Mark Dredze, associate professor in the Department of Computer Science, has been appointed as a John C. Malone Associate Professor. The John C. Malone Associate Professorship was endowed through the generosity of John C. Malone ’64, ’69 to support outstanding Whiting School faculty members within the Malone Center for Engineering in Healthcare. Mark develops new methods […]
Mark Dredze, associate professor of computer science, is profiled by Baltimore Style as one of three Baltimore influencers targeting unique ways to stop the nationwide crisis of gun violence.
Researchers study Google Trends data for period after controversial Netflix show’s March debut.
After actor Charlie Sheen disclosed his HIV-positive status on NBC’s Today show last November, millions took to the Internet to find out more about HIV, according to a new study led by computer scientist Mark Dredze and two Whiting School alumni.
Social media users who share conspiracy theories and pseudoscientific claims about the Zika virus may undermine upcoming efforts to keep the disease from spreading.
Recent research published in Vaccine revealed a key factor in making articles about vaccines go viral, ScienceDaily reports. Mark Dredze served as a technical lead for the study.
Health awareness days are ubiquitous. But does dedicating a day to a serious disease or to healthy living habits actually make a difference?
Baltimore Business Journal reports that Sickweather, a startup that provides sickness forecasting and mapping by scanning social networks, is one of ten startups selected as finalists for Light City Baltimore’s pitch competition.
The day of Sheen’s disclosure coincided with a 265 percent increase in news reports mentioning HIV (97 percent of which also mentioned Sheen) archived on the Bloomberg Terminal, even though HIV-related news reports have been in historic decline. An additional 6,500 stories were reported on Google News alone. This placed Sheen’s disclosure among the top 1 percent of historic HIV-related media events.
In 2009, two computer scientists at the Center for Language and Speech Processing at Johns Hopkins University got access to a trove of Tweets — some 2 billion posted between May 2009 and October 2010 — and decided to find a way to analyze them.
