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.

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.

The post Inside Patch’s AI-era listening post: how Dataminr rewired its breaking news workflow appeared first on The Media Copilot.

Most publishers collect email addresses by asking readers to subscribe to newsletters or pay for premium content. Golf.com took a different approach: giveaways.

The strategy makes sense for a publication whose core audience consists of gearheads—golfers who obsess over equipment, follow the latest club releases, and constantly upgrade their bags. Why not offer them what they already want in exchange for a few personal details? Enter your email, state, and phone number, and you’re in the running for a Stewart golf trolley or a set of custom clubs.

Golf.com is a small remote newsroom with about 10 full-time editors and reporters, occasionally supplemented by freelancers. Founded in 1998 by Mike and Kass Lazerow, the site grew into a popular destination for golf news, tips, and gear coverage before being sold to Time Inc. in 2006 for $24 million. After Time Inc. was acquired by Meredith in 2018, Golf.com and Golf Magazine were sold to their current owners, Howard Milstein and Emigrant Capital. The publication reaches golf fans with tournament coverage, equipment reviews, and instructional content.

Like most small newsrooms, Golf.com evaluates its software vendors annually to ensure it’s getting the most value for its budget. “We try to be lean and mean and make sure that we’re getting the biggest bang for our buck,” says Kip Morgan, head of audience development, marketing and analytics at Golf.com. A few years ago, the team realized they could consolidate tools and save money by expanding their use of Admiral, a platform they were already using for ad-block recovery.

This is how Golf.com built a first-party data collection system around giveaways, bundled its services for cost savings, and turned equipment promotions into a sustainable audience growth strategy.

Discovering an opportunity to consolidate tools

Golf.com had been using Admiral to recapture revenue from visitors who arrived with ad blockers enabled. The tool prompts these users with a pop-up asking if they’re willing to allow cookies and ads to support the site. It was working well for that specific use case.

Then the team discovered Admiral offered a first-party data service called Connect that could replace their email service provider, Sumo. By cutting Sumo and adding Connect to their existing Admiral account, Golf.com could reduce costs while maintaining the functionality they needed.

This kind of bundling is common for Admiral. “Oftentimes someone will just kind of turn on the ad-block recovery because it’s low-hanging fruit, but then it feels like they’re getting the rest of everything else paid for, because the ad-block is paying it,” says Dan Rua, CEO and co-founder of Admiral. “Golf really wants to know their people better, and so they’re using our first-party data capture for that.”

For Golf.com, the decision was straightforward. “If a new platform can give us 95 percent of what we had in the past for less than 95 percent of the money,” Morgan says, “it’ll just be a little bit of a pain to convert, but it’ll be worth it at the end.”

Migrating from Sumo and building custom features

As with any migration, there were growing pains. Golf.com needed specific functionality that Admiral didn’t offer out of the box, including a state dropdown for collecting geographic data from users. This feature mattered because Golf.com wanted to target readers with regional golf promotions—tournaments, courses, and events based on where they live.

Admiral worked with the Golf.com team to build the state entry dropdown and develop an API that allowed the site to “launch new campaigns with all the functionality” it needed, Morgan says. The collaboration required patience and coordination, but it resulted in a system tailored to Golf.com’s specific use case.

This responsiveness to feature requests became one of the benefits Golf.com values most about working with Admiral. For a small newsroom without extensive technical resources, having a vendor willing to build custom solutions made the platform viable.

Designing giveaway campaigns with Admiral’s pop-up editor

Once the technical infrastructure was in place, Golf.com began running giveaways using Admiral’s Connect module. The process involves setting up custom, branded pop-ups that prompt readers to enter their email, state, phone number, and other personal data.

Admiral’s editor allows publishers to control how these pop-ups appear. They can take over the entire window—requiring visitors to interact before accessing content—or appear in a corner as an optional “Nudge” that lets visitors browse freely. Publishers can also customize font, colors, images, size, and branding to match their site design.

Golf.com uses these giveaways regularly, sometimes running multiple promotions simultaneously. “There were some times, like during the Masters, where we had sold a giveaway to someone, or promised to feature their product in a giveaway, where we had a couple running, and they ran in rotation,” Morgan says. The campaigns vary in performance based on prize quality and timing—higher-value gear and tournament-season promotions tend to drive more entries.

The approach transforms what could feel like an intrusive data request into something readers actively want to participate in. “It gives the site an additional sort of fun, engaging thing of like, ‘Oh, not only can you come and read articles, you can come and win stuff or enter the giveaway.’ It’s kind of fun,” Morgan says.

