Full descriptions of this year’s Magic Grants can be seen below.
Bi-Coastal Magic Grants
Lauren Peace, recent graduate of Columbia Journalism School, Carissa Quiambao recent graduate of Columbia Journalism School and McArdle Hankin, masters candidate in communication at Stanford
Local Live(s) aims to create a dialogue between journalists in local news organizations and the communities they serve. Through live events (held virtually) with journalists telling the stories behind their reporting, they invite their community into “a world that they have only observed from above the fold.” By establishing a rapport between journalists and community members, the team is hoping to help restore trust in local news. Complementary to the live events, the team will produce a handbook for local newsrooms on how to curate, design, and launch live journalism events.
Covid Local News Collaboration
Members from Big Local News and OpenNews
The Covid Local News Collaboration is a partnership between OpenNews and Big Local News. Its aim is to help journalists tell deeper, data-driven stories that assist communities responding to Covid-19. The Big Local News platform, funded as a Magic Grant in 2018-19, has become an important platform for data related to Covid-19, offering information not easily accessible to journalists otherwise. OpenNews has been exploring new kinds of peer mentoring for data journalists and the joint creation of story guides. As a team, these groups will identify Covid-19 stories that are most needed by communities across the country, and help local newsrooms tell them through data and visualizations.
Wolf Pack: How Media Coverage of Criminal Justice Enabled Mass Incarceration
Carroll Bogert, president of The Marshall Project and visiting scholar at Columbia; LynNell Hancock, professor of journalism at Columbia Journalism School and Shanto Iyengar, professor in political science at Stanford
Wolf Pack examines for the first time how media coverage of criminal justice helped turn the United States into the most incarcerated country in the world. In collaboration with data scientists at both Stanford and Columbia, the team will prepare a database of national and local media coverage of criminal justice and, through Natural Language Processing techniques, reveal the framing and narrative structure, and especially the racial and ethnic clues, embedded in this coverage. The lessons from this project will inevitably provide historical framing for contemporary events.
Columbia Magic Grants
Covid-19 FOIA Repository
The Covid-19 FOIA Repository began as a 2019-2020 Magic Grant that shifted its focus to reporting on how local governments were responding to the Covid-19 pandemic. The team will continue its work, issuing targeted Freedom of Information Act requests to build a nationwide repository of Covid-19 related emails between city, county, and state officials. So far, the project has requested records from more than 200 agencies in 44 states, and received 16 substantive responses from 10 states totaling more than 50,000 pages and hundreds of attachments, in data and PDF forms. The Covid-19 FOIA Repository will make the full document sets searchable and available to news organizations, academics and the public.
A Data ‘Concierge Service’ for Climate and Resilience Journalism
Francesco Fiondella, Rémi Cousin, Ashley Curtis, and Weston Anderson (International Research Institute for Climate and Society)
The Data ‘Concierge Service’ for Climate and Resilience Journalism seeks to create a quick-response, concierge-style data service to help journalists access Columbia’s vast climate and environmental data repositories and connect them to its climate scientists. This service addresses two key challenges: first, many journalists aren’t aware of the massive quantities of climate data and analytical tools available to enhance their reporting; and second, it facilitates expert assistance to identify the most relevant and reliable data sets for their stories as well as help downloading the data.
The Covid Financial Crisis
Nick Thieme, Emily Merwin DiRico, Kenneth Foskett, Jennifer Peebles, and John Perry (Atlanta Journal Constitution)
The Covid Financial Crisis project will create an investigative series into the economic impacts of the Covid-19 crisis on Georgia’s citizens, companies, and municipal governments. Focusing on personal and corporate bankruptcies and municipal defaults, the project will measure the financial damage to the people of Georgia, allowing a comparison between this crisis and previous ones. Are black Americans more likely to file for bankruptcy because of Covid-19 than white Americans? Are rural municipalities more likely to default on bond obligations than urban municipalities? What industry factors are most associated with bankruptcy in the Covid-19 era?
Seed Grant — Project Immerse
Lance Douglas Weiler (Digital Storytelling Lab) and Nicholas Fortugno (Playmatics)
In a time of deep fakes, conspiracy theories, and AI-driven writing and social networking scams, we are surrounded by manipulative and deceptive technologies that, in the wrong hands, could constitute an existential threat to our society. Project Immerse will apply a learning methodology the team has called “attract and educate” to create an immersive educational experience using existing technologies, specifically the Miro collaborative platform and Zoom, to tell stories featuring these malicious techniques. Building an anthology series akin to Black Mirror, the project will create “episodes” that deal with the ways misinformation manifests on the internet, drawn out to their potential dramatic and powerful conclusions. Each episode will be a standalone piece of entertainment built in web native technologies, but at the end of each episode Project Immerse takes users behind the scenes and gives them access to the actual tools to play with on their own.
