Diversifying news distribution with meaningful collaborations
Lessons from identifying opportunity outside of the industry
Journalism has historically made its news more adaptable in order to reach new distribution and business opportunities. Richard Gingras, now VP of News at Google, said at ISOJ in 2012: “shifts in distribution shape the business model.” Well… the next strategies for distribution have arrived and we have work to do.
The news story was the industry’s first powerful example of how opportunity arises when things are valued independently. As it was unbundled from the newspaper and found its independence as a webpage, it enabled us to distribute and monetize in new ways. As technology continues to change shape, journalism’s success will continue to depend on how well its content can further unbundle and adapt.
Smart cities, smart homes, smart cars, smartphones... over time, these devices and their algorithms have nestled themselves comfortably between producers and consumers. The news story is now widely distributed to interfaces out of the publisher’s direct control where the success of its readership has become more dependent on its filterable data than its marketing promotions. Technology will continue to maintain this publisher-reader gap while aiming to deliver content more effectively to the reader by prioritizing convenience —increasingly outside of traditional interfaces.
Journalism must learn how news can adapt to these new technologies, services, and consumer expectations in order to reach audiences through more effective channels. By limiting news to traditional approaches, the industry risks falling behind again.
Over the past year, I’ve researched and launched programs at Bloom with a goal to identify opportunities for local news in non-traditional distribution channels and interfaces. Our work with NYC real estate firm, Roomeze, has created a niche distribution channel that taps into a growing group of 1,600 new residents to more conveniently deliver news happening near their home. I’ve also initiated conversations with Intersection, a smart city firm in NYC, to start discussing how interfaces planted on sidewalks and bus stations could one day make people aware of local news happening nearby.
In this article, I’ll explain the challenges I encountered while working in this new phase of adaptation and the model found to be helpful in identifying new distribution opportunities for journalism. If you’re interested in digging more into our projects, take a look at our case studies in real estate and smart cities.
Semantic journalism: The strategy almost no one is talking about
Creating meaningful (semantic) relationships between data and services is the cornerstone of what Internet founder Tim Berners-Lee et al. advocates as the future of the web, Web 3.0 or the Semantic Web. It’s an extension of long-known semantic networks, introduced in the late 50s, that helps transform information into meaningful networks of data. With data more accessible, it acquires the flexibility to be repurposed on services inside and outside of its traditional information outlets.
Adrian Holovaty, the founder of EveryBlock, was one of the first to thoroughly advocate that semantics is the “fundamental change” needed in journalism in his 2006 essay:
“So much of what local journalists collect day-to-day is structured information: the type of information that can be sliced-and-diced, in an automated fashion, by computers. Yet the information gets distilled into a big blob of text — a newspaper story — that has no chance of being repurposed.”
I’m happy to know journalism has improved since Adrian wrote that. News publishers have wider appreciation and adoption of metadata on their websites today. This is primarily in thanks to education around how metadata improves SEO by enabling faceted search. But there’s more to semantics than just defining data for search engines.
I believe the ability to find ways to repurpose data and, therefore, to repurpose its content is vastly overlooked but will be a key factor for journalism’s success in the 21st century. Repurposing news cannot only help diversify readership but could also lead to new business collaborations and revenue streams as well.
But there’s a problem. Barely anybody in journalism is talking about this.
For good measure, a Google search brings up just 63 results! Most of which talk about semantics in social networks, little about semantic journalism itself.
I’ve found only a few articles published in the past 10 years that correlate journalism with semantics. RJI fellow David Caswell’s take with structured journalism has received a bit more attention and experimentation over the years, which I’ve greatly admired. But from recent industry conferences that I’ve attended and discussions I’ve had with publishers, attention toward semantics, structured data, and repurposing news are rare or brief.
Today, it seems that not many publishers are truly focused on how they will operate in this new digital paradigm and even fewer have begun experimenting.
A brief model for semantic journalism
As with any new technology or strategy, education must be thoroughly and patiently accounted for. It seems we haven’t yet developed sufficient resources to do so at scale in journalism for semantics.
I’d like to explain a few lessons learned that I’ve kept close and a strategy that helped me uncover opportunities to experiment with.
