2018
DOI: 10.1073/pnas.1802331115
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Facebook language predicts depression in medical records

Abstract: SignificanceDepression is disabling and treatable, but underdiagnosed. In this study, we show that the content shared by consenting users on Facebook can predict a future occurrence of depression in their medical records. Language predictive of depression includes references to typical symptoms, including sadness, loneliness, hostility, rumination, and increased self-reference. This study suggests that an analysis of social media data could be used to screen consenting individuals for depression. Further, soci… Show more

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Cited by 488 publications
(405 citation statements)
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References 43 publications
(46 reference statements)
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“…For example, text analysis can map the lifecycle of social movements (Ahmed, Jaidka, & Cho, 2017) and indicate mental health diagnoses (Eichstaedt et al, 2018). The current paper adopts this approach, paired with social science theory, empirical evidence, and exploratory techniques, to predict the amount of time a pet will spend available online (Study 1) and the verbal correlates of pet adoption (e.g., text features that define adopted vs. unadopted profiles; Study 2).…”
Section: Language Patterns and Psychological Dynamicsmentioning
confidence: 99%
“…For example, text analysis can map the lifecycle of social movements (Ahmed, Jaidka, & Cho, 2017) and indicate mental health diagnoses (Eichstaedt et al, 2018). The current paper adopts this approach, paired with social science theory, empirical evidence, and exploratory techniques, to predict the amount of time a pet will spend available online (Study 1) and the verbal correlates of pet adoption (e.g., text features that define adopted vs. unadopted profiles; Study 2).…”
Section: Language Patterns and Psychological Dynamicsmentioning
confidence: 99%
“…There is growing interest in building a “social mediome,” using data derived from social media platforms that reveal individual‐ and population‐level health information to gain greater insight into patient health habits and facilitate treatment. Public health researchers now utilize data from websites such as Google, Twitter, and Facebook to more precisely examine health trends, diagnose illnesses, and even predict behavior such as depression and suicide …”
Section: Articlementioning
confidence: 99%
“…Topic models have been shown to be able to differentiate social affections in web-based texts (Bao et al, 2009), to extract emotional components, such as anger and anxiety, from a psychotherapy corpus (Imel et al, 2015), and to estimate a writer's emotional states from Facebook language to predict future diagnoses of depression better than clinical screening tests (Eichstaedt et al, 2018). Thus, topic model is a promising approach to extract and compare the meaning of emotional words.…”
Section: Awe In the Japanese Contextmentioning
confidence: 99%