Major life changes and behavioral markers in social media: case of childbirth

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

123 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Social Psychology, Sociology and Political Science

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Citations in the corpus

Sentiment Analysis in MOOC Discussion Forums: What does it tell us? (2014, methodology): “Sentiment analysis has been used as an invaluable tool for identification of markers of affective responses to crisis [10], as well as depression [9], anxiety, and other psychological disorders [8] from social media sites.”
Characterizing and predicting postpartum depression from shared facebook data (2014, background): “postings of new mothers to detect [4] and to predict [5]”
Deep Learning for Depression Detection of Twitter Users (2018, methodology): “For example, the use of ﬁrst-person pronouns (Lehrman et al., 2012) compared to the lesser use of second and third person pronouns (De Choudhury, 2013) are being used to detect users susceptible to dis-89 tress and depression.”
Predicting postpartum changes in emotion and behavior via social media (2013, methodology): “postpartum for C1, but negative affect (NA) increases [6,8].”
Distilling the Outcomes of Personal Experiences: A Propensity-scored Analysis of Social Media (2017, background): “Twitter is also frequently used by users to report on their personal daily happenings [1, 25, 53, 71], which, along with their timestamps, may provide insights into temporal relations among personal events.”
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).”
Quantifying Mental Health Signals in Twitter (2014, methodology): “We manually examined and annotated only half the diagnosis statements for depression – indicating there are likely 800-900 depression users available via these automatic methods from our collection, compared to the 117 obtained via the methods of De Choudhury et al. (2013d). Additionally, we emphasi”