The subgroup analysis of 10 studies (those which focused only on Facebook) showed an identical pooled proportion of 0.67 (95% CI: 0.38–0.86) with a homogenous characteristic ( p-value for heterogeneity = 0.09, I2 = 100%). The forest plots for each scenario are illustrated in Figure 3, Figure 4 and Figure 5, respectively. Considering the scope of the studies (Facebook, Twitter, Instagram, all three, or unknown/not specified), the year of the publication (before 2018 and after 2018), and the sample size of the studies (below 600 and above 600), a subgroup analysis was used to determine the effect of this variation on the pooled results, and the results of these analyses are reported in Table S1, Supplementary Materials. The forest plots for the studies are found in Figure 2. It indicates a wide range of values in the outcomes of the studies, and according to I2 = 100%, it was estimated that approximately all of the variance was due to heterogeneity ( Figure 1). Here, Q is distributed as a chi-square statistic with k (number of studies) minus 1. Confidence intervals are based on the Clopper–Pearson interval (exact binomial interval). These results are based on the random-effects model. Of the twenty studies, nine reported a proportion lower than 50% for a positive effect ( Figure 1). (2019) pointed out in their study how although most of the study participants (80%) had come across expressions of active suicidal thoughts, activity and language use on Instagram did not predict acute suicidality. Another study discussed how image-based social media such as Instagram may become a source of mental health-related information and a tool for health communication. Content Advisory warnings were only generated by one-third of the relevant hashtags. The term #MySecretFamily had approximately 900,000 search results at the time. “#MySecretFamily” was a popular term that described the broader community of NSSI and mental illness. The sample of 201 Instagram posts led to the identification of 10 ambiguous NSSI hashtags, with some common terms including #selfinjuryy and #MySecretFamily. This study focused on content posted on Instagram between 18 June 2014, and 30 June 2014, to evaluate the meaning, popularity, and content advisory warnings related to ambiguous non-suicidal self-injury (NSSI) hashtags on Instagram. Of the three studies focusing exclusively on Instagram, one study found a relationship between consistent Instagram usage and negative body image and self-harm. Across these studies, the general trend was that Instagram may be a contributing factor in causing body image and self-harm issues in young people. Three of the studies included in the analysis focused on Instagram. Therefore, evidence from studies focusing on Twitter seems to suggest a positive relationship between social media and mental health. Four overarching themes were derived from the tweets collected: (1) A sense of community (2) raising awareness and combatting stigma (3) a safe space for expression and (4) coping and empowerment. (2017) conducted a study using Text mining methods for Twitter to collect and organize tweets from the hashtag #WhyWeTweetMH. The results of their study indicated that despite the limitation of 180 characters per tweet, people who were depressed showed signs of depression in their tweets significantly before the actual diagnosis, resulting in the viable option to use Twitter as a predictive depression evaluation tool for clinicians. (2017) built models to predict the emergence of depression and PTSD by using learning algorithms analyzing the linguistic patterns in Tweets of the sample months before a clinical diagnosis of depression. Furthermore, Twitter has been useful in the detection and anticipation of mental health issues.
0 Comments
Leave a Reply. |