Feeling bad on Facebook: depression disclosures by college students on a social networking site

Moreno, Megan A.; Jelenchick, Lauren A.; Egan, Katie G.; Cox, Elizabeth; Young, Henry N.; Gannon, Kerry; Becker, Tara

doi:10.1002/da.20805

Cited by 498 publications

(347 citation statements)

References 33 publications

(48 reference statements)

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“…Cohen's Kappa statistic is used to evaluate the extent to which there was overall agreement in the coding of the presence or absence of depression symptom references on a profile. In our previous work, Cohen's kappa was 0.79 for depression symptom references [5].…”

Section: Coder Trainingmentioning

confidence: 85%

“…These disclosures may include references to depression symptom in the form of "status updates," personally written text describing the profile owner's current experiences. Previous work found that approximately a quarter of publicly available Facebook profiles of undergraduates displayed depression symptom references, and a small proportion of profiles displayed depression symptoms in patterns consistent with the Diagnostic and Statistical Manual (DSM-IV) criteria for a major depressive episode (MDE) [4,5]. Additionally, display of depression symptoms was positively associated with reporting depression symptoms using a clinical screen [6].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Evaluating Displayed Depression Symptoms on Social Media Sites

Moreno¹,

Kelleher²,

Pumper³

2013

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Social networking sites (SNSs) are immensely popular and allow for display of personal information, including references to depression. Evaluating displayed content on a SNS for research purposes requires a systematic approach and a precise data collection instrument. The purpose of this paper is to describe one approach to the development of a research codebook for depression so that others may develop and test their own codebooks for use in SNS research. The depression SNS research codebook was grounded in ethics, theory and clinical criteria. The key elements in the codebook developmental process included an iterative team approach to develop variables of interest and data collection sheet layout. Training protocols involve coding practice and reliability assessments. Interrater reliability remains a critical assessment tool. Codebook successes include consistently high interrater reliability. Challenges include time investment in coder training, SNS server changes, and social or cultural norms regarding public displays of mental health. We provide detailed information about a systematic approach to codebook development so that other researchers may use this structure to develop and test their own codebooks for use in SNS research. Future directions for the codebook include expanding areas of interest such as anxiety or other depression evaluation criteria, and expansion to other SNSs such as Twitter.

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Section: Coder Trainingmentioning

confidence: 85%

Section: Introductionmentioning

confidence: 99%

Evaluating Displayed Depression Symptoms on Social Media Sites

Moreno¹,

Kelleher²,

Pumper³

2013

View full text Add to dashboard Cite

show abstract

“…But the study triggered an understandable flurry of outrage and concern for failing to inform or obtain consent from participants before attempting to manipulate their moods, as reflected in their Facebook updates (Calvo, Peters and D'Mello 2015). Moreno and Jelenchick (2011) manually evaluated a year's worth of status updates of 200 college students. Each update was coded against the DSM IV criteria for depression.…”

Section: Facebookmentioning

confidence: 99%

Natural language processing in mental health applications using non-clinical texts

Calvo¹,

Milne²,

Hussain³

et al. 2017

Nat. Lang. Eng.

224

148

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Natural language processing (NLP) techniques can be used to make inferences about peoples' mental states from what they write on Facebook, Twitter and other social media. These inferences can then be used to create online pathways to direct people to health information and assistance and also to generate personalized interventions. Regrettably, the computational methods used to collect, process and utilize online writing data, as well as the evaluations of these techniques, are still dispersed in the literature. This paper provides a taxonomy of data sources and techniques that have been used for mental health support and intervention. Specifically, we review how social media and other data sources have been used to detect emotions and identify people who may be in need of psychological assistance; the computational techniques used in labeling and diagnosis; and finally, we discuss ways to generate and personalize mental health interventions. The overarching aim of this scoping review is to highlight areas of research where NLP has been applied in the mental health literature and to help develop a common language that draws together the fields of mental health, human-computer interaction and NLP.

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“…and the specific event they are attempting to predict. In [7], Moreno et al first demonstrated that social networking sites could be a potential avenue for identifying students suffering from depression. The prevalence rates found for depression disclosed on Facebook corresponded to previous works in which such information was self-reported.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Recently, online social media networks have become one such informal resource. Research has shown that at-risk individuals are turning to contemporary technologies (forums, micro-blogs) to express their deepest struggles without having to face someone directly [6,7]. As a result, suicide risk factors and warning signs have been seen in a new arena.…”

Section: Introductionmentioning

confidence: 99%

Detection of suicide-related posts in Twitter data streams

Vioulès¹,

Moulahi²,

Azé³

et al. 2018

IBM J. Res. & Dev.

120

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Suicidal ideation detection in online social networks is an emerging research area with major challenges. Recent research has shown that the publicly available information spread across social media platforms holds valuable indicators to effectively detecting individuals with suicidal intentions. The key challenge of suicide prevention is understanding and detecting the complex risk factors and warning signs that may precipitate the event. In this paper, we present a new approach that uses the social media platform Twitter to quantify suicide-warning signs for individuals and to detect posts containing suicide-related content. The main originality of this approach is the automatic identification of sudden changes in a user's online behavior. To detect such changes, we combine natural language processing techniques to aggregate behavioral and textual features and pass these features through a martingale framework, which is widely used for change detection in data streams. Experiments show that our text-scoring approach effectively captures warning signs in text compared to traditional machine learning classifiers. Additionally, the application of the martingale framework highlights changes in online behavior and shows promise for detecting behavioral changes in at-risk individuals.

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Feeling bad on Facebook: depression disclosures by college students on a social networking site

Cited by 498 publications

References 33 publications

Evaluating Displayed Depression Symptoms on Social Media Sites

Evaluating Displayed Depression Symptoms on Social Media Sites

Natural language processing in mental health applications using non-clinical texts

Detection of suicide-related posts in Twitter data streams

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