World's Best Scientists 2026 revealed!

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Computer Science

D-Index
71
Citations
24469
World Ranking
1755
National Ranking
892

Overview

Mark Dredze is affiliated with Johns Hopkins University in the United States. Their research spans multiple disciplines including Computer Science, Social Sciences, and Medicine, with a substantial focus on intersecting subfields such as Artificial Intelligence, Sociology and Political Science, Epidemiology, Health, and General Health Professions.

The scientist's work addresses a range of topics, particularly emphasizing misinformation and its impacts, data-driven disease surveillance, sentiment analysis and opinion mining, topic modeling, natural language processing techniques, vaccine coverage and hesitancy, and hate speech and cyberbullying detection.

Notable recent papers by Mark Dredze include the following:

  • "Comparing Physician and Artificial Intelligence Chatbot Responses to Patient Questions Posted to a Public Social Media Forum," 2023, JAMA Internal Medicine
  • "You Are What You Tweet: Analyzing Twitter for Public Health," 2021, Proceedings of the International AAAI Conference on Web and Social Media
  • "BloombergGPT: A Large Language Model for Finance," 2023, arXiv (Cornell University)
  • "Not just conspiracy theories: Vaccine opponents and proponents add to the COVID-19 'infodemic' on Twitter," 2020, Harvard Kennedy School Misinformation Review
  • "Internet Searches for Unproven COVID-19 Therapies in the United States," 2020, JAMA Internal Medicine

Frequent collaborators in their research include:

  • David Broniatowski
  • Amelia Jamison
  • Sandra Crouse Quinn
  • Xiaolei Huang
  • John W. Ayers

Mark Dredze's publications appear predominantly in venues such as:

  • Zenodo (CERN European Organization for Nuclear Research)
  • arXiv (Cornell University)
  • JAMA Internal Medicine
  • UNC Libraries
  • American Journal of Public Health

Best Publications

  • Biographies, Bollywood, Boom-boxes and Blenders: Domain Adaptation for Sentiment Classification

    John Blitzer;Mark Dredze;Fernando Pereira

  • You Are What You Tweet: Analyzing Twitter for Public Health

    Michael J. Paul;Mark Dredze

  • Weaponized Health Communication: Twitter Bots and Russian Trolls Amplify the Vaccine Debate

    David A. Broniatowski;Amelia M. Jamison;Si Hua Qi;Lulwah AlKulaib

  • Discovering Shifts to Suicidal Ideation from Mental Health Content in Social Media

    Munmun De Choudhury;Emre Kiciman;Mark Dredze;Glen Coppersmith

  • Quantifying Mental Health Signals in Twitter

    Glen Coppersmith;Mark Dredze;Craig Harman

  • Beto, Bentz, Becas: The Surprising Cross-Lingual Effectiveness of BERT

    Shijie Wu;Mark Dredze

  • Confidence-weighted linear classification

    Mark Dredze;Koby Crammer;Fernando Pereira

  • National and local influenza surveillance through Twitter: an analysis of the 2012-2013 influenza epidemic

    David A. Broniatowski;Michael J. Paul;Mark Dredze

  • BloombergGPT: A Large Language Model for Finance

    Unknown

  • Combining Search, Social Media, and Traditional Data Sources to Improve Influenza Surveillance.

    Mauricio Santillana;André T. Nguyen;Mark Dredze;Michael J. Paul

  • Annotating Named Entities in Twitter Data with Crowdsourcing

    Tim Finin;William Murnane;Anand Karandikar;Nicholas Keller

  • Creating Speech and Language Data With Amazon's Mechanical Turk

    Chris Callison-Burch;Mark Dredze

  • Named Entity Recognition for Chinese Social Media with Jointly Trained Embeddings

    Nanyun Peng;Mark Dredze

  • Entity Disambiguation for Knowledge Base Population

    Mark Dredze;Paul McNamee;Delip Rao;Adam Gerber

  • From ADHD to SAD: Analyzing the Language of Mental Health on Twitter through Self-Reported Diagnoses

    Glen Coppersmith;Mark Dredze;Craig Harman;Kristy Hollingshead

  • Adaptive Regularization of Weight Vectors

    Koby Crammer;Alex Kulesza;Mark Dredze

  • Improving Lexical Embeddings with Semantic Knowledge

    Mo Yu;Mark Dredze

  • Measuring Post Traumatic Stress Disorder in Twitter

    Glen Coppersmith;Craig Harman;Mark Dredze

  • Separating Fact from Fear: Tracking Flu Infections on Twitter

    Alex Lamb;Michael J. Paul;Mark Dredze

  • CLPsych 2015 Shared Task: Depression and PTSD on Twitter

    Glen Coppersmith;Mark Dredze;Craig Harman;Kristy Hollingshead

  • Discovering health topics in social media using topic models.

    Michael J. Paul;Mark Dredze

Frequent Co-Authors

Michael J. Paul
Michael J. Paul University of Colorado Boulder
Sandra Crouse Quinn
Sandra Crouse Quinn University of Maryland, College Park
Benjamin Van Durme
Benjamin Van Durme Johns Hopkins University
Fernando Pereira
Fernando Pereira Google (United States)
Koby Crammer
Koby Crammer Technion – Israel Institute of Technology
Sanjeev Khudanpur
Sanjeev Khudanpur Johns Hopkins University
Nanyun Peng
Nanyun Peng University of California, Los Angeles
Mo Yu
Mo Yu IBM (United States)
Jason Eisner
Jason Eisner Johns Hopkins University
Chris Callison-Burch
Chris Callison-Burch University of Pennsylvania

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