Post in the Machine: How Blockast is Taking a Shot at Local News Collection
Were I to count the ways mainstream news outlets, hyperlocals and other hopefuls have tried to tackle the challenge of successfully covering local news for a mobile audience (which is to say… everyone), I’d need more hands.
This is not to imply prior efforts have been in vain. Rather, for me, it’s evidence this nut is far from cracked.
With news coverage in the digital age (particularly local) we’ve seen the pendulum swing from massive human-driven operations to semi-automated curation, and partway back. I remember spending several days at the Google campus in the early aughts where most of my conversations with execs involved me arguing the value of a human touch and them over-talking me on how algorithms and automation could deliver a better product. It was ultimately a discussion of customer experience, and by the end of my trip I found myself debating similar points with Larry Page — and not many disputes are won in that court. Still, I think it remains an open question which is the best path.
And the automation camp keeps graduating hopefuls, each eyeing the problem of local news presentment. One of these is called Blockast, a four-year-old startup spawned from the mind of a former Microsoft-Amazon engineer who faced a very common problem: “how do I find out what’s happening outside my home right now?”
“One morning in August of 2013 I stepped outside of my house and saw a police helicopter circling nearby,” said Jason DeMorrow, a 15-year technology vet and entrepreneur with a longtime interest in geo-centric services and machine learning.
“These days I live in a pretty nice neighborhood, so that was kind of surprising. I pulled out my phone to do some searching but found nothing. I had just started laboriously browsing the local news sites for some mention of an incident nearby when my neighbor saw me standing in my driveway. He’d just happened to catch the local news on TV and told me there was a police standoff with an armed suspect in a nearby apartment complex. Of all things, some guy had pulled a gun on his friend for beating him at chess! I thought to myself, how is it I can get a notification on my phone every time a friend likes my food pics but I can’t get an alert when there’s a police standoff on my way to work? That was how Blockast was born.” (The initial private release in 2013 was followed by a public launch in 2015 and refinements this month.)
DeMorrow believes proximity binds people and aligns their interests in ways nothing else can.
“You and your neighbor can have nothing in common but you’ll both want to know when there’s a five-alarm fire down the street,” he said. “Of all the forms of shared relevance between people, location is the least well served by technology. Even with the recent success stories in location-based discovery and networking, the field still feels mostly nascent, which makes it that much more exciting.”
I sat down virtually with DeMorrow to probe some of these contentions and explore why he believes the Blockast app (still rough around the edges for sure, and only in 11 markets currently) is the answer.
First of all, do you think people really care about local news?
Absolutely I do, and I think they’d care even more if they knew how much happens right under their noses. In the deluge of news items we see online, our attention is dominated by stories that manage to be relevant in ten words or less. That sort of quick relevance is easier when you’re targeting a national audience because you’ve got a deep well of impactful stories from which to draw.
The challenge for local news is that proximity to the reader is what makes their stories impactful, so you have to make that proximity very quickly obvious to compete for readers’ attention. This is the problem Blockast sets out to solve, to make local news quickly and easily discoverable by proximity.
I once saw a story in my feed about a felon escaping custody from a courthouse in town. If all I’d seen was the headline I probably would’ve passed it by, but because it happened two blocks from my front door I stopped to read the article. I’ve been honestly surprised by how often that happens to me now.
So let’s get into it – talk about the app and how you expect people to utilize it.
The app itself is designed to be as straightforward as possible. The first time a user opens it they’ll immediately get a list of news headlines with direction and distance from their current location. The closest, most recent items are shown first. They also have the option of entering a street address to see news and events happening near that location.
When a user taps on a news item to view it, they’re shown a brief summary of the item, a thumbnail, and a map of the relevant location. From there, they can go on to the news provider’s site to read the full article, view a larger map of the location in the story, or share the story with others.
Finally, users will get an app notification whenever high-impact items (violent crime, fires, etc.) are discovered near their last viewed location. We currently serve New York, DC, Boston, Detroit, Chicago, Atlanta, Dallas, Denver, Seattle, San Francisco, and LA. If there’s enough interest we can add a new city in a day or two but for now we have to keep our costs low. That means restricting our service to major urban areas for the time being.
