Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet

The aim of this study was to investigate whether problematic social networking site (SNS) use (i.e., degree of dependent relationship with SNSs), total SNS time/day, total SNS friends, and specific SNS activities were related to body image (BI), self-esteem (SE), and eating disorder (ED) symptoms/concerns. A sample of young adults ( N = 147) completed an online survey which measured SNS usage, problematic SNS use, BI, SE, and ED symptom/concerns. The findings revealed that females and males spent 4.1 ± 3.9 and 2.9 ± 2.8 hr on SNS, respectively, with the majority of time spent lurking (i.e., looking at another users’ profile but not actually communicating with them). Furthermore, problematic SNS use was found to be related to BI, SE, and ED symptoms/concerns. Moreover, SNS activities, such as lurking and posting comments on others’ profiles, were found to be related to BI, whereas SNS total time was found to be related to ED symptoms/concerns. Overall, this study demonstrates the possible correlational influence of SNSs on BI, SE, and ED symptoms/concerns.

The #fitspo ‘tag’ is a recent trend on Instagram, which is used on posts to motivate others towards a healthy lifestyle through exercise/eating habits. This study used a mixed-methods approach consisting of text and network analysis via the Netlytic program (N = 10,000 #fitspo posts), and content analysis of #fitspo images (N = 122) was used to examine author and image characteristics. Results suggest that #fitspo posts may motivate through appearance-mediated themes, as the largest content categories (based on the associated text) were ‘feeling good’ and ‘appearance’. Furthermore, #fitspo posts may create peer influence/support as personal (opposed to non-personal) accounts were associated with higher popularity of images (i.e. number of likes/followers). Finally, most images contained posed individuals with some degree of objectification.

The present study aimed to explore using popular technology that people already have/use as a health promotion tool, in a campus wide social media health promotion initiative, entitled #LancerHealth. During a two-week period the university community was asked to share photos on Twitter and Instagram of What does being healthy on campus look like to you?, while tagging the image with #LancerHealth. All publically tagged media was collected using the Netlytic software and analysed. Text analysis (N=234 records, Twitter; N=141 records, Instagram) revealed that the majority of the conversation was positive and focused on health and the university. Social network analysis, based on five network properties, showed a small network with little interaction. Lastly, photo coding analysis (N=71 unique image) indicated that the majority of the shared images were of physical activity (52%) and on campus (80%). Further research into this area is warranted.

Background: The scientific community is just beginning to uncover the potential long-term effects of COVID-19, and one way to start gathering information is by examining the present discourse on the topic. The conversation about long COVID-19 on Twitter provides insight into related public perception and personal experiences. Objective: The aim of this study was to investigate the #longCOVID and #longhaulers conversations on Twitter by examining the combined effects of topic discussion and social network analysis for discovery on long COVID-19. Methods: A multipronged approach was used to analyze data (N=2500 records from Twitter) about long COVID-19 and from people experiencing long COVID-19. A text analysis was performed by both human coders and Netlytic, a cloud-based text and social networks analyzer. The social network analysis generated Name and Chain networks that showed connections and interactions between Twitter users. Results: Among the 2010 tweets about long COVID-19 and 490 tweets by COVID-19 long haulers, 30,923 and 7817 unique words were found, respectively. For both conversation types, “#longcovid” and “covid” were the most frequently mentioned words; however, through visually inspecting the data, words relevant to having long COVID-19 (ie, symptoms, fatigue, pain) were more prominent in tweets by COVID-19 long haulers. When discussing long COVID-19, the most prominent frames were “support” (1090/1931, 56.45%) and “research” (435/1931, 22.53%). In COVID-19 long haulers conversations, “symptoms” (297/483, 61.5%) and “building a community” (152/483, 31.5%) were the most prominent frames. The social network analysis revealed that for both tweets about long COVID-19 and tweets by COVID-19 long haulers, networks are highly decentralized, fragmented, and loosely connected. Conclusions: This study provides a glimpse into the ways long COVID-19 is framed by social network users. Understanding these perspectives may help generate future patient-centered research questions.