Automating email collection and integration with existing tools

Once readers enter a giveaway, their data flows into Golf.com’s broader audience engagement systems. The site uses Zapier to automatically connect emails collected through Admiral into Sailthru, its email service provider.

“We use [the giveaways] to grow emails that will then grow further engagement, because we’ll be sending them our newsletters,” Morgan says. This automation means giveaway entries translate directly into newsletter subscribers without manual data transfer or additional technical work.

The first-party data collected through giveaways provides more than just email addresses. By asking readers to self-identify their location, Golf.com can create targeted audience segments for regional promotions. “If we’re looking to do regional promotions to travel destinations in a certain location, we would say, ‘Send them to the state or these groups of states,’ based on the self-declared geo information” collected through the giveaway pop-ups, Morgan says.

This segmentation capability allows Golf.com to send readers information about golf tournaments, courses, and events relevant to where they live, increasing the likelihood of engagement. Publishers looking for more advanced audience segmentation may want to evaluate enterprise platforms like BlueConic, which offers unified visitor profiles across multiple channels.

What didn’t work—and how they adapted

• Conversion rate dip: Golf.com’s 2025 conversion rates (impressions vs. emails) came in at approximately 0.5 percent, slightly below the 0.7 to 1 percent they saw with Sumo. However, the team still considers the switch successful due to cost savings and an improved reader experience.
• Dual pop-up problem: When Golf.com was running both Sumo for giveaways and Admiral for ad-block recovery, readers sometimes encountered two pop-ups on the site. Consolidating to Admiral eliminated this friction, creating a cleaner experience.

The results

Golf.com has “driven tens of thousands of emails a year” with Admiral, Morgan writes in an email. While conversion rates are slightly lower than with their previous tool, the overall impact is positive when factoring in cost and user experience improvements.

The biggest win for the small newsroom is financial. “For us, the single Admiral platform was more cost-affordable than having two different ones,” Morgan says. By bundling ad-block recovery and first-party data collection into one vendor relationship, Golf.com reduced expenses while maintaining essential functionality.

The team also values Admiral’s responsiveness to feature requests. Being able to work with the vendor to build the state dropdown and other custom features made the platform workable for their specific needs.

What’s next for Golf.com

Given the success of the giveaway strategy and the flexibility Admiral provides, Golf.com is positioned to expand its use of first-party data for more sophisticated audience segmentation and targeted promotions. The platform’s ability to integrate with existing tools through Zapier and custom APIs suggests potential for deeper automation and personalization in future campaigns.

Publishers looking to implement similar first-party data strategies on a budget can explore Admiral’s Connect module starting at $50 per month, with pricing that scales based on monthly pageviews. Admiral offers a seven-day free trial for all products, and enterprise pricing is available through demos.

The post How Golf.com built a first-party data engine with giveaways using Admiral appeared first on The Media Copilot.

For Proto Thema, one of Greece’s largest online publishers, reader comments were both an asset and a recurring headache. Open threads encouraged debate, drove return visits, and kept readers on the site longer. But anonymous participation also attracted a steady stream of abuse, off-topic arguments, and toxic exchanges that staff had to sift through comment by comment.

Journalists found themselves spending hours on work they neither enjoyed nor considered core to their jobs. Delays in manual review meant comments appeared long after articles were published, undermining real-time discussion and the engagement metrics advertisers care about. More than once, the newsroom weighed shutting comments down entirely.

Utopia Analytics offered a different option: an AI system trained on Proto Thema’s own moderation history that could shoulder most of the decision-making while staying within the outlet’s standards. The goal was not to outsource judgment entirely, but to ensure that human moderators focused on edge cases instead of every single submission.

The gist

Utopia’s deployment at Proto Thema shows how an AI-led approach can keep comments open without overwhelming staff.

• AI-powered moderation now handles 80–90 percent of comments automatically
• Journalists recovered about 80 percent of the time once spent on manual review
• Monthly comment volume tripled to roughly 250,000, with readers staying longer on site

How they did it

Proto Thema’s starting point was a familiar mix of high comment volume and limited moderation capacity. The newsroom allowed readers to post without logging in, which made participation easy but also opened the door to “many, many, many” inappropriate comments that staff had to catch after the fact.

Training on historical data: Utopia began by ingesting several months of Proto Thema’s accepted and rejected comments, along with the outlet’s moderation guidelines. That dataset allowed the AI to learn how the newsroom differentiated between acceptable debate and comments that should be blocked.

