Background: Instagram is increasingly becoming a platform on which visual communication of cancer takes place, but few studies have investigated the content and effects. In particular, a paucity of research has evaluated the effects of visual communication of cancer on participative engagement outcomes.
Objective: The objective of our study was to investigate cancer-related beliefs and emotions shared on Instagram and to examine their effects on participative engagement outcomes including likes, comments, and social support.
Methods: This study analyzed the content of 441 posts of #melanomasucks on Instagram and assessed the effects of the content characteristics on outcomes, including the number of likes and comments and types of social support using group least absolute shrinkage and selection operator logistic regression.
Results: Posts about controlling melanoma were most frequent (271/441, 61.5%), followed by 240 (54.4%) posts about outcomes of having melanoma. Ninety posts (20.4%) were about the causes of melanoma. A greater number of posts expressed positive (159/441, 36.1%) than negative emotions (100/441, 22.7%). Eighty posts (18.1%) expressed hope, making it the most frequently expressed emotion; 49 posts expressed fear (11.1%), 46 were humorous (10.4%), and 46 showed sadness (10.4%). Posts about self behavior as a cause of melanoma decreased likes (P<.001) and social support comments (P=.048). Posts about physical consequences of melanoma decreased likes (P=.02) but increased comments (P<.001) and emotional social support (P<.001); posts about melanoma treatment experience increased comments (P=.03) and emotional social support (P<.001). None of the expressions of positive emotions increased likes, comments, or social support. Expression of anger increased the number of likes (P<.001) but those about fear (P<.001) and joy (P=.006) decreased the number of likes. Posts about fear (P=.003) and sadness (P=.003) increased emotional social support. Posts showing images of melanoma or its treatment on the face or body parts made up 21.8% (96/441) of total posts. Inclusion of images increased the number of comments (P=.001).
Conclusions: To our knowledge, this is the first investigation of the content and effects of user-generated visual cancer communication on social media. The findings show where the self-expressive and social engagement functions of #melanomasucks converge and diverge, providing implications for extending research on the commonsense model of illness and for developing conceptual frameworks explaining participative engagement on social media.
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http://dx.doi.org/10.2196/10501 | DOI Listing |
Sci Rep
December 2024
College of Animal Science and Technology, Inner Mongolia Minzu University, Tongliao, 028000, China.
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December 2024
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Laboratoire Campus de Biotechnologies Végétales, Département de Biologie Végétale, Faculté des Sciences et Techniques, Université Cheikh Anta Diop, Dakar-Fann, Dakar, 10700, Senegal.
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December 2024
School of Civil Engineering, Architecture and Environment, Hubei University of Technology, Wuhan, China.
The dolomite dust-emulsified asphalt composite (DAC) with excellent mechanical properties was successfully prepared using alkali activation. The effects of different alkali concentrations and emulsified asphalt contents on the mechanical properties of the materials were studied. And the micro-mechanisms of its mechanical performance changes were analyzed through SEM and XRD characterization.
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December 2024
Institute for Agro-Environmental Sciences, National Agriculture and Food Research Organization (NARO), Tsukuba, Ibaraki, Japan.
Legume content (LC) in grass-legume mixtures is important for assessing forage quality and optimizing fertilizer application in meadow fields. This study focuses on differences in LC measurements obtained from unmanned aerial vehicle (UAV) images and ground surveys based on dry matter assessments in seven meadow fields in Hokkaido, Japan. We propose a UAV-based LC (LC) estimation and mapping method using a land cover map from a simple linear iterative clustering (SLIC) algorithm and a random forest (RF) classifier.
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