Proceedings Article•
Predicting Depression via Social Media
28 Jun 2013-Vol. 7, Iss: 1, pp 128-137
TL;DR: It is found that social media contains useful signals for characterizing the onset of depression in individuals, as measured through decrease in social activity, raised negative affect, highly clustered egonetworks, heightened relational and medicinal concerns, and greater expression of religious involvement.
Abstract: Major depression constitutes a serious challenge in personal and public health. Tens of millions of people each year suffer from depression and only a fraction receives adequate treatment. We explore the potential to use social media to detect and diagnose major depressive disorder in individuals. We first employ crowdsourcing to compile a set of Twitter users who report being diagnosed with clinical depression, based on a standard psychometric instrument. Through their social media postings over a year preceding the onset of depression, we measure behavioral attributes relating to social engagement, emotion, language and linguistic styles, ego network, and mentions of antidepressant medications. We leverage these behavioral cues, to build a statistical classifier that provides estimates of the risk of depression, before the reported onset. We find that social media contains useful signals for characterizing the onset of depression in individuals, as measured through decrease in social activity, raised negative affect, highly clustered egonetworks, heightened relational and medicinal concerns, and greater expression of religious involvement. We believe our findings and methods may be useful in developing tools for identifying the onset of major depression, for use by healthcare agencies; or on behalf of individuals, enabling those suffering from depression to be more proactive about their mental health.
Citations
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Proceedings Article•
16 May 2014TL;DR: Interestingly, using the authors' parsimonious rule-based model to assess the sentiment of tweets, it is found that VADER outperforms individual human raters, and generalizes more favorably across contexts than any of their benchmarks.
Abstract: The inherent nature of social media content poses serious challenges to practical applications of sentiment analysis. We present VADER, a simple rule-based model for general sentiment analysis, and compare its effectiveness to eleven typical state-of-practice benchmarks including LIWC, ANEW, the General Inquirer, SentiWordNet, and machine learning oriented techniques relying on Naive Bayes, Maximum Entropy, and Support Vector Machine (SVM) algorithms. Using a combination of qualitative and quantitative methods, we first construct and empirically validate a gold-standard list of lexical features (along with their associated sentiment intensity measures) which are specifically attuned to sentiment in microblog-like contexts. We then combine these lexical features with consideration for five general rules that embody grammatical and syntactical conventions for expressing and emphasizing sentiment intensity. Interestingly, using our parsimonious rule-based model to assess the sentiment of tweets, we find that VADER outperforms individual human raters (F1 Classification Accuracy = 0.96 and 0.84, respectively), and generalizes more favorably across contexts than any of our benchmarks.
3,299 citations
01 Jun 2014
TL;DR: A novel method for gathering data for a range of mental illnesses quickly and cheaply is presented, then analysis of four in particular: post-traumatic stress disorder, depression, bipolar disorder, and seasonal affective disorder are focused on.
Abstract: The ubiquity of social media provides a rich opportunity to enhance the data available to mental health clinicians and researchers, enabling a better-informed and better-equipped mental health field. We present analysis of mental health phenomena in publicly available Twitter data, demonstrating how rigorous application of simple natural language processing methods can yield insight into specific disorders as well as mental health writ large, along with evidence that as-of-yet undiscovered linguistic signals relevant to mental health exist in social media. We present a novel method for gathering data for a range of mental illnesses quickly and cheaply, then focus on analysis of four in particular: post-traumatic stress disorder (PTSD), depression, bipolar disorder, and seasonal affective disorder (SAD). We intend for these proof-of-concept results to inform the necessary ethical discussion regarding the balance between the utility of such data and the privacy of mental health related information.
570 citations
Cites background or methods or result from "Predicting Depression via Social Me..."
...Social engagement has been correlated with positive mental health outcomes (Greetham et al., 2011; Berkman et al., 2000; Organization, 2001; De Choudhury et al., 2013d), which is difficult to measure directly so we examine various ways in which this may be manifest in a user’s tweet stream: Tweet…...
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...Previous work has found signal in the ‘positive affect’ and ‘negative affect’ categories of the LIWC when applied to social media (including Twitter), so we examine their correlations separately, as well as in the context of other LIWC categories (De Choudhury et al., 2013a)....
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...Such topics have already been the focus of several studies (Coppersmith et al., 2014; De Choudhury et al., 2014; De Choudhury et al., 2013d; De Choudhury et al., 2013b; De Choudhury et al., 2013c; Ayers et al., 2013)....