Building a context-aware model: Rather than relying on static keyword lists, Utopia’s system evaluates each comment in its broader environment—article topic, headline, whether it is a new comment or a reply, and up to six lines of conversation history. That makes it more effective at catching subtle insults, coded language, and seemingly neutral phrases that become problematic in specific contexts.

Setting confidence thresholds: Each comment receives a confidence score indicating how likely it is to pass moderation. Proto Thema started with conservative thresholds, allowing the AI to auto-approve or reject only the clearest cases while routing borderline comments to human reviewers. As trust in the system grew, thresholds were adjusted upward to increase automation.

Ongoing calibration: Utopia recommends regular check-ins during the early months of deployment. Proto Thema’s team reviewed false positives and false negatives, discussed edge cases, and refined policies so the model could be retrained on more precise examples of desired behavior.

Key numbers

Utopia’s implementation at Proto Thema produced a mix of time savings and engagement gains.

• Moderation time saved: Journalists gained back roughly 80 percent of the hours they had previously devoted to comment review, allowing them to focus on reporting and editing.
• Automation rate: Utopia’s AI now handles about 80–90 percent of moderation decisions automatically, reserving only complex or sensitive cases for human review.
• Comment volume: Monthly comments have approximately tripled since deployment, to around 250,000 per month.
• Audience behavior: Readers are spending longer on the site, with some visiting primarily to read and participate in comment threads.

What to watch for

Utopia’s materials emphasize that successful deployments still depend on clear policies and thoughtful oversight.

• Policy clarity: The AI’s performance is closely tied to the quality of the guidelines and historical decisions it is trained on; vague or inconsistent policies will produce uneven results.
• Edge cases and sensitive topics: Even with high automation, Utopia recommends maintaining 10–20 percent human review for breaking news, controversial subjects, and borderline comments that require editorial judgment.
• Moderator behavior: Analytics have revealed that some human moderators in other organizations simply “click accept, accept, accept” when paid by volume, a reminder that human oversight can drift without monitoring.
• Security and privacy detail: Public-facing materials offer limited specifics on technical controls; publishers still need to review contracts and documentation with their legal and security teams.

In Proto Thema’s case, Utopia turned moderation from an exhausting chore into a manageable part of daily operations. The comments section remains open, engagement is higher, and journalists spend more of their time on the work they were hired to do.

For publishers facing similar pressures, Utopia’s example suggests that AI-led moderation, when trained on local standards and paired with clear policies, can reclaim the value of the comments section without accepting the worst of what it can become.

The post How one Greek publisher reclaimed 80% of moderation time with AI appeared first on The Media Copilot.

When The Post and Courier expanded its investigative coverage across South Carolina, the 200-year-old Charleston newspaper faced a data problem. Reader information was scattered across newsletter platforms, subscription systems, website analytics, and CRM tools. No unified view existed, making it difficult to identify at-risk subscribers, recommend relevant content, or personalize outreach.

BlueConic, a customer data platform designed for media organizations, enabled the consolidation of fragmented data into unified user profiles. The platform uses behavioral triggers to prompt newsletter signups, surface personalized content recommendations, and deliver retention messages to subscribers showing signs of disengagement.

Implementation took six months and required dedicated technical resources. But the results—40 percent lower churn, 115 percent higher content recirculation, and a DigiDay Award nomination—validated the investment for a regional newsroom funding ambitious statewide journalism.

The gist

BlueConic helped The Post and Courier turn scattered audience data into a retention and engagement strategy.

• Unified profiles consolidate data from newsletters, subscriptions, website behavior, and CRM systems.
• Behavioral triggers automate personalized prompts for signups, offers, and retention messages.
• Subscriber churn dropped 40 percent; content recirculation clicks rose 115 percent.

How they did it

The Post and Courier’s implementation followed a structured, multi-phase approach over six months.

• Audited existing data systems: Mapped where audience data lived—email platform, subscription database, analytics tools—and identified integration requirements before touching BlueConic.
• Defined high-value users: Established criteria for engaged readers likely to subscribe versus low-value one-time visitors, focusing data collection on the former.
• Built unified profiles: Connected data sources to BlueConic’s “listeners,” which capture user behavior and append it to individual records over time.
• Configured behavioral dialogues: Set up targeted pop-ups triggered by reader actions—newsletter signup prompts for engaged non-subscribers, retention messages for at-risk paying subscribers.
• Deployed dynamic recommendations: Replaced manual “related stories” curation with algorithmic suggestions based on each reader’s profile and interests.
• Tested and refined continuously: Used BlueConic’s A/B testing tools to experiment with modal designs, timing, and recommendation algorithms.

