform. We provide a first mapping of the online hate network across six major social media platforms. We demonstrate how malicious content can travel across this network in ways that subvert platform moderation efforts. Machine learning topic analysis shows quantitatively how online hate communities are sharpening COVID-19 as a weapon, with topics evolving rapidly and content becoming increasingly coherent. Based on mathematical modeling, we provide predictions of how changes to content moderation policies can slow the spread of malicious content.
| S-EPMC8206165 | biostudies-literature
Project description:To examine current vaccine sentiment on social media by constructing and analyzing semantic networks of vaccine information from highly shared websites of Twitter users in the United States; and to assist public health communication of vaccines.Vaccine hesitancy continues to contribute to suboptimal vaccination coverage in the United States, posing significant risk of disease outbreaks, yet remains poorly understood.We constructed semantic networks of vaccine information from internet articles shared by Twitter users in the United States. We analyzed resulting network topology, compared semantic differences, and identified the most salient concepts within networks expressing positive, negative, and neutral vaccine sentiment.The semantic network of positive vaccine sentiment demonstrated greater cohesiveness in discourse compared to the larger, less-connected network of negative vaccine sentiment. The positive sentiment network centered around parents and focused on communicating health risks and benefits, highlighting medical concepts such as measles, autism, HPV vaccine, vaccine-autism link, meningococcal disease, and MMR vaccine. In contrast, the negative network centered around children and focused on organizational bodies such as CDC, vaccine industry, doctors, mainstream media, pharmaceutical companies, and United States. The prevalence of negative vaccine sentiment was demonstrated through diverse messaging, framed around skepticism and distrust of government organizations that communicate scientific evidence supporting positive vaccine benefits.Semantic network analysis of vaccine sentiment in online social media can enhance understanding of the scope and variability of current attitudes and beliefs toward vaccines. Our study synthesizes quantitative and qualitative evidence from an interdisciplinary approach to better understand complex drivers of vaccine hesitancy for public health communication, to improve vaccine confidence and vaccination coverage in the United States.
| S-EPMC5548132 | biostudies-literature
Project description:Multiparameter flow cytometry is a robust and reliable method for determining tumour DNA content applicable to formalin-fixed paraffin-embedded (FFPE) tissue. This study examined the clinical and pathological associations of DNA content in primary breast cancer using an improved multiparametric technique.The FFPE tissue from 201 primary breast cancers was examined and the cancers categorised according to their DNA content using multiparametric flow cytometry incorporating differential labelling of stromal and tumour cell populations. Mathematical modelling software (ModFit 3.2.1) was used to calculate the DNA index (DI) and percentage S-phase fraction (SPF%) for each tumour. Independent associations with clinical and pathological parameters were sought using backward stepwise Binary Logistic Regression (BLR) and Cox's Regression (CR) analysis.Tumours were grouped into four categories based on the DI of the tumour cell population. Low DI tumours (DI=0.76-1.14) associated with progesterone receptor-positive status (P=0.012, BLR), intermediate DI (DI=1.18-1.79) associated with p53 mutant tumours (P=0.001, BLR), high DI (DI1.80) tumours with human epidermal growth factor receptor 2 (HER2)-positive status (P=0.004, BLR) and 'multiploid tumours' (two or more tumour DNA peaks) did not show any significant associations. Tumours with high SPF% (10%) independently associated with poor overall survival (P=0.027, CR).Multiparametric flow analysis of FFPE tissue can accurately assess tumour DNA content. Tumour sub-populations associated with biomarkers of prognosis or likely response to therapy. The alterations in DNA content present the potential for greater understanding of the mechanisms underlying clinically significant biomarker changes in primary breast cancer.
| S-EPMC3590677 | biostudies-other
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:This work investigates the ability to classify misinformation in online social media networks in a manner that avoids the need for ground truth labels. Rather than approach the classification problem as a task for humans or machine learning algorithms, this work leverages user-user and user-media (i.e.,media likes) interactions to infer the type of information (fake vs. authentic) being spread, without needing to know the actual details of the information itself. To study the inception and evolution of user-user and user-media interactions over time, we create an experimental platform that mimics the functionality of real-world social media networks. We develop a graphical model that considers the evolution of this network topology to model the uncertainty (entropy) propagation when fake and authentic media disseminates across the network. The creation of a real-world social media network enables a wide range of hypotheses to be tested pertaining to users, their interactions with other users, and with media content. The discovery that the entropy of user-user and user-media interactions approximate fake and authentic media likes, enables us to classify fake media in an unsupervised learning manner.
| S-EPMC7994853 | biostudies-literature