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...Similarly, an increase in negative emotion and first person pronouns, and a decrease in third person pronouns, (via LIWC) is observed, as well as many manifestations of literature findings in the pattern of life of depressed users (e.g., social engagement, demographics) (De Choudhury et al., 2013d)....
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...…significant differences between depressed users (according to an internetadministered diagnostic battery): significant increases are expected in NegEmo, Anger, Pro1 and Pro3 and no change in PosEmo, given all previous work (Park et al., 2012; Chung and Pennebaker, 2007; De Choudhury et al., 2013d)....
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07 May 2016
TL;DR: This paper develops a statistical methodology to infer which individuals could undergo transitions from mental health discourse to suicidal ideation, and utilizes semi-anonymous support communities on Reddit as unobtrusive data sources to infer the likelihood of these shifts.
Abstract: History of mental illness is a major factor behind suicide risk and ideation. However research efforts toward characterizing and forecasting this risk is limited due to the paucity of information regarding suicide ideation, exacerbated by the stigma of mental illness. This paper fills gaps in the literature by developing a statistical methodology to infer which individuals could undergo transitions from mental health discourse to suicidal ideation. We utilize semi-anonymous support communities on Reddit as unobtrusive data sources to infer the likelihood of these shifts. We develop language and interactional measures for this purpose, as well as a propensity score matching based statistical approach. Our approach allows us to derive distinct markers of shifts to suicidal ideation. These markers can be modeled in a prediction framework to identify individuals likely to engage in suicidal ideation in the future. We discuss societal and ethical implications of this research.
513 citations
Cites background from "Predicting Depression via Social Me..."
...Linguistic attributes of shared content and social interactional patterns have been utilized to understand and infer risk to major depressive disorder [24, 49, 32, 16, 60, 70], postpartum depression [21, 22], addiction [47, 44], and other mental health concerns [35, 18, 17, 46]....
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Proceedings Article•
16 May 2014TL;DR: These findings reveal, for the first time, the kind of unique information needs that a social media like reddit might be fulfilling when it comes to a stigmatic illness, and expand the understanding of the role of the social web in behavioral therapy.
Abstract: Social media is continually emerging as a platform of information exchange around health challenges. We study mental health discourse on the popular social media:reddit. Building on findings about health information seeking and sharing practices in online forums, and social media like Twitter, we address three research challenges. First, we present a characterization of self-disclosure inmental illness communities on reddit. We observe individuals discussing a variety of concerns ranging from the daily grind to specific queries about diagnosis and treatment. Second, we build a statistical model to examine the factors that drive social support on mental health reddit communities. We also develop language models to characterize mental health social support, which are observed to bear emotional, informational, instrumental, and prescriptive information. Finally, we study disinhibition in the light of the dissociative anonymity that reddit’s throwaway accounts provide. Apart from promoting open conversations,such anonymity surprisingly is found to gather feedback that is more involving and emotionally engaging. Our findings reveal, for the first time, the kind of unique information needs that a social media like reddit might be fulfilling when it comes to a stigmatic illness. They also expand our understanding of the role of the social web in behavioral therapy.
492 citations
TL;DR: A layered, hierarchical model for translating raw sensor data into markers of behaviors and states related to mental health is provided, focused principally on smartphones, but also including studies of wearables, social media, and computers.
Abstract: Sensors in everyday devices, such as our phones, wearables, and computers, leave a stream of digital traces. Personal sensing refers to collecting and analyzing data from sensors embedded in the context of daily life with the aim of identifying human behaviors, thoughts, feelings, and traits. This article provides a critical review of personal sensing research related to mental health, focused principally on smartphones, but also including studies of wearables, social media, and computers. We provide a layered, hierarchical model for translating raw sensor data into markers of behaviors and states related to mental health. Also discussed are research methods as well as challenges, including privacy and problems of dimensionality. Although personal sensing is still in its infancy, it holds great promise as a method for conducting mental health research and as a clinical tool for monitoring at-risk populations and providing the foundation for the next generation of mobile health (or mHealth) interventions.
451 citations
Cites result from "Predicting Depression via Social Me..."
...In a large sample of Twitter users, rates of depression were consistent with geographical, demographic, and seasonal patterns reported by the US Centers for Disease Control and Prevention (CDC) (De Choudhury et al. 2013a)....
