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2020
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Journalist, quantify thyself

“We could use natural language processing tools to examine the tone of our coverage over time. We could use maps to gauge how much time we spend covering communities and where our sources are from.”

At any given moment, a digital newsroom knows a lot about its audience: how much time people spend with stories, what they share, what they are saying about the news and much more. Newsrooms use this data to understand readers and develop strategies to grow their business.

But now that we’ve armed ourselves with all this data about our readers, what data do we have about ourselves to inform how our jobs and our coverage should evolve? What would become possible if we turned these tools around to look at ourselves?

We can imagine the effect by looking at a recent example of what happens when people see data about how they spend their time: Spotify Wrapped. One of the most intriguing features of Wrapped is seeing how your tastes and listening habits have evolved over the years, based on the minutes you’ve spent listening to specific artists. Some people who shared their 2019 data on social media were proud of how they spent their time, while others were slightly embarrassed. Regardless, the data gave people new points of reference for their habits, which in turn gave them a sense of how they wanted to move forward. For many, it seemed to deepen their commitment to the product, even if they wanted to change or expand their choices.

This isn’t to say that we should all rush to build a Spotify Wrapped for news. But there’s something we can learn as an industry from examining how people react to seeing data about how they spend their time.

Within many news organizations, we now have the technology to build the types of tools that help us be even more thoughtful about the time we spend covering things, and for whom. We also have the capacity and incentive to use this kind of data about ourselves, with a clear need to better serve audiences. An example of this type of evaluation is WHYY’s recent cultural competency audit, which took a research and data-driven approach to assessing whether the public radio station’s narratives were skewed. The results included a list of ways they could lessen the gap between the perspectives of the newsroom and local communities.

In 2020, we can begin to pair the product development mindset and methods with the skills usually found in data journalism and graphics, to build tools that help us be more self reflective. Like Spotify Wrapped, the data could help us compare our current habits to past ones to help develop future habits.

We could use natural language processing tools to examine the tone of our coverage over time. We could use maps to gauge how much time we spend covering communities and where our sources are from. We could compare broad geographic coverage trends over time through an interface, like Uber Movement. We could even layer public data about social or government activities on top of coverage maps to see patterns. This data would also be additive to existing newsroom knowledge about communities, important topics, key players, and issues that need attention or investigation. In the best cases, this analysis would amplify and underscore that knowledge.

This type of “quantified self” reflection could be a mirror to help us evolve with more data-informed intention and avoid the pitfalls of trendy pivots, which often originate with other players on the stage: startups and platforms. Combined with what we already know about our audiences and businesses, tools that helps us better understand and analyze our time spent could shape the purpose and function of future newsrooms. And provide us with the data to stick with it.

Sarah Schmalbach is product director at the Lenfest Local Lab.

At any given moment, a digital newsroom knows a lot about its audience: how much time people spend with stories, what they share, what they are saying about the news and much more. Newsrooms use this data to understand readers and develop strategies to grow their business.

But now that we’ve armed ourselves with all this data about our readers, what data do we have about ourselves to inform how our jobs and our coverage should evolve? What would become possible if we turned these tools around to look at ourselves?

We can imagine the effect by looking at a recent example of what happens when people see data about how they spend their time: Spotify Wrapped. One of the most intriguing features of Wrapped is seeing how your tastes and listening habits have evolved over the years, based on the minutes you’ve spent listening to specific artists. Some people who shared their 2019 data on social media were proud of how they spent their time, while others were slightly embarrassed. Regardless, the data gave people new points of reference for their habits, which in turn gave them a sense of how they wanted to move forward. For many, it seemed to deepen their commitment to the product, even if they wanted to change or expand their choices.

This isn’t to say that we should all rush to build a Spotify Wrapped for news. But there’s something we can learn as an industry from examining how people react to seeing data about how they spend their time.

Within many news organizations, we now have the technology to build the types of tools that help us be even more thoughtful about the time we spend covering things, and for whom. We also have the capacity and incentive to use this kind of data about ourselves, with a clear need to better serve audiences. An example of this type of evaluation is WHYY’s recent cultural competency audit, which took a research and data-driven approach to assessing whether the public radio station’s narratives were skewed. The results included a list of ways they could lessen the gap between the perspectives of the newsroom and local communities.

In 2020, we can begin to pair the product development mindset and methods with the skills usually found in data journalism and graphics, to build tools that help us be more self reflective. Like Spotify Wrapped, the data could help us compare our current habits to past ones to help develop future habits.

We could use natural language processing tools to examine the tone of our coverage over time. We could use maps to gauge how much time we spend covering communities and where our sources are from. We could compare broad geographic coverage trends over time through an interface, like Uber Movement. We could even layer public data about social or government activities on top of coverage maps to see patterns. This data would also be additive to existing newsroom knowledge about communities, important topics, key players, and issues that need attention or investigation. In the best cases, this analysis would amplify and underscore that knowledge.

This type of “quantified self” reflection could be a mirror to help us evolve with more data-informed intention and avoid the pitfalls of trendy pivots, which often originate with other players on the stage: startups and platforms. Combined with what we already know about our audiences and businesses, tools that helps us better understand and analyze our time spent could shape the purpose and function of future newsrooms. And provide us with the data to stick with it.

Sarah Schmalbach is product director at the Lenfest Local Lab.

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