Seed Grant — The Right to be Forgotten in U.S. Newsrooms
Sarah Collins (Columbia Journalism School)
The Right to be Forgotten in U.S. Newsrooms will research and create guidelines for newsrooms to “unpublish” obsolete content — such as stories about crimes that are now sealed by courts, reports about arrests that never led to charges, and factually inaccurate information. Through consultation with experts in philosophy, ethics, journalism, civic nonprofits, international relations, law, government, internet technology, and big data, the project will consider not just situations when content should be unpublished, but also examine concrete technical steps newsrooms can follow to securely and correctly remove the articles in question.
Seed Grant — Community Networking for Community Storytelling in Appalachia
Houman Saberi, Greta Byrum, Raul Enriquez and April Jarocki
The Community Networking for Community Storytelling in Appalachia will build on their existing efforts to develop community owned communications infrastructure and use it as a platform for storytelling. Through this, the project will highlight the ongoing efforts of the residents of the Clearfork Valley in rural Eastern Tennessee to build transformational resilience in the face of the impacts of the coal industry and a changing climate.
Seed Grant — Automatic Identification of Online Harassment of Women Journalists
Julia Hirschberg and Sarah Iva Levitan (Computer Science at Columbia University)
The Automatic Identification of Online Harassment of Women Journalists will develop methods to identify abusive and hateful speech targeting female journalists on social media. The project will work with journalists to collect a large-scale corpus of private harassment messages received by journalists on Twitter, and to develop an easily-employed annotation method for labeling messages by degree of observed harassment, collecting self-labeled data from journalists. They will use automated Natural Language Processing methods to extract features from these messages and to build Machine Learning classifiers to distinguish between harassing or abusive and neutral messages. These classifiers will be integrated into a tool for journalists to use to manage their Twitter feeds to identify and segregate these messages.
Stanford Magic Grants
Self-Moderating Online Focus Groups and Deliberation
Lodewijk Gelauff and Sukolsak Sakshuwong, both doctoral candidates in computer science at Stanford
Due to the precautions we are taking around COVID-19, our society has rapidly shifted from traditional in-person meetings to online meetings. Building a tool for better online deliberation through an automated moderator that provides equitable, respectful, and constructive conversation is the focus of Self-Moderating Online Focus Groups and Deliberation. To support deliberative democracy, the team will develop a scalable online platform that addresses current challenges such a small group that dominates the conversation, a single topic that absorbs too much time, and a biased moderator.
Improving Remote Learning via Hierarchical Decomposition of Instructional Videos
Anh Truong and Chien-Yi Chang, both doctoral candidate in computer science at Stanford
Increasingly, we turn to instructional videos for accomplishing everyday tasks and learning new skills such as sewing a face mask, cooking, and household repairs. Improving Remote Learning via Hierarchical Decomposition of Instructional Videos will facilitate the creation of instructional video to allow better navigation by hierarchically segmenting tasks into steps and providing voice-based navigation commands for accessing the steps; accomplishing this with algorithms that can automatically learn shared action steps from videos across different tasks by explicitly leveraging the conjugate constraints between actions and states.
Sports Illustrated: Enabling Machines to Understand and Describe Tennis Matches
Sumith Kulal and Haotian Zhang, both doctoral candidate in computer science at Stanford
Major sports events like the World Cup and Wimbledon attract millions of viewers. Sports Illustrated: Enabling Machines to Understand and Describe Tennis Matches aims to build a higher-level abstraction for understanding and describing sports videos (e.g. soccer, tennis) by enabling three types of applications (1) Retrieval: retrieve similar action from a video database (2) Text: synthesize text/speech to describe details of the action (3) Edit: edit particular micro-movement and re-render the video. This team hopes to provide a better viewing experience for audiences and powerful game analysis for athletes.
Leitmotif: Location-Driven Audio Storytelling
Jacob Ritchie, doctoral candidate in computer science at Stanford and Jean Costa, post-doctoral researcher in computer science at Stanford
Delivering dynamic audio storytelling, using geolocation, to connect the user to stories of people, places, and things that they walk by, is the Leitmotif: Location-Driven Audio Storytelling system this team is undertaking. The team will create software to enable the generation of location-specific audio stories, and a paired smartphone application to allow users to consume audio content. Users will be able to preview and select audio stories of interest to listen to as they move through their physical world.