Key numbers

The implementation delivered measurable improvements in engagement and retention.

• Churn reduction: 40 percent fewer subscriber cancellations, exceeding the initial 35 percent target
• Content recirculation: 115 percent increase in click-through rates on recommended stories
• Deep engagement: 14.2 percent more readers consuming five or more articles within 30 days
• Recognition: Nomination for Best First Party Data Strategy at the 2025 DigiDay Awards

What to watch for

BlueConic requires significant commitment and isn’t a plug-and-play solution.

• Implementation timeline: Expect six months from kickoff to full deployment; rushing the process risks poor integration and incomplete data.
• Technical resources: Requires either in-house expertise or outside consulting support familiar with CDPs and newsroom workflows.
• Cost: Custom pricing based on data volume and organization size; leadership buy-in on investment is essential.
• Legal review: Built-in consent management tools exist, but newsrooms must work with counsel to update privacy policies and ensure regulatory compliance.
• Ongoing optimization: The platform requires dedicated staff time for testing, refinement, and response to changing audience behavior.

For newsrooms with clear goals, substantial existing data, and technical capacity, BlueConic offers a path to sophisticated audience engagement. Smaller outlets with simpler needs may find native CMS solutions like Newspack or Blox more appropriate. Contact BlueConic’s sales team for custom pricing and implementation guidance.

The post What it takes to implement BlueConic at a regional newspaper appeared first on The Media Copilot.

Dan Gaul helped launch Digital Trends in 2006 during what he calls the “Wild West days” of online publishing. The formula was simple: create content, optimize for Google, watch the traffic roll in. Display ads and affiliate links funded the operation. The clicks kept coming.

That model is collapsing. When Gaul pulls up TollBit’s analytics dashboard today, the numbers tell a brutal story. In the past week alone, Digital Trends received 4.1 million bot scrapes. During that same period, AI chatbots referred just under 4,200 human visitors back to the site.

“966 AI scrapes to one referral,” Gaul says. “It’s crazy.”

Digital Trends, based in Portland, covers consumer electronics, smart home technology, gaming and lifestyle content through its flagship site and eight additional verticals. The company remains privately held and independent nearly two decades after founding. But the shift from search traffic to AI overviews has been devastating. The company once employed 170 people, most in editorial roles. Today it relies primarily on freelancers.

“It’s really hard to maintain a huge workforce when we don’t have private equity or large media dollars behind us,” Gaul says.

The fundamental equation changed. AI applications read dozens of articles per query but deliver minimal referral traffic. Bot traffic increasingly outnumbers human pageviews. Some hosting providers now charge publishers for bandwidth consumed by scrapers—a double punishment where publishers lose human traffic while paying to serve bots. TollBit offered a way to quantify the problem and potentially monetize what was otherwise pure extraction. Implementation took under 30 minutes and cost nothing upfront.

Implementing lightweight monitoring without backend access

Gaul started with TollBit’s monitoring features, which require only a JavaScript tag similar to Google Analytics. The setup demands no backend system access or complex technical integration. For Digital Trends, implementation took under 30 minutes—mostly DNS configuration that any IT team could handle.

TollBit operates on a “three Ms” framework: Monitor, Manage, Monetize. Co-founder Toshit Panigrahi designed the platform to address how the web’s primary consumers shifted from humans to autonomous agents. Traditional analytics tools like Google Analytics track human behavior but miss bot activity entirely. Publishers couldn’t quantify what was happening to their traffic.

The monitoring dashboard reveals which AI services and bots access content, how frequently, which specific pages get scraped and how many human referrals arrive in return. This data doesn’t exist in standard analytics. Google Analytics counts human sessions. TollBit counts the bots reading content without sending humans back.

For Digital Trends, the analytics immediately quantified what Gaul suspected. ChatGPT’s crawler accounted for 3.6 million scrapes over one month—87.8 percent of all AI bot traffic to the site. The homepage got scraped most frequently as bots checked for fresh content. Individual articles showed surprising patterns. The top scraped article in October 2025 was “Instagram finally fixes the one thing you hated about Reels”—a timely news story without lasting SEO value.

Gaul remains unsure why that particular article spiked bot traffic, but he’s hoping monitoring will reveal long-term trends over time.

