Anna Schier’s first job in media involved one piece of equipment and a lot of patience. Sitting in a small Chicago newsroom, she listened to a police scanner with “one ear on the scanner at all times,” waiting for the crackle that signaled news.
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Today, as a national breaking news editor at Patch.com, Schier still listens for emergencies. But instead of a single radio tuned to one city, she monitors Dataminr, an AI-powered platform that scans thousands of public sources—from police and fire channels to social media posts and traffic cameras—to flag early signs of news events in communities she has never set foot in.
For a network of hyperlocal sites that covers more than 1,900 communities with lean staffs, that shift has changed how Patch finds and prioritizes breaking stories. It has also raised new questions about verification, information overload, and what it means to cover a place you know mostly through data.
A thinly stretched local network
Patch Media runs local news sites in markets ranging from Los Angeles to Wheatland, Wyoming, population just under 4,000. The stories swing from nationally relevant—major crimes, severe weather—to the deeply local: stolen e-bikes, school board fights, town parades.
Most of that reporting still happens the old-fashioned way. Editors and reporters attend municipal meetings, file from courthouses, and rely on tips from residents and community Facebook groups. In many small markets, a single reporter covers everything.
Schier and other breaking news editors sit above that structure, providing backup when big stories break or when a local editor is on vacation. “If we have a story that’s breaking in an unstaffed area that might still be of interest to readers or someone’s on vacation or we just need a set of extra hands, that’s kind of where I come in,” she says.
The model creates a central problem: how to know, quickly, when something newsworthy is happening in a town where the company has no one on the ground.
Replacing one scanner with thousands of feeds
Dataminr is designed for that gap. The platform ingests information from police scanners, traffic cameras, government advisories, corporate disclosures, blogs, alternative social media services, and public sensors such as power outage and flight data. Its AI runs trillions of computations on billions of inputs each day, looking for patterns that suggest something is happening.
For newsrooms, those patterns arrive as alerts filtered by geography and severity. Patch configures its Dataminr feed to focus on the kinds of stories its readers expect: crime, fire, weather, and significant community events.
Alerts fall into three broad categories:
- Flash: major national stories, such as presidential announcements or large-scale disasters
- Urgent: regional breaking news including serious crimes, accidents, and severe weather
- Alert: lower-priority items
Most Patch editors rely on Urgent alerts, which capture local breaking news without overwhelming staff. The system pipes notifications into email inboxes for searchability and into Dataminr’s web dashboard for real-time monitoring during shifts.
Instead of a single voice crackling over a scanner, Schier now sees a stream of structured alerts, each with enough context—location, source type, initial details—to decide whether to assign a story, flag it for a local editor, or watch for further confirmation.
Speed as a survival strategy
Patch’s audience expects immediacy. “When a story breaks, get one sentence up… and then build it out from there,” Schier says of the company’s approach to high-urgency stories. “The most important thing is to let people know as quickly as possible.”
Without Dataminr, that speed is hard to sustain across 1,900 markets. Local editors know their beats, but they can’t monitor every possible signal alone. For breaking news editors covering unfamiliar territories, the challenge is heavier: there are no entrenched source networks to lean on.
Dataminr doesn’t eliminate that problem, but it narrows the gap. The platform can surface an alert about heavy police presence, a highway closure, or an emerging wildfire in a matter of minutes. In some cases, Dataminr surfaces information five minutes to several hours before it would otherwise land on an editor’s radar via social media or a local tip.
For a newsroom competing with television, radio, and social feeds in each market, that head start is often the difference between leading coverage and playing catch-up.
verification-remains-a-human-job”>Verification remains a human job
The volume and variety of Dataminr’s inputs create their own risks. The platform flags everything from official police bulletins to unverified social media posts, and it does not—and cannot—guarantee that every alert is accurate.
“Dataminr’s job is to raise alarm bells and let me decide what to do with them,” Schier says. “So I don’t necessarily expect that it’s going to be right and I don’t ever trust that it’s right. I always look at the source of where it’s coming from first.”
Patch treats Dataminr alerts as starting points, not publishable facts. Official sources, such as law enforcement accounts, may justify a short initial story with clear attribution, followed by updates. Alerts based on more ambiguous signals—multiple eyewitness posts, scanner chatter without confirmation—require phone calls, cross-checking, and often patience.
Dataminr’s own Multi-Modal Fusion AI is designed to reduce false alarms by looking for corroboration across data types. “A real breaking news event is likely to have corroboration across multiple data sources,” says Mike D’Orio, the company’s chief product officer. Even so, the burden of judgment rests with editors.
Managing overload and tailoring the feed
One of the most consistent challenges noted in Dataminr’s own documentation is volume. Even with filtering, the platform can surface more potential stories than a small team can handle.
Patch’s editors respond by tightening geographic and topical filters and relying heavily on mapping features to see where clusters of alerts are emerging. “Use the filters, use the mapping feature,” Schier advises. “These kinds of tools work the best when you personalize them to meet your needs and to align with your goals.”
Dataminr’s implementation guidance echoes that approach: start with restrictive settings and expand gradually. Newsrooms are encouraged to:
- Define primary coverage areas by county or city clusters
- Designate secondary markets for occasional coverage
- Assign dedicated staff to monitor alerts during peak hours
- Create email rules to sort alerts by priority and desk
Without that discipline, Dataminr can feel less like a scanner and more like a firehose.
Where Dataminr fits—and where it doesn’t
Dataminr is not a universal solution. Its strengths lie in:
- Multi-market coverage: Newsrooms that cover wide regions or national beats, especially in unfamiliar communities
- Early warning: Detecting events before they surface through traditional channels
- Backup capacity: Enabling central editors to support local staff during absences or major events
The platform is less critical when a single reporter has deep, longstanding ties in a small community. In those cases, a text from a parent at a school board meeting or a call from a trusted source may still beat any algorithm.
Subscription costs are also a factor. Dataminr offers custom pricing based on organization size and includes unlimited newsroom licenses, but its own documentation acknowledges that newsrooms with fewer than five staff members may struggle to justify the expense.
Alternatives fill other parts of the monitoring stack. NewsWhip focuses on social media trending rather than raw breaking alerts. Rolli emphasizes tracking disinformation and connecting journalists with vetted experts. Traditional scanner apps provide direct access to emergency communications but require constant attention and only cover a limited geography.
A new kind of listening
For Schier, the move from a single police scanner to Dataminr has not changed the core of the job so much as it has extended its reach.
“Nothing is going to replace the work that a local reporter has done to be informed about a community, to build relationships,” she says. “But Dataminr can be used in tandem with that to get you the story a little bit faster.”
In a fragmented local news landscape, where a small staff may be responsible for dozens of towns, that “little bit faster” can be the difference between being the place readers go first—or not at all.
Newsrooms interested in Dataminr can contact the company’s news team at [email protected]. Implementation typically takes one to two hours for initial setup and one to two weeks for full customization and training.
