Characterizing and predicting postpartum depression from shared facebook data

By M. de Choudhury, Scott Counts, Eric Horvitz, Aaron Hoff. Conference on Computer Supported Cooperative Work, 2014

378 citations recorded by the source corpus.

Subfields: Public Health, Environmental and Occupational Health, Social Psychology

View Paper: https://www.semanticscholar.org/paper/8b9b5c3dcadaa2d03f15dca2c60f348e33252041

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In Public Health, Environmental and Occupational Health

In Social Psychology

Citations in the corpus

Privacy is an essentially contested concept: a multi-dimensional analytic for mapping privacy (2016, background): “Privacy harms may arise for which individual control offers no protection or remedy, for example, when actions are taken based on group classifications [3], or new and unexpected insights are inferred from data that individuals have intentionally disclosed [4,5], or an individual’s sensitive persona”
Exploring Time-Dependent Concerns about Pregnancy and Childbirth from Search Logs (2015, neutral): “[10] postings.”
Mother, baby and Facebook makes three: does social media provide social support for new mothers? (2018, background): “As social media platforms actively encourage participants to share their current experience or state of mind, mothers’ use of such technologies not only reflects but also articulates, and in some cases predicts, their well-being (De Choudhury et al., 2013, 2014).”
Natural language processing in mental health applications using non-clinical texts† (2017, background): “To our knowledge, the only published attempt to do so automatically is by De Choudhury and Counts (2014), who recruited new mothers willing to share their Facebook data.”
Sensitive Self-disclosures, Responses, and Social Support on Instagram: The Case of #Depression (2017, methodology): “[18,20] used Twitter and Facebook data to detect and predict postpartum depression, using online behavioral signals (e.”
Recognizing Depression from Twitter Activity (2015, background): “Such approaches are applied to prediction of postpartum depression from Facebook and Twitter data [11, 13].”
Understanding Social Media Disclosures of Sexual Abuse Through the Lenses of Support Seeking and Anonymity (2016, methodology): “media data [18,19], such as using classifiers to detect posts discussing domestic abuse on reddit [54].”
Detecting depression and mental illness on social media: an integrative review (2017, positive): “Mentally ill users have been identified using screening surveys, their public sharing of a diagnosis on Twitter, or by their membership in an online forum, and they were distinguishable from control users by patterns in their language and online activity.”