Understanding which content AI extracts most aggressively

The analytics revealed patterns about which content types AI scrapers target. Evergreen content like “how to take a screenshot on Windows” took the hardest traffic hit because AI can answer those queries with specific step-by-step instructions without attribution. Reviews of consumer electronics similarly suffered as AI aggregates specs and ratings from multiple sources.

“Information that is data-centric or spec-centric I think is where publishers are getting hurt most,” Gaul says. “And where they’re getting hurt the least is more on the featured, editorial, creative writing stuff.”

This distinction matters for editorial strategy. Content easily summarized or spec-heavy faces maximum AI competition. Original analysis, narrative features and creative work face less substitution because AI can’t replicate unique perspectives as easily as it aggregates facts.

Bandwidth costs compound the problem. Some hosting providers charge publishers for traffic consumed by bot scrapes. Digital Trends avoided this through their provider choice, but Gaul heard from other publishers facing the double bind: losing human traffic while paying to serve automated scrapers.

TollBit’s monitoring helps publishers track CDN costs from automated traffic and identify the specific bots driving bandwidth consumption. This data informs both technical decisions—which providers penalize bot traffic—and business decisions about whether enforcement or licensing makes more sense than absorption.

Evaluating monetization without implementing paywalls yet

Digital Trends hasn’t activated TollBit’s bot paywall or monetization features. The platform offers two licensing types publishers can price independently: summarization licenses allowing AI products to use content once for citations or grounding, and full display licenses closest to lightweight syndication rights. Neither permits model training.

Publishers set rates per 1,000 pages accessed. TollBit handles transaction infrastructure—metering, checkout, invoicing—while charging AI companies a small fee on top of publisher-set prices. The revenue-sharing model means publishers pay nothing upfront or monthly. Costs come only from the AI companies accessing content.

Gaul remains realistic about monetization potential. “We’re just not there yet in terms of the whole AI ecosystem,” he says. The licensing marketplace remains nascent. Revenue is unpredictable. But the infrastructure matters for future positioning as the market matures.

Panigrahi argues uniqueness drives premium pricing: “If you have an article that is irreplaceable, that you cannot find anywhere else, you can command a very high premium.” This applies particularly to local news outlets publishing information—school closures, municipal meetings, community events—that appears nowhere else on the web.

The team at TollBit thinks bot traffic will only increase. “These AI companies are coming tens of thousands of times a day to your site. It’s not that they just crawled it once, took the content and left. They need to access this regularly,” Panigrahi says.

Balancing enforcement with legitimate search access

Implementation requires policy decisions beyond technical setup. Publishers must define which bots get allowlisted—approved for free access—versus which face paywalls. Search engines like Google need free access for SEO to function. Legitimate research tools, accessibility services and archival projects may warrant exceptions.

Misconfiguration risks accidentally blocking tools publishers want accessing content. TollBit handles technical enforcement through subdomain routing and robots.txt rules, but publishers determine policy: default action for unauthorized bots (block entirely or redirect to paywall), allowlist maintenance and published terms.

These decisions involve legal and finance teams, not just editorial or technical staff. TollBit’s documentation recommends inviting stakeholders early to review terms and configure payout methods—even if monetization activation comes later.

The platform requires ongoing vigilance. Bot spoofing remains a challenge as unauthorized scrapers masquerade as legitimate browsers. TollBit provides detection tools, but publishers must monitor dashboards regularly to catch evolving evasion tactics.

What didn’t work—and realistic expectations

Digital Trends’ experience highlights limitations in current AI licensing economics. The platform provides sophisticated monitoring and enforcement infrastructure, but the marketplace connecting publishers to AI companies willing to pay remains underdeveloped.

Documentation doesn’t specify implementation challenges Digital Trends encountered beyond the broader reality that monetization hasn’t materialized at scale. The “knowledge value” Gaul emphasizes—understanding bot behavior patterns—represents the current payoff rather than direct revenue.

Publishers considering TollBit should calibrate expectations accordingly. The monitoring delivers immediate value. The licensing infrastructure positions publishers for future opportunities. But guaranteed revenue justifying implementation time remains speculative.

The results

Digital Trends’ TollBit implementation provided detailed analytics quantifying AI scraping patterns previously invisible in standard analytics tools:

• Bot scrapes tracked: 4.1 million in one week
• Human referrals received: 4,200 in the same period
• Scrape-to-referral ratio: 966 to 1
• ChatGPT dominance: 87.8% of all AI bot traffic (3.6 million monthly scrapes)
• Revenue generated: None—monitoring only, no monetization activated yet

Gaul’s assessment remains measured: “The value of TollBit is the knowledge.” The data informs strategy without delivering income.

