Koustuv Saha|John Torous|Sindhu Kiranmai Ernala|Conor Rizuto|Amanda Stafford|Munmun De Choudhury
A computational study of mental health awareness campaigns on social media
Georgia Institute of Technology|Beth Israel Deaconess Medical Center|Harvard University|Georgia Institute of Technology|Beth Israel Deaconess Medical Center|Harvard University|Beth Israel Deaconess Medical Center|Harvard University|Georgia Institute of Technology
public discourse continues progress online important mental health advocates public health officials curious parties stakeholders ranging researchers affected issue aware advancing new mediums public share content ranging useful resources selfhelp tips personal struggles respect illness stigmatization better understanding new public discourse mental health often framed social media campaigns help perpetuate allocation sparse mental health resources need educational awareness usefulness community opportunity reach seeking help right moment objective study understand nature engagement around mental health content shared mental health campaigns specifically mytipsformentalhealth twitter around world mental health awareness day 2017 collected 14217 twitter posts 10805 unique users september october 2017 contained hashtag mytipsformentalhealth involvement domain experts handlabeled 700 posts categorized fact b stigmatizing c inspirational medicalclinical tip e resource related f lifestyle social tip personal view g topic creating seed machine learning classifier used unsupervised semi supervised methods classify posts various expert identified topical categories also performed content analysis understand information different topics spread social networks support vector machine classification algorithm achieved mean crossvalidation accuracy 081 accuracy 064 unseen data found inspirational twitter posts spread mean 417 retweets stigmatizing content second mean 366 retweets classification social mediarelated mental health interactions offers valuable insights public sentiment well window evolving world online selfhelp varied resources within results suggest important role social mediabased peer support guide information seekers useful content local resources also illuminate sociallyinsular aspects stigmatization however results also reflect challenges quantifying heterogeneity mental health content social media need novel machine learning methods customized challenges field