Announcing funding for three AI & Local News teams | NYC Media Lab

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We’re excited to announce funding to three teams — Overtone, Information Tracer and Localizer — to continue their work at the intersection of artificial intelligence and local news.

The projects offer innovative solutions in areas including: contextualizing and quantifying information spread; using news automation techniques, such as Natural Language Generation, to parse data into local stories; and scoring and sorting online content by quality.

Artificial intelligence and automation are some of the technologies shaping the evolution of the digital media landscape. These three projects exemplify how technologists can play a crucial role in sustaining local news and meeting the information needs of communities across the country. 

The three teams participated in the AI & Local News Challenge, organized by NYC Media Lab. As part of the program they met weekly with the cohort, heard from guest speakers (including AI journalism startups and local news experts) and connected with industry mentors to further develop their offerings.


Team information:


Team members: Philip Allin, Marley Bacelar, Christopher Brennan, Natalia Gutierrez, Reagan Nunnally

Overtone has built a Natural Language Processing algorithm that finds and sorts online content by its intrinsic qualities, rather than clicks or shares. Their AI assesses texts for journalistic signals that demonstrate human effort. Their data suggests that readers understand quality, and subscribe to it at higher rates. 

Their tech helps publishers discover, productize and monetize their pieces, delivering insight instantaneously. Overtone wants to be available to all communities, and this spring has finished a proof of concept for its Spanish model. This model will help highlight all the great journalism out there that doesn’t reach the audience it should.

A five person team from diverse backgrounds, we have all seen the need for new solutions to sort through articles online. Christopher and Natalia come from journalism, with experience in reporting as well as audience engagement. Philip and Reagan have spent time both at startups and marquee brands, where they have seen the limitations in the current approach as well as the promise of new innovation. Marley is an engineer with a strong background in machine learning. Through decades of experience they have seen the issues that plague the media ecosystem and have created a primary datapoint, rather than another derivative metric, that can help build sustainable news businesses.


Information Tracer

Team members: Zhouhan Chen, Joshua Tucker

Information Tracer is a real-time, cross-platform system to detect information operations such as disinformation or bot campaigns. We provide an infrastructure to enable users to choose metrics, set thresholds, and monitor potentially manipulated news content. 

Currently, our system takes a URL, hashtag or keyword as an input, then collects posts that mention the input from five social media platforms—Twitter, Facebook, YouTube, Reddit, and Gab. We provide both an interactive web interface and API endpoints to cater different use cases. We will be adding new platforms such as Telegram and Whatsapp in the future.

Zhouhan Chen is a recent PhD graduate from NYU Center for Data Science. His main research projects are Information Tracer -- a cross-platform system to detect information operations, and Malware Discoverer -- a proactive system to discover malicious URL redirection campaigns. He is currently a startup founder and independent consultant.

Zhouhan previously interned at security teams at Twitter, Google and Amazon Web Services, building real-world systems to detect fake accounts, malware and suspicious domains. He is broadly interested in how to use data science and machine learning to make the Internet safer, more connected and less polarized.

Joshua A. Tucker is Professor of Politics, affiliated Professor of Russian and Slavic Studies, and affiliated Professor of Data Science at New York University. He is the Director of NYU’s Jordan Center for Advanced Study of Russia, a Co-Director of NYU’s Center for Social Media and Politics, and a co-author/editor of the award-winning politics and policy blog The Monkey Cage at The Washington Post. He serves on the advisory board of the American National Election Study, the Comparative Study of Electoral Systems, and numerous academic journals, and was the co-founder and co-editor of the Journal of Experimental Political Science. His original research was on mass political behavior in post-communist countries, including voting and elections, partisanship, public opinion formation, and protest participation.



Team members: Jessica Davis, Steve Dorsey, Mike Stucka, Eric Ulken

At Gannett, news automation is the fuel that helps achieve greater relevance by delivering hyperlocal information at scale to drive digital subscription growth. The Localizer project uses news automation techniques, such as Natural Language Generation, to parse data into local stories – either to the state-level, or even to county- and city levels of specificity. This frees reporters to create deeply reported journalism that can’t be done by a machine. We seek to operationalize this work and build out a robust content calendar to create one local story in multiple formats (articles, newsletters, graphics, audio scripts) every weekday for each of our 230+ local newsrooms. For this challenge, we sought to add a real estate trend story.

The News Automation Team has been at the forefront of pilot concepts through our Localizer Project, producing hyperlocal articles for COVID-19 case and vaccination trackers, hurricane coverage, a U.S. Census data release and real estate trends in the past year. The team is a mix of experienced journalists and product partners with backgrounds including data reporting, software development, product management, audience development and content strategy. The current cross-divisional effort is comprised of and draws on experience from previous automation efforts at GateHouse Media and Gannett prior to the companies’ merger.

AI & Local News initiative. The AI & Local News initiative is funded by Knight Foundation and consists of projects from Associated Press, Brown Institute’s Local News Lab, NYC Media Lab and Partnership on AI.

NYC Media Lab. NYC Media Lab, housed at New York University’s Tandon School of Engineering, supports innovation in media and technology through collaborations between industry and academia. Applications for the next cohort will open in fall 2023.