What’s next for Digital Trends

Gaul’s editorial strategy has shifted from traffic optimization to community building. “At the end of the day, it’s going to be about community. How do you build community without relying on Google?”

The monitoring data informs this pivot by revealing which content types face maximum AI substitution versus which retain unique value. Featured editorial and creative writing suffer less extraction than spec-heavy product reviews—a signal about where to invest scarce editorial resources.

(Inference based on documentation: Digital Trends’ monitoring-only implementation suggests future monetization activation depends on AI licensing marketplace maturation. As more publishers join TollBit’s network and collective bargaining power increases, implementing bot paywalls and pricing to capture compensation for millions of weekly scrapes becomes more viable. The current approach—gather data, understand patterns, position for future revenue—reflects realistic assessment of marketplace timing rather than technical limitations.)

Publishers can explore TollBit’s free monitoring and licensing infrastructure at tollbit.com. Setup takes under 30 minutes with no upfront costs. The platform works best for outlets willing to invest monitoring time now for knowledge value and potential revenue later.

The post TollBit can monitor AI bot scraping, track referral traffic declines appeared first on The Media Copilot.

Givebutter is a nonprofit fundraising platform that offers donation collection, campaign pages and event ticketing at no monthly cost on its basic tier. For news organizations exploring reader revenue, it provides a low-barrier entry point—though its fee structure and data portability limitations warrant attention.

This guide covers the essentials of getting a Givebutter campaign running.

The gist

Givebutter lets newsrooms collect donations and sell event tickets without paying monthly software fees.

• Free tier relies on optional donor tips; declining to donors the option to tip triggers a 3% platform fee
• Three campaign types: donation forms, fundraising pages, and ticketed events

How to set it up

Givebutter’s setup process is designed for users without technical support.

• Create an account at givebutter.com with your nonprofit name, EIN, work email and fundraising goal.
• Choose a campaign type:
• Donation form for basic, embeddable payment collection
• Fundraising page for storytelling, video, progress tracking and matching gifts
• Event for ticketed in-person, virtual or hybrid gatherings
• Configure campaign details: title, goal, description, branding, SEO metadata, thank-you messages and fee structure.
• Connect payment processing: Add bank account information before requesting payouts.
• Set up donor communications: Free-tier email tools handle updates, progress notifications and acknowledgments. Paid tiers add SMS and direct mail.

Key numbers

Givebutter’s cost structure depends on donor behavior and plan selection.

• Basic tier: $0/month; platform requests optional tips from donors. If donors decline to top, Givebutter will cover the transaction fees.
• Without tips: 3 percent platform fee + standard processing (2.9 percent + 30¢ for cards; 1.9% + 30¢ for ACH)
• Enterprise: Custom pricing for larger organizations

What to watch for

Givebutter’s free model comes with trade-offs newsrooms should understand upfront.

• Tip dependency: The “free” label assumes donors agree to tip. Budget for a 3% processing fee if don’t wish to ask for optional tips. (Givebutter will cover the processing fee if you ask for a tip but the donor declines to do so.)
• Limited analytics: Advanced reporting requires paid tiers.
• No journalism-specific features: Templates and integrations are designed for general nonprofits, not newsrooms.

Newsrooms seeking more robust donor management, faster data export or news-specific tools may also want to evaluate FundraiseUp, RevEngine (via News Revenue Hub) or GiveWP.

Accounts can be created at givebutter.com.

Correction: In a previous version of this post, Givebutter’s capitalization was incorrect. It’s “Givebutter.” Also, fee structure and interoperability with other fundraising platforms was listed incorrectly. The Media Copilot regrets the errors.

The post How To Launch A Givebutter Fundraiser For Your Newsroom appeared first on The Media Copilot.

Dan Gaul, the co-founder and chief technical officer of Digital Trends, suspected AI was gutting his site’s search traffic. Traditional analytics showed referrals dropping as AI overviews replaced direct links. But Google Analytics tracks human visitors, not the bots harvesting content at scale. Gaul couldn’t quantify the extraction.

TollBit’s free monitoring revealed the brutal math: 4.1 million bot scrapes in one week delivered only 4,200 human referrals. ChatGPT alone accounted for 87.8 percent of automated traffic. The data doesn’t generate revenue yet, but it quantifies what was invisible.

“The value of TollBit is the knowledge,” Gaul says.

