Magic Grants

Earnings Inspector

In direct response to criticisms of the rigor of business journalism, Earnings Inspector will provide business journalists a new tool to make the methods of forensic accounting more accessible. By sifting through a database of accounts of all public U.S. companies, Earnings Inspector will use fraud detection algorithms to report the likelihood of manipulated earnings.

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Reframe Iran

Journalists can glean remarkable insights into the social and cultural tensions of a region by studying the lives and experiences of its artists. These insights are particularly important in countries whose cultures have been misconstrued by traditional reporting in mainstream media. Built on this notion, Reframe Iran will present 40 profiles of Iranian artists living

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Visual Genome

Visual Genome seeks to enable journalists to effectively gather crowd-sourced breaking news images and videos in near real time, extract meta-data and relationships from these images, and utilize them to enhance the quality of their articles and reports. The Visual Genome team consists of Ranjay Krishna, a graduate student in computer science at Stanford Engineering,

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Web Transparency

By revealing the mechanisms behind previously opaque advertising schemes at an arbitrarily detailed level, the Web Transparency project unveils today’s commercial and political tactics that are used to funnel consumers and citizens’ attention. The Web Transparency team consists of Charles Berret, a PhD candidate in communications at Columbia University, Cecilia Reyes, an undergraduate in computer science at

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Bushwig

A Columbia School of Journalism documentary film student and a PhD candidate in the Rutgers University School of Communication and Information will tell the story of a drag renaissance taking place in Bushwick, Brooklyn, that is enlisting and extending social media platforms for the “identity curation” that happens in the drag community. The Team

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CityBeat

CityBeat is multi-platform application for newsrooms and journalists that sources, monitors and analyzes hyper-local information from multiple social media platforms such as Instagram, Twitter and Foursquare, in real time. We use public, geo-tagged, real time data shared via social media services, in order to trace a city’s happenings and dynamics. During the first year of

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The Declassification Engine

A partnership between faculty and students in the Departments of History, Statistics and Computer Science at Columbia University, this project will probe the limits of official secrecy by applying natural language processing software to archives of declassified documents to examine whether it is possible to predict the contents of redacted text, attribute authorship to anonymous documents and model the geographic and temporal patterns

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Ensemble

Ensemble is a Web platform created by Joy Kim and Justin Cheng that provides structure to collaborative storytelling. In Ensemble, one person is assigned the responsibility of managing creative direction, and can then enlist a crowd of friends or strangers to perform various tasks – such as contributing narrative direction or developing a character’s back

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Gistraker

A collaboration by Richard Socher and Rebecca Weiss, Gistraker is a Web application that analyzes the sentiment of language used in news media. Users will be able to create filters and explore visual summaries of how different media outlets cover specific actors or issues of interest, which could reveal instances of media bias.   The

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NewsHub

A team of graduate students and recent graduates of the Columbia School of Journalism and the School of Engineering and Applied Science will create a system for tracking censorship in authoritarian regimes post-publication (i.e. when a story is revised or deleted after publication).  The team will create real-time assessments and monthly reports of journalistic improprieties around the globe. The Team

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Widescope and Synapp

Widescope and Synapp, a 2013-14 Magic Grant recipient, will receive renewed funding to scale up the current systems to achieve widespread usage and impact by partnering with governments, schools, and media organizations. Additionally, the team will further develop and implement algorithms and mechanisms for more effective aggregation and collaboration, all in an effort to posit

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Personalized TV News

In this project, we seek to develop and demonstrate a platform for personalized television news to replace the traditional one-broadcast-fits-all model. We forecast that next-generation video news consumption will be more personalized, device agnostic, and pooled from many different information sources. The technology for our project represents a major step in this direction, providing each

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