Social media as a measurement tool of depression in populations

By M. de Choudhury, Scott Counts, Eric Horvitz. 2013

546 citations recorded by the source corpus.

Subfields: Applied Psychology, Experimental and Cognitive Psychology, Social Psychology, Medical & Health AI

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Citations in the corpus

Distilling the Outcomes of Personal Experiences: A Propensity-scored Analysis of Social Media (2017, methodology): “To this end, we start our analysis with a Twitter data archive corresponding to all public tweets in English from March 1 to May 31, 20145—selected to avoid strong seasonal phenomena, typically observed in winter, summer and early fall [26, 68].”
Quantifying Mental Health Signals in Twitter (2014, methodology): “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…”
The role of personality, age, and gender in tweeting about mental illness (2015, background): “This is in contrast to the strong differences in first person pronoun use among depressed individuals documented in the literature (Rude et al., 2004; Stirman and Pennebaker, 2001), confirmed in prior Twitter studies (Coppersmith et al., 2014a; De Choudhury et al., 2013) and replicated here.”
Natural language processing in mental health applications using non-clinical texts† (2017, methodology): “In another study, the same authors focused on measuring behavioral and emotional changes that could be indicative of postpartum depression (De Choudhury et al. 2013a).”
Self-Esteem, Daily Internet Use and Social Media Addiction as Predictors of Depression among Turkish Adolescents (2016, background): “Duran (1999) defined depression as a syndrome that includes symptoms of retardation and fuzziness in speech and movement; worthlessness, pettiness, weakness and reluctance; pessimistic emotions, thoughts and psychological states.”
Knowledge-aware Assessment of Severity of Suicide Risk for Early Intervention (2019, background): “So far, prior research studied the identification of signals for predicting the suicide risk, mental health conditions leading to suicide [15, 16, 47], psychological state and well-being [50, 51].”
Analyzing Biases in Human Perception of User Age and Gender from Text (2016, background): “…orientation (Volkova et al., 2014), income (Preot¸iuc-Pietro et al., 2015c), socio-economic status (Lam-pos et al., 2016), popularity (Lampos et al., 2014), personality (Schwartz et al., 2013) or mental illnesses (De Choudhury et al., 2013; Coppersmith et al., 2014; Preot¸iuc-Pietro et al., 2015a).”
Sensitive Self-disclosures, Responses, and Social Support on Instagram: The Case of #Depression (2017, background): “Research suggests that decrease in social activity, heightened social and medicinal concerns, increased negative affect, clustered ego-networks, and greater expression of religious involvement are social media signals that may characterize the onset of depression [19].”
Toward Reproducibility in Online Social Network Research (2018, background): “leveraged their corporate Twitter access to collect depression-indicative content which others might not be able to recreate [7].”
Leveraging Large Language Models for Mental Health Prediction via Online Text Data (2023, background): “From the computational technology perspective, early research started with basic methods [19, 24, 53].”
Social Networking Sites, Depression, and Anxiety: A Systematic Review (2016, background): “For instance, De Choudhury et al [91] demonstrated that the volume of tweets and the associated replies were reduced in Twitter users with depression compared with those without.”
Recognizing Depression from Twitter Activity (2015, methodology): “have also proposed a method for estimating the depressive tendencies of populations by a similar approach [12].”