M
Mark Dredze
Researcher at Johns Hopkins University
Publications - 290
Citations - 20099
Mark Dredze is an academic researcher from Johns Hopkins University. The author has contributed to research in topics: Social media & Mental health. The author has an hindex of 62, co-authored 276 publications receiving 16812 citations. Previous affiliations of Mark Dredze include Bloomberg L.P. & AmeriCorps VISTA.
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Proceedings Article
Biographies, Bollywood, Boom-boxes and Blenders: Domain Adaptation for Sentiment Classification
TL;DR: This work extends to sentiment classification the recently-proposed structural correspondence learning (SCL) algorithm, reducing the relative error due to adaptation between domains by an average of 30% over the original SCL algorithm and 46% over a supervised baseline.
Proceedings Article
You Are What You Tweet: Analyzing Twitter for Public Health
Michael J. Paul,Mark Dredze +1 more
TL;DR: This work applies the recently introduced Ailment Topic Aspect Model to over one and a half million health related tweets and discovers mentions of over a dozen ailments, including allergies, obesity and insomnia, suggesting that Twitter has broad applicability for public health research.
Journal ArticleDOI
Weaponized Health Communication: Twitter Bots and Russian Trolls Amplify the Vaccine Debate
David A. Broniatowski,Amelia M. Jamison,Si Hua Qi,Lulwah AlKulaib,Tao Chen,Adrian Benton,Sandra Crouse Quinn,Mark Dredze +7 more
TL;DR: Whereas bots that spread malware and unsolicited content disseminated antivaccine messages, Russian trolls promoted discord, showing that directly confronting vaccine skeptics enables bots to legitimize the vaccine debate.
Proceedings ArticleDOI
Quantifying Mental Health Signals in Twitter
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.
Proceedings ArticleDOI
Discovering Shifts to Suicidal Ideation from Mental Health Content in Social Media
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.