Big Local News
A Stanford Journalism and Democracy Initiative (JDI) team of faculty, students and researchers led by computational journalist Cheryl Phillips will collect, process and share governmental data that are hard to obtain and difficult to analyze; partner with local and national newsrooms on investigative projects across a range of topics; and make it easy to teach best practices for finding stories within the data.
Big Local News builds upon the Stanford Open Policing Project, which obtained records of more than 130 million police traffic stops through public records requests to state agencies in every state. The information collected from 31 states resulted in local accountability stories in Denver, Boston, Washington state, Colorado, Ohio, and other cities and states. Now, Big Local News has hired three data journalists who are working with Stanford students, newsrooms and other universities to collect and analyze local data that lends itself to accountability journalism on a variety of topics, including criminal justice, housing, health and education.
JSK Impact Partnerships
The JSK Impact Partnerships, an initiative of the John S. Knight Journalism Fellowships at Stanford, leverages JSK’s connections, vast alumni network, and access to one of the world’s premier universities to accelerate progress in the journalism industry and improve the quality of news and information reaching the public. The work of the JSK Impact Partnerships is based on JSK’s four call-to-action themes: challenging misinformation and disinformation; holding the powerful accountable; fighting bias, intolerance and injustice; and strengthening local news.
Through its Impact Partnerships, JSK offers a range of support to efforts that advance the themes: financial assistance, leadership coaching, project advising and relationship building. The partnership operates in parallel with the JSK Journalism Fellowships, which each year brings up to 20 diverse leaders to Stanford to work on the most urgent problems in journalism.
Analyzing Cable TV News
Cable TV news networks make editorial choices that influence how information is provided to millions of Americans. Stanford Computer Science Professor Maneesh Agrawala and a team of researchers are using artificial intelligence-based image, audio and transcript processing techniques to analyze data from nearly a decade of 24/7 broadcasts by CNN, MSNBC and Fox News. Their mission is to help explain why some stories get told more than others, who tells these stories, and what perspectives are included or left out.
The team has annotated roughly 200,000 hours of cable TV news video spanning 2010 to 2018. With information about gender, appearance attributes such as clothing type and hair color, and audio attributes like a speaker’s volume, these annotations answer questions about visual representations in the news. How often are men onscreen vs. women? (Answer: 2.2-to-1 ratio.) Do female-hosted programs give significantly more screen time to women than male-hosted shows? (Answer: They do not.) Interactive, web-based tools will be created to enable journalists and data scientists to explore the data with ease.
Computational expertise from across Stanford can participate in a JDI Bridges program to work on journalism challenges involving reporting, storytelling, and distribution and consumption of news. The program will develop related courses spanning the university’s academic departments and organize workshops, meetings, and international conferences. It builds on the Computation + Journalism Symposium that Professor Jay Hamilton, Director of the Stanford Journalism Program, co-founded with Maneesh Agrawala in 2016.