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Analysis of user-generated content from online social communities to characterise and predict depression degree
16 references·1 citing·52 second hopsampled
Analysis of user-generated content from online social communities to characterise and predict depression degree (2017)Analysis of user-generated content from o…Predicting Depression via Social Media (2021)Predicting Depression via Soc…A Framework for Classifying Online Mental Health-Related Communities With an Interest in Depression (2016)A Framework for Classifying O…Why do social network site users share information on Facebook and Twitter? (2015)Why do social network site us…Motivations for sharing information and social support in social media: A comparative analysis of F acebook, T witter, D elicious, Y ou T ube, and F lickr (2015)Motivations for sharing infor…Mental health research priorities for Europe (2015)Mental health research priori…Information encountering on social media and tacit knowledge sharing (2015)Information encountering on s…From ADHD to SAD: Analyzing the Language of Mental Health on Twitter through Self-Reported Diagnoses (2015)From ADHD to SAD: Analyzing t…Detecting Changes in Suicide Content Manifested in Social Media Following Celebrity Suicides (2015)Detecting Changes in Suicide …Affective and Content Analysis of Online Depression Communities (2014)Affective and Content Analysi…Global burden of disease attributable to mental and substance use disorders: findings from the Global Burden of Disease Study 2010 (2013)Global burden of disease attr…Activities on Facebook Reveal the Depressive State of Users (2013)Activities on Facebook Reveal…Feeling bad on Facebook: depression disclosures by college students on a social networking site (2011)Feeling bad on Facebook: depr…Validation of the Center for Epidemiologic Studies Depression Scale—Revised (CESD-R): Pragmatic depression assessment in the general population (2010)Validation of the Center for …Reading Between the Lines: The Lay Assessment of Subclinical Depression From Written Self-Descriptions (2010)Reading Between the Lines: Th…The Hamilton Depression Rating Scale: Has the Gold Standard Become a Lead Weight? (2004)The Hamilton Depression Ratin…Affective Norms for English Words (ANEW): Instruction manual and affective ratings (1999)Affective Norms for English W…Exploring the dominant features of social media for depression detection (2019)Exploring the dominant featur…Intrinsic Motivation and Self-Determination in Human Behavior (1985)Self-Efficacy: The Exercise of Control (1999)Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. 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References

  • 2013Global burden of disease attributable to mental and substance use disorders: findings from the Global Burden of Disease Study 20106,000 cited
  • 2021Predicting Depression via Social Media1,582 cited
  • 1999Affective Norms for English Words (ANEW): Instruction manual and affective ratings1,435 cited
  • 2004The Hamilton Depression Rating Scale: Has the Gold Standard Become a Lead Weight?1,148 cited
  • 2010Validation of the Center for Epidemiologic Studies Depression Scale—Revised (CESD-R): Pragmatic depression assessment in the general population648 cited
  • 2011Feeling bad on Facebook: depression disclosures by college students on a social networking site604 cited
  • 2015From ADHD to SAD: Analyzing the Language of Mental Health on Twitter through Self-Reported Diagnoses342 cited
  • 2015Motivations for sharing information and social support in social media: A comparative analysis of F acebook, T witter, D elicious, Y ou T ube, and F lickr324 cited
  • 2014Affective and Content Analysis of Online Depression Communities225 cited
  • 2015Mental health research priorities for Europe217 cited
  • 2015Detecting Changes in Suicide Content Manifested in Social Media Following Celebrity Suicides147 cited
  • 2015Why do social network site users share information on Facebook and Twitter?132 cited
  • 2013Activities on Facebook Reveal the Depressive State of Users128 cited

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2015
Information encountering on social media and tacit knowledge sharing
93 cited
  • 2016A Framework for Classifying Online Mental Health-Related Communities With an Interest in Depression84 cited
  • 2010Reading Between the Lines: The Lay Assessment of Subclinical Depression From Written Self-Descriptions84 cited
  • Cited by

    • 2019Exploring the dominant features of social media for depression detection59 cited