The gist

Digital Trends’ lightweight implementation exposed the AI extraction economy:

• Implementation required under 30 minutes with no upfront costs
• Analytics revealed 966-to-1 scrape-to-referral ratio documenting traffic asymmetry
• Monitoring identified content types facing maximum AI substitution

How they did it

Digital Trends’ implementation prioritized understanding bot patterns before enforcement:

• Installed JavaScript tracking tag: Added lightweight monitoring code similar to Google Analytics, requiring no backend access or complex technical integration.
• Configured DNS settings: Completed minimal DNS configuration enabling TollBit to route detected bots appropriately—implementation handled by any IT team in under 30 minutes.
• Established baseline metrics: Monitored which AI services accessed content, scraping frequency, specific pages targeted and human referral ratios revealing value exchange.
• Analyzed content patterns: Used data to identify which article types faced maximum extraction—evergreen how-tos and spec-heavy reviews versus original analysis and narrative features.
• Informed editorial strategy: Applied monitoring insights to content planning, recognizing spec-centric information faces AI substitution while creative writing retains unique value.

Key numbers

• Implementation time: Under 30 minutes for monitoring setup
• Cost: Free for publishers (no upfront fees or monthly subscription)
• Bot scrapes tracked: 4.1 million in one week
• Human referrals received: 4,200 in same period
• Scrape-to-referral ratio: 976 to 1
• ChatGPT dominance: 87.8 percent of all AI bot traffic (3.6 million monthly scrapes)
• Revenue generated: None—monitoring only, monetization not activated

What to watch for

Implementation considerations and realistic expectations:

• No guaranteed revenue: Licensing marketplace remains nascent—monetization is speculative positioning for future opportunities, not immediate income stream.
• Requires ongoing monitoring: Dashboard review and policy development demand attention—free implementation doesn’t mean zero time investment.
• Misconfiguration risks: Accidentally blocking legitimate search engines or accessibility tools could harm SEO—allowlist maintenance requires vigilance.
• Bot spoofing continues: Unauthorized scrapers masquerade as browsers—detection requires ongoing adaptation as evasion tactics evolve.

Publishers can explore TollBit’s free monitoring at tollbit.com. The platform delivers value through visibility into bot behavior, with potential revenue dependent on AI licensing marketplace maturation.

The post AI Web Scraping: The Invisible Threat to Websites appeared first on The Media Copilot.

Zetland faced a productivity crisis hiding in plain sight. The Danish digital outlet publishes primarily audio-based journalism, which meant 35 reporters each conducted multiple hours of interviews weekly. Manual transcription consumed five to seven hours per journalist every week—time CEO Tav Klitgaard described as journalists “basically being robots.” Many skipped transcription entirely because the work was so tedious, weakening their reporting by forcing reliance on notes rather than recorded quotes.

When OpenAI released its Whisper speech recognition model in September 2022, Zetland developer Jakob Steinn built an overnight test later called Good Tape after a senior editor complained about transcription burden over lunch. The next morning, a journalist ran into Klitgaard’s office demanding he allocate all resources to the project because “it’s magic.” Zetland spun off Good Tape as a separate company in 2023, and the tool now serves 2.5 million users globally.

This quick reference covers Zetland‘s implementation approach, the measurable time savings achieved, and what other newsrooms should consider before adopting transcription automation.

The gist

Zetland‘s in-house development team solved a transcription crisis by building Good Tape when existing tools failed to handle Danish language audio, then:

• Saved three to six hours per journalist weekly
• Eliminated tedious manual transcription work entirely
• Scaled to 2.5 million global users within 18 months

How they did it

Zetland moved quickly from identifying the problem to building and deploying a solution that transformed newsroom operations:

• Recognized the productivity drain: Leadership calculated that 35 journalists spending five to seven hours weekly on manual transcription represented enormous wasted capacity for actual journalism work.
• Tested the alpha internally first: After Steinn built the first version, Zetland journalists tested it despite slow speed and imperfect accuracy—it was still better than manual transcription.
• Released public beta to Danish journalism community: In January 2023, Zetland asked Danish journalists to test the tool, receiving unanimous enthusiasm and requests to purchase immediately.
• Launched paid version: March 2023 launch of Good Tape Pro proved willingness to pay, with thousands signing up within minutes of the paid tier becoming available.
• Spun off as separate company: Zetland established Good Tape as independent entity to serve journalists worldwide, not just internal needs.

