Introducing The Brown Institute’s Local News Lab

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Photo via Pexels

The Brown Institute for Media Innovation at the Columbia Journalism School is exploring the application of machine learning (ML) and other technologies to support local newsrooms — both advancing their editorial missions as well as creating new business opportunities to engage readers. As is now the hallmark of Brown Institute projects, our approach is collaborative and interdisciplinary, developing responsible technology in the service of journalism’s public mission.

Who? Meet the Local News Lab. We are a new team of engineers, designers, data scientists, and journalists with over two decades combined of experience in media and digital news products. We have varied backgrounds in both legacy and digital-first start up journalism and academic education in engineering and data science.

Why? In the last two decades, journalism, as a profession and as a business, has faced a difficult transition. The subjects of its reporting, the “powerful,” are now as likely to act through the accumulation of data and algorithms as they are through the exercise of traditional political, cultural or social mechanisms. In the same period, journalism’s core means of production and distribution have become digital, with social media and other platforms forcing a reexamination of readers’ relationships with traditional outlets and the ways they can support their businesses.

In short, journalism is becoming an exercise in computation — from the ways we report and tell stories, to how we understand and engage our readers. This shift has placed newsrooms under tremendous economic pressures, and only a handful have the capacity to keep up with the technological changes reshaping the field. It is difficult for all but the largest newsrooms to provide creative direction in the development of substantial new forms of journalism and engagement strategies to support monetizing this content. Small- to medium-sized outlets have very few choices when it comes to this kind of innovation, and often simply do without or are forced to acquire third party solutions at considerable expense and without in-house technical expertise.

Journalism needs broad leadership to envision a new future for the profession, one that mobilizes the power of technology directly in the service of news organizations. Achieving this leadership requires both a commitment to journalism’s public mission, while meeting our new responsibility to sustain technical and creative innovation, placing special emphasis on local outlets.

What? Our primary goal is to support the financial sustainability of local newsrooms. We are creating a culture of interdisciplinary experimentation within journalism, one that produces easily and widely shared strategies for approaching current editorial and business-side challenges. We are exploring traditional paywall tactics along with more innovative and broad ways that an organization can convert casual readers into loyal fans that support its journalism with their dollars and attention.

These strategies will be implemented through open source software and readily deployed systems — “news products.” Our approach to innovation will be collaborative, involving multiple newsrooms and our development team will work with partner news organizations from design through engineering and deployment. Our framework for product development is nimble enough that ideas can be quickly tested and developed to respond to the needs of our changing profession in a timely way.

These efforts are designed to produce a stream of new strategies and accompanying open source tools in an industry that desperately needs to make the most of its contemporary involvement with computation. We believe a university is the natural and ideal point of coordination for these efforts, with its mandate to assess the present and future needs of the industry, and to provide continuing education as the nature of the profession shifts.

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How? As the first of a number of anticipated projects incubated by Brown, we are developing an open source “smart” paywall that deploys ML to go beyond one-size-fits-all approaches to audience engagement. It will help fill a resource gap, in which a handful of large news outlets have this development capacity, but the vast majority of daily newspapers — almost 1,300 — do not. The word “paywall” seems insufficient in this context; while the product will indeed be a gate to the content, the intention is for it to be flexible enough to offer newsrooms the ability to use it in a number of ways that may not have anything to do with money (if they so choose), helping build engagement, better understanding of audiences as well as having the capacity to drive subscriptions, memberships or solicit donations. In this way, the product is the place where business, newsroom and reader needs all meet — it can become a place of creativity and innovation.

What’s next? The work is underway and we are actively looking for organizations to collaborate with. If this sounds like something your newsroom might be interested in, please get in touch with Hannah Wise, News Partnerships Lead about becoming a development partner.

LocalAtBrown

Brown Institute’s Local News Lab

Local News Lab @ Brown Institute

Written by

Media innovation team at the Columbia University Graduate School of Journalism building AI-powered, open-source products to help support and sustain local news.

LocalAtBrown

The Brown Institute’s Local News Lab is a new team of engineers, designers, data scientists, and journalists working to build AI-powered, open-source products to help support local newsrooms and their businesses.

Local News Lab @ Brown Institute

Written by

Media innovation team at the Columbia University Graduate School of Journalism building AI-powered, open-source products to help support and sustain local news.

LocalAtBrown

The Brown Institute’s Local News Lab is a new team of engineers, designers, data scientists, and journalists working to build AI-powered, open-source products to help support local newsrooms and their businesses.

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