Analyzing Trends of Loneliness Through Large-Scale Analysis of Social Media Postings: Observational Study
| Autor: | Elad Yom-Tov, Keren Mazuz |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
medicine.medical_specialty
Future studies social media Population 050109 social psychology online self-disclosure 050105 experimental psychology medicine loneliness Psychology 0501 psychology and cognitive sciences Social media education behavior online education.field_of_study Original Paper Public health 05 social sciences Loneliness Mental health BF1-990 UCLA Loneliness Scale Psychiatry and Mental health computer-based analysis Observational study medicine.symptom Social psychology text postings |
| Zdroj: | JMIR Mental Health JMIR Mental Health, Vol 7, Iss 4, p e17188 (2020) |
| ISSN: | 2368-7959 |
| Popis: | Background Loneliness has become a public health problem described as an epidemic, and it has been argued that digital behavior such as social media posting affects loneliness. Objective The aim of this study is to expand knowledge of the determinants of loneliness by investigating online postings in a social media forum devoted to loneliness. Specifically, this study aims to analyze the temporal trends in loneliness and their associations with topics of interest, especially with those related to mental health determinants. Methods We collected a total of 19,668 postings from 11,054 users in the loneliness forum on Reddit. We asked seven crowdsourced workers to imagine themselves as writing 1 of 236 randomly chosen posts and to answer the short-form UCLA Loneliness Scale. After showing that these postings could provide an assessment of loneliness, we built a predictive model for loneliness scores based on the posts’ text and applied it to all collected postings. We then analyzed trends in loneliness postings over time and their correlations with other topics of interest related to mental health determinants. Results We found that crowdsourced workers can estimate loneliness (interclass correlation=0.19) and that predictive models are correlated with reported loneliness scores (Pearson r=0.38). Our results show that increases in loneliness are strongly associated with postings to a suicidality-related forum (hazard ratio 1.19) and to forums associated with other detrimental behaviors such as depression and illicit drug use. Clustering demonstrates that people who are lonely come from diverse demographics and from a variety of interests. Conclusions The results demonstrate that it is possible for unrelated individuals to assess people’s social media postings for loneliness. Moreover, our findings show the multidimensional nature of online loneliness and its correlated behaviors. Our study shows the advantages of studying a hard-to-reach population through social media and suggests new directions for future studies. |
| Databáze: | OpenAIRE |
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