Nicholas Diakopoulos

Assistant Professor, Director of the Computational Journalism Lab

Northwestern University Asst Professor of Communication & Tow Center fellow. Computational journalism, algorithmic accountability, social computing.


Projects Nicholas Diakopoulos has worked on.

Auditing the NewsEvaluating News Quality on Smart Speakers

Alexa, Siri, Google Home, Cortana—smart speakers and agents are now used by about 20% of US homes. People use them to ask about weather, set timers, play games, get information, or listen to the news. But are these devices delivering high-quality news and information or could they be misinforming and sharing “junk” news? This project aims to find out. By developing an audit method that defines what queries to audit and systematically collects data on the results over time for those queries from several different smart speakers, the project will allow for an assessment and comparison of news quality from these different devices.

Claim SpottingAutomation and Crowdsourcing to Support Factchecking

News sites like Politifact and have defined a whole genre of journalism that pursues fact checking as a public activity, its own form of coverage and content. There is a large audience demand for factchecking. For instance, NPR’s live factcheck of the first 2016 presidential debates led to record site traffic. But journalists’ attention is limited, and given the endless sea of things people say, how should journalists go about identifying and ranking the most newsworthy and important things to fact check?

Personalize My StoryAutomatically Adapting News Article Text for Individual Users

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.

The News Localizing EngineAugmenting Local Angles with Data

As local papers are forced to cut back on coverage and struggle to survive, the future of local news is uncertain. Some communities are even considered “news deserts” with little to no local information. What can be done to increase the volume of local news? The goal of this project is to address this issue by considering how national reporting can be augmented with a local angle based on data.