First, the process. The model I see for semantic journalism has 3 main steps:
- Independence: Picking out data points from a story.
- Parameterization: Organizing this data into meaningful parameters that help define what the data represents. “17 degrees” is more than just temperature, it also represents a place and a time. At Bloom, we primarily organize stories with the location parameter — but the topic, facts, people, and time could all be utilized here as well.
- Relationships: Identifying valuable ways these data points relate to and complement data from other services outside of your organization — furthermore, outside of your industry. It’s like the business version of Scrabble. This step is critical but vastly overlooked.
To put this process to the test, I started by breaking out many local news stories by their location (1). I defined the location for each one by street level using geotagging, which was super easy with Bloom (2) and then brainstormed to learn the value this data created that could relate to other services (3).
As Bunk recommends from The Wire, “soft eyes” helps: “If you got soft eyes, you can see the whole thing. If you got hard eyes, you’re staring at the same tree, missing the forest.”
So, with soft eyes, I was able to identify two main unique values that I believe geotagged news helps to achieve: summarizing trends in neighborhoods and street blocks by aggregating topics from many local stories, and personalizing by specific location which stimulates local experiences.
With these, I began to think of other services outside of journalism that see these values useful. A few came to mind —local community forums or reports, smart city kiosks, real estate services, car dashboards, digital bus shelters. Each of these services is adopting technology today that aims to give its consumers a better local experience but isn’t doing so with the help of journalism... yet?
My hypothesis is that if journalism adapts in a way to integrate into these services to transfer its value and help the service achieve its goals, then we have an opportunity on our hands.
I was eager to see how far I could get, so I chose smart city and real estate services to start with. I spent months researching their services and identifying how the value that geotagged news provides could be transferred and integrated. Furthermore, I found it important to also research the value that journalism would be getting back itself — was this an opportunity to reach new audiences, collaborate with businesses, sell data?
With this process, you’re able to create a valuable ontological framework, one where Gingras explains you’re “connecting the dots between consumer interest and knowledge… quality journalism.”
Former Boston Globe and Toronto Star editor, David Skok, also reports from ISOJ to correlate this process to Interdependence and Modularity Theory and how it's applicable to many industries.
Alvin Toffler’s seminal book Future Shock (1970) supports this modular perspective where he posited that even our social interactions are habitually focused on independent, modular values: “We interact with specific modules of a person rather than the full human.”
However, even with a thorough definition, simple model, and a few supporting quotes here, it doesn’t mean things will be easygoing.
One preconceived notion that I’ve encountered with publishers when educating them on semantics with geotagging news, for example, is that they perceive the effort of organizing data as a weight added to the publishing process. Like adding toppings to a hamburger, you can think of it as an action that will make you obese (pessimistic) or one that will make you grow (optimistic). There needs to be a change in how publishers perceive this process to appreciate the latter, to see data as an investment.
As I’ve stated a few times before, I strongly believe publishers must be held accountable for gathering data in their content rather than relying entirely on technology to do so. And Doug Fisher warns “your future in journalism probably depends on it.”
Where semantics could lead journalism
The model I’ve explained only covers the basics. There is so much more semantics could help journalism evolve into as we get more acquainted with this practice.
ML and AI: Opportunities on demand
If data is organized well across journalism and other industries, then there’s nothing stopping us from passing datasets to a machine learning model that can help us quickly identify where relationships exist.
Perhaps then, AI can identify where value-needs exist that our content doesn’t achieve yet — “Hey journalist, if you create content with [this value] and with [this data point], there are [X] services that would benefit by connecting with your work.”
This is the simple nature of machine learning and AI and can be done today, but we have to make the data accessible in order to get there.
Strategic design: Thinking outside of the individual
Especially critical with local journalism, we are essentially designing experiences where our service’s impact on the individual is equally important to its impact on the community.
Strategic design is mission-oriented, ecology-centered design for services. Many products and services have been made with respect to interaction design for the individual (the doorbell), new ones implement service design for the home (the doorbell with a camera), and some of those are critically thinking about strategic design for the community (the doorbell with a camera that sends robbery notifications to the police department or allows access).