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References
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TL;DR: The CES-D scale as discussed by the authors is a short self-report scale designed to measure depressive symptomatology in the general population, which has been used in household interview surveys and in psychiatric settings.
Abstract: The CES-D scale is a short self-report scale designed to measure depressive symptomatology in the general population. The items of the scale are symptoms associated with depression which have been used in previously validated longer scales. The new scale was tested in household interview surveys and in psychiatric settings. It was found to have very high internal consistency and adequate test- retest repeatability. Validity was established by pat terns of correlations with other self-report measures, by correlations with clinical ratings of depression, and by relationships with other variables which support its construct validity. Reliability, validity, and factor structure were similar across a wide variety of demographic characteristics in the general population samples tested. The scale should be a useful tool for epidemiologic studies of de pression.
48,339 citations
"Predicting Depression via Social Me..." refers background in this paper
...The CES-D is a 20-item self-report scale that is designed to measure depressive symptoms in the general population (Radloff, 1977), and is one of the most common screening tests used by clinicians and psychiatrists for the purpose....
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...With CES-D, typically three groups of depression severity are calculated (Radloff, 1977): low (0-15), mild to moderate (16-22), and high range (23-60)....
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TL;DR: Notably, major depressive disorder is a common disorder, widely distributed in the population, and usually associated with substantial symptom severity and role impairment, and while the recent increase in treatment is encouraging, inadequate treatment is a serious concern.
Abstract: ContextUncertainties exist about prevalence and correlates of major depressive
disorder (MDD).ObjectiveTo present nationally representative data on prevalence and correlates
of MDD by Diagnostic and Statistical Manual of Mental Disorders,
Fourth Edition (DSM-IV) criteria, and on study
patterns and correlates of treatment and treatment adequacy from the recently
completed National Comorbidity Survey Replication (NCS-R).DesignFace-to-face household survey conducted from February 2001 to December
2002.SettingThe 48 contiguous United States.ParticipantsHousehold residents ages 18 years or older (N = 9090) who responded
to the NCS-R survey.Main Outcome MeasuresPrevalence and correlates of MDD using the World Health Organization's
(WHO) Composite International Diagnostic Interview (CIDI), 12-month severity
with the Quick Inventory of Depressive Symptomatology Self-Report (QIDS-SR),
the Sheehan Disability Scale (SDS), and the WHO disability assessment scale
(WHO-DAS). Clinical reinterviews used the Structured Clinical Interview for DSM-IV.ResultsThe prevalence of CIDI MDD for lifetime was 16.2% (95% confidence interval
[CI], 15.1-17.3) (32.6-35.1 million US adults) and for 12-month was 6.6% (95%
CI, 5.9-7.3) (13.1-14.2 million US adults). Virtually all CIDI 12-month cases
were independently classified as clinically significant using the QIDS-SR,
with 10.4% mild, 38.6% moderate, 38.0% severe, and 12.9% very severe. Mean
episode duration was 16 weeks (95% CI, 15.1-17.3). Role impairment as measured
by SDS was substantial as indicated by 59.3% of 12-month cases with severe
or very severe role impairment. Most lifetime (72.1%) and 12-month (78.5%)
cases had comorbid CIDI/DSM-IV disorders, with MDD
only rarely primary. Although 51.6% (95% CI, 46.1-57.2) of 12-month cases
received health care treatment for MDD, treatment was adequate in only 41.9%
(95% CI, 35.9-47.9) of these cases, resulting in 21.7% (95% CI, 18.1-25.2)
of 12-month MDD being adequately treated. Sociodemographic correlates of treatment
were far less numerous than those of prevalence.ConclusionsMajor depressive disorder is a common disorder, widely distributed in
the population, and usually associated with substantial symptom severity and
role impairment. While the recent increase in treatment is encouraging, inadequate
treatment is a serious concern. Emphasis on screening and expansion of treatment
needs to be accompanied by a parallel emphasis on treatment quality improvement.
7,706 citations
"Predicting Depression via Social Me..." refers background in this paper
...It is also well-established that people suffering from MDD tend to focus their attention on unhappy and unflattering information, to interpret ambiguous information negatively, and to harbor pervasively pessimistic beliefs (Kessler et al., 2003; Rude et al., 2004)....
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Book•
01 Oct 2000
4,025 citations
"Predicting Depression via Social Me..." refers methods in this paper
...The best performing classifier was found to be a Support Vector Machine classifier with a radial-basis function (RBF) kernel (Duda et al., 2000)....
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