How do you gather the news that’s relevant to me?
Behind the scenes, our service monitors select RSS and Twitter feeds for geo-locatable content. When we find something, we immediately index it and send notifications if necessary. We process new content throughout the day and in most cases we’ll surface a new article or tweet within minutes of it being published.
In October of last year there was a six-alarm fire in the Chelsea neighborhood of Manhattan. We picked up and geo-located the FDNY tweet several hours before any of the local news outlets picked up the story. Of course we also picked up their stories once they went live too.
Do you do any editing or is this all automated?
As much as we love human judgement, it’s unlikely that we can make a viable business out of this if we have to employ an army of editors. Our goal has always been to be completely automated and to make this as close in quality to manual curation as possible. The advantage of this is that we can pick up stories almost immediately after they’ve broken, as in the case of the Chelsea fire.
How about the alerts feature — when would I expect to be notified?
Whenever a high-impact news item such as a violent crime is discovered, we send a notification to all users whose last viewed location is within a certain radius of it. We try to be very selective about the items for which we send alerts, so right now they aren’t very frequent.
There’s a big social element potential here. What are you doing in that area?
There are social elements, like voting and sharing, but that’s as far as it goes today. Our first goal is to make monitoring your community fast, frictionless, and compelling.
This may sound like heresy, but we’re not all that interested in what your friends or followers are into. Location is relevance to us, and the only people who should influence your feed are people (probably strangers) nearby. That’s not to say we won’t become more social one day, perhaps letting users in proximity to a news item comment on it, like Spout for news. That’s not our focus right now, though.
What about Patch and the others — you aggregate their content, so are they competitors?
We’re all about content discovery. We don’t compete with content providers like Patch any more than Google competes with websites in their search results. Our goal is to drive traffic to these providers because we have a vested interest in their success.
We only provide a brief summary, a thumbnail, proximity, and location. If our users want the full story, they have to navigate to the content provider’s site. That’s the way it should be, and we’re more than happy to have an open dialogue with any content provider who feels like we’re overstepping our bounds.
What are your thoughts on community-generated news in apps like Nextdoor?
I love Nextdoor and am fortunate enough to live in a neighborhood where it’s actively used. To me it feels more like the neighborhood bulletin board than a source of hard news. That may change and they may become a great source of location-discoverable content.
Then it’s a question of whether they’d be willing to expose that content outside of the community where it was generated. The other problem you run into with community generated news is reliability. I think it’d be fascinating to tackle the problem of community news verification at scale, but that’s one of the toughest problems out there. Until somebody figures it out, professional content providers will continue to be the only reliable source of local news.
What are your plans for monetizing the Blockast, if any?
We may run location-relevant ads one day, but these would be relatively sparse. Our costs are low enough that we might even break even on ad revenue. The more exciting opportunity for us is aggregated, location-specific marketing data. We can collect anonymous data about reading habits tied to particular locations, across each of the most populous cities in America. Our goal is to reach the point where can tell everyone from insurance salespeople to presidential candidates about the most pressing concerns in a given region, down to the square mile.
What have you learned from talking to your customers?
We’ve had several rounds of feedback from family, friends, and beta-list users. Our UX is very heavily based on this. One important source of user feedback is the anonymous usage data we collect. This gives us a very detailed sense of the features our users like and the ones they’re ignoring. For example, we knew within 48 hours of adding ‘search-by-address’ that it was a hit.
We’ve gotten some great anecdotal feedback from users as well:
One of our users was notified that his kid’s school was on lockdown due to a burglary suspect loose in the area. That was a great feeling, knowing that we’d delivered important local news to someone who otherwise might not have seen it.
While it remains to be seen whether these sorts of smart, location-centric aggregators will fill the gap in obtaining information that would commonly be agreed to as “local news” – but one thing’s for sure, as illustrated above, while machine learning can lessen the “need” for human editors on the receiving end of the content spigot, the sources of news will long be from flesh and blood, not bits.