Predicting postpartum changes in emotion and behavior via social media

By M. de Choudhury, Scott Counts, Eric Horvitz. International Conference on Human Factors in Computing Systems, 2013

457 citations recorded by the source corpus.

Subfields: Clinical Psychology, Public Health, Environmental and Occupational Health, Social Psychology

View Paper: https://www.semanticscholar.org/paper/fb71d0f98fb8b9ed5b323a51540223a957f2abb8

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In Clinical Psychology

In Public Health, Environmental and Occupational Health

In Social Psychology

Citations in the corpus

Recognizing Depression from Twitter Activity (2015, background): “Such approaches are applied to prediction of postpartum depression from Facebook and Twitter data [11, 13].”
Developing Age and Gender Predictive Lexica over Social Media (2014, background): “Social media may track disease rates (Paul and Dredze, 2011; Google, 2014), psychological well-being (Dodds et al., 2011; De Choudhury et al., 2013; Schwartz et al., 2013a), and a host of other behavioral, psychological and medical phenomena (Kosinski et al., 2013).”
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…”
Discussion Graphs: Putting Social Media Analysis in Context (2014, background): “Many studies perform a validation by comparison with ground-truth data based on expensive conventional surveys and study techniques (Schwartz et al. 2013; De Choudhury, Counts, and Horvitz 2013b; Cranshaw et al. 2012).”
Discovering Shifts to Suicidal Ideation from Mental Health Content in Social Media (2016, unknown): “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].”
Forecasting the onset and course of mental illness with Twitter data (2016, methodology): “While earlier research identified depression predictors in Twitter data for Major Depressive Disorder and postpartum depression (7,8), we used a more general category in”
Using matched samples to estimate the effects of exercise on mental health from twitter (2015, background): “…Jamison-Powell et al. 2012; Sadilek, Kautz, and Silenzio 2012; Stewart and Diaz 2012; Hanson et al. 2013; Culotta 2013; De Choudhury et al. 2013; De Choudhury, Counts, and Horvitz 2013), perhaps the most aligned with our vision is Sadilek and Kautz (2013), who infer adverse health symptoms…”
Detection of Depression-Related Posts in Reddit Social Media Forum (2019, unknown): “[29] used linguistic features to train a classifier to examine Twitter posts that indicated depression.”
Understanding Social Media Disclosures of Sexual Abuse Through the Lenses of Support Seeking and Anonymity (2016, methodology): “media data [18,19], such as using classifiers to detect posts discussing domestic abuse on reddit [54].”
Sensitive Self-disclosures, Responses, and Social Support on Instagram: The Case of #Depression (2017, methodology): “[18,20] used Twitter and Facebook data to detect and predict postpartum depression, using online behavioral signals (e.”
Towards Decision Support and Goal Achievement: Identifying Action-Outcome Relationships From Social Media (2015, unknown): “Similar studies have been conducted in urban informatics [8], mental health [9, 16], natural disaster monitoring [11, 41], and other domains.”
COVID-19 Public Sentiment Insights and MachineLearning for Tweets Classification (2020, background): “(2013) [10] investigated behavioral changes and moods of new mothers in the postnatal situation.”
Exploring Time-Dependent Concerns about Pregnancy and Childbirth from Search Logs (2015, background): “characterized postpartum behavioral changes apparent in Twitter [9] and Facebook”
Depression detection from social network data using machine learning techniques (2018, background): “[14] considered online networking as a promising instrument for public health, concentrating on the utilization of Twitter presents on fabricating predictive models about the forthcoming impact of childbirth on the conduct and disposition of new mothers.”