Culotta runs the Text Analysis in the Public Interest Lab, where his team develops algorithms that analyze online social networks to enable socially beneficial technologies. Applications include tracking the spread of influenza, monitoring unfolding natural disasters, assessing the effectiveness of smoking cessation campaigns, and identifying misleading marketing statements. To support these applications, TAPI research has resulted in several advances in machine learning and text mining algorithms, including distant supervision, domain adaptation, and robust classification.
Links to two ongoing NSF-funded projects:
Two recent papers:
Additional Publications »