Introduction Social media has created opportunities for children to gather social support online (Blackwell et al., 2016; Gonzales, 2017; Jackson, Bailey, & Foucault Welles, 2018; Khasawneh, Rogers, Bertrand, Madathil, & Gramopadhye, 2019; Ponathil, Agnisarman, Khasawneh, Narasimha, & Madathil, 2017). However, social media also has the potential to expose children and adolescents to undesirable behaviors. Research showed that social media can be used to harass, discriminate (Fritz & Gonzales, 2018), dox (Wood, Rose, & Thompson, 2018), and socially disenfranchise children (Page, Wisniewski, Knijnenburg, & Namara, 2018). Other research proposes that social media use might be correlated to the significant increase in suicide rates and depressive symptoms among children and adolescents in the past ten years (Mitchell, Wells, Priebe, & Ybarra, 2014). Evidence based research suggests that suicidal and unwanted behaviors can be promulgated through social contagion effects, which model, normalize, and reinforce self-harming behavior (Hilton, 2017). These harmful behaviors and social contagion effects may occur more frequently through repetitive exposure and modelling via social media, especially when such content goes “viral” (Hilton, 2017). One example of viral self-harming behavior that has generated significant media attention is the Blue Whale Challenge (BWC). The hearsay about this challenge is that individuals at allmore »
Classification of Health-Related Social Media Posts: Evaluation of Post Content–Classifier Models and Analysis of User Demographics
Background The increasing volume of health-related social media activity, where users connect, collaborate, and engage, has increased the significance of analyzing how people use health-related social media. Objective The aim of this study was to classify the content (eg, posts that share experiences and seek support) of users who write health-related social media posts and study the effect of user demographics on post content. Methods We analyzed two different types of health-related social media: (1) health-related online forums—WebMD and DailyStrength—and (2) general online social networks—Twitter and Google+. We identified several categories of post content and built classifiers to automatically detect these categories. These classifiers were used to study the distribution of categories for various demographic groups. Results We achieved an accuracy of at least 84% and a balanced accuracy of at least 0.81 for half of the post content categories in our experiments. In addition, 70.04% (4741/6769) of posts by male WebMD users asked for advice, and male users’ WebMD posts were more likely to ask for medical advice than female users’ posts. The majority of posts on DailyStrength shared experiences, regardless of the gender, age group, or location of their authors. Furthermore, health-related posts on Twitter and Google+ were more »
- Award ID(s):
- 1901379
- Publication Date:
- NSF-PAR ID:
- 10161340
- Journal Name:
- JMIR Public Health and Surveillance
- Volume:
- 6
- Issue:
- 2
- Page Range or eLocation-ID:
- e14952
- ISSN:
- 2369-2960
- Sponsoring Org:
- National Science Foundation
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