Algorithmic news curation aggregators (e.g. Google News) are sometimes known to personalize the selection of stories shown to individuals. But far less is known about the potential for article-level personalization in which an article is automatically re-written to appeal to different types of users, perhaps even adapted to each individual. Could this be used to manipulate, persuade, inform, or engage people more effectively? The goal of this project is to prototype one or more templates for automated news articles that adapt to different types of people or individuals based on a given user model based on the types of information a news site might know (e.g. gender, age, race, location, interest-level, etc.). These templates will be used to produce personalizable news articles that are published to the web.
Results from the project
Faculty and Staff Leads
Assistant Professor, Director of the Computational Journalism Lab
Northwestern University Asst Professor of Communication & Tow Center fellow. Computational journalism, algorithmic accountability, social computing.