These could help produce the city that its citizens might both want and need, balancing desired interactions at the scale of the individual with the systemic [social, civic, and environmental] outcomes required at the scale of the many. This enables and requires design for the homescreen and the city at the same time…
Challenges of semantics
Going through this process and getting experiments launched weren’t always crystal clear — and it’s still not easy to do. There have been three main challenges I’ve encountered while exploring semantics in journalism.
Challenge #1: Vagueness
In general, semantics is vague, vast, and uncertain. These characteristics have made it personally challenging to work with and I’ve found people are typically turned off by working in such broad concepts because the benefit isn’t quite clear — as compared to selling an advertising spot.
What’s been most difficult to communicate is that in order to find benefits of semantic journalism, we have to begin with vagueness and experiment our way to clarity. There’s work ahead, opportunities won’t become clear on their own.
Challenge #2: The Business Case
Identifying tangible business opportunities in semantics requires finding an extremely valuable and scalable connection between your data points and another discipline’s data points (3). One that’s worth paying for.
As I explained above, my most helpful insight for this was to define the value that the data holds, then identify other disciplines that could benefit from that value and can connect with your data.
Challenge #3: Collaborative Culture
What makes this more difficult is that you’re likely to find a new market or approach of some sort. Not every prospect will be open to being an early pilot with your unvalidated solution. Over time, I’ve learned how younger or evolving industries or organizations are more susceptible to this type of collaboration than others. Designing Bloom’s approach around them helped me begin conversations and projects sooner.
Every day, more people in journalism are thinking in terms of collaboration. Most recently, attention has been given to the collaboration in news production — working with residents to identify, research, and publish stories such as City Bureau’s Documenters. But there’s another side to collaboration that’s much less acknowledged in the industry — collaboration for news distribution. Journalism’s collaborative culture must live on both sides of the story — pre- AND post-publication.
Additionally, organizations who have technology that journalism wants to collaborate with must also have a collaborative culture. Those who aren’t traditional news outlets is where I believe we’ll find the most sustainable, diversified collaborations, but these have proven more difficult to initiate.
In 2017, I approached dozens of real estate services in NYC to begin conversations for opening local news distribution channels. My pitch was to help them improve their customers’ local experiences and neighborhood descriptions in real-time with local news. A very small handful of firms and publishers were open to doing such a collaboration. On the other hand, I also approached real estate firms outside of the U.S. who expressed lots of interest in becoming a distribution channel for their region. However, this time, their local publishers were not open to collaborating.
It’s certainly been a winding ride to get to this point but I’m extremely happy with how far these once-ideas have gone today.
Bloom’s real estate collaboration in NYC is enabling publishers to connect with and serve a young pool of 1,600 newly-moved residents right now with experiences personalized for their home. We began with just 200 residents in February last year and are set to reach 3,000 before the end of this year. Local publishers in NYC and NJ can register and participate at no charge.
Our collaboration with smart city firms has been more challenging than expected, which you can read in the next post. We’ve done more research in this space than actual development, but I’m certain opportunities will move closer within reach as this type of technology improves in the near future.
One major perspective that all of this work has helped me acquire is that innovation in journalism may not be what we build onto or around the story, but how we break it down and leverage hidden value within its contents. The semantic web will not come in the form of a new device or interface, it is merely an internal strategy of organizing and connecting data in a meaningful way.
I think it’s also important to realize that this change mustn’t and will likely not be an effort of a single organization. It needs to be a dedicated, collaborative effort across the industry. There have been efforts that have pushed very far into certain directions with semantics, such as data gathering and storage, which has brought up valid concerns about data ownership. And there will be many more efforts that push journalism’s limits and raise concerns as well. But these concerns shouldn’t be seen as signs to avoid this new direction. This type of response from industry shifts is natural. As long as stakeholders are educated, aware, and ethical in how they approach these efforts, I’m sure we’ll achieve mutual fairness and business success.
There’s much left to learn and experiment with, far more than what I have resources for alone. So I hope this writing has made sense to you and heightens your data senses a bit to seek new ways the content you work with can be valuable to others.
I’d be happy to hear from you, about your thoughts, questions, or experiences related to this — please share in the comments.
Thank you for reading!