Key numbers

• 200+ hours saved weekly: With 35 journalists each saving three to six hours per week, Zetland reclaimed substantial reporting capacity
• 2.5 million users: Growth from internal tool to global platform within 18 months of public launch
• 90-95 percent accuracy: Typical transcription accuracy requiring minimal correction of names and technical terms
• $17/month: Pricing significantly below competitors charging $24-52 monthly

What to watch for

Implementation challenges emerged despite strong results:

• Feature limitations: Good Tape initially lacked integration with common newsroom tools like Slack and Google Drive, requiring standalone workflow
• Mobile gap: No mobile app available at launch, limiting field recording workflows (mobile app expected in fall)
• Speed vs. quality tradeoff: Early alpha version was slow; balancing transcription speed with accuracy required significant development work

Good Tape offers free testing with no commitment. Newsrooms can evaluate transcription accuracy, interface usability, and workflow fit before purchasing subscriptions. Teams of five or more qualify for custom pricing that scales with organizational size.

Frequently Asked Questions

The post How <em>Zetland</em> reclaimed 200+ journalist-hours weekly with Good Tape appeared first on The Media Copilot.

Laura Lee sent a reporter to check on an empty Ramada Inn expecting a quick beat story. Twenty-two months after Asheville approved a motel conversion for homeless housing, all 113 rooms sat vacant. Early reporting raised more questions than answers.

When Blue Ridge Public Radio discovered the California developer facing 125 similar cases in Los Angeles courts, documents flooded in: court records, government emails, financial statements. A seven-person newsroom suddenly needed organizational capacity they didn’t have. Traditional methods—filing folders, spreadsheet indexes—would have crumbled.

Google’s free Pinpoint platform became BPR’s backbone, transforming document chaos into searchable archives. The resulting investigation won an Edward R. Murrow Award and prevented fraud schemes in other cities. This quick reference covers how they managed the scale.

The gist

BPR’s systematic document organization turned overwhelming volume into award-winning journalism:

• Platform is free for verified journalists with unlimited document capacity
• Automated entity extraction surfaced connections across thousands of pages instantly
• Collaborative features enabled statewide investigation preventing additional fraud

How they did it

BPR’s implementation prioritized searchability and institutional memory from the start:

• Created dedicated collection: Established single Pinpoint workspace for all investigation materials, ensuring centralized access as documents arrived sporadically across months of public records requests.
• Uploaded systematically: Added court filings, government emails and financial records as received, using optical character recognition to make scanned documents fully searchable.
• Leveraged entity extraction: Let automated tagging identify names, organizations, locations and dates across the growing archive without manual indexing.
• Shared with collaborators: When investigation expanded statewide, granted partner newsrooms access to shared collection, enabling coordination without duplicating document requests.
• Maintained searchability: Used platform as institutional memory—reporters could instantly locate details from documents reviewed months earlier when new materials revealed their significance.

Key numbers

• Cost: Free for verified journalists and academics through Google News Initiative
• Document capacity: Up to 100,000 documents per collection with unlimited collections
• Court cases tracked: Approximately 125 Los Angeles cases involving same developer
• Newsrooms collaborating: Three outlets (BPR, WFDD, CityView) coordinating statewide investigation
• Award: Edward R. Murrow Award for investigative reporting, October 2023
• Fraud prevented: Similar schemes in Winston-Salem, Fayetteville stopped after collaborative series

What to watch for

Implementation considerations and limitations BPR encountered:

• Cloud-based hosting: Google hosts all documents, requiring comfort with cloud storage—unsuitable for newsrooms handling materials too sensitive for email-level security.
• Manual analysis still required: Pinpoint organizes and searches but doesn’t interpret—reporters must still read documents and draw conclusions from patterns surfaced.
• Beta AI features unreliable: Generative AI capabilities remain experimental with unclear accuracy—better served using established tools like NotebookLM for AI-assisted analysis.

Small newsrooms facing document-heavy investigations can apply for Pinpoint access at journaliststudio.google.com/pinpoint. Verification typically granted within days for working journalists and academics.

Frequently Asked Questions

The post How a Small Newsroom Used Google Pinpoint for Investigative Journalism appeared first on The Media Copilot.

Susan Catron wasn’t looking to experiment with AI. As managing editor of The Current, a nonprofit investigative newsroom covering Georgia’s coastal communities, she had watched general-purpose tools like ChatGPT produce plausible-sounding nonsense often enough to know the risks.

“I was the one screaming across the newsroom: ‘Don’t touch AI, leave it alone,'” Catron says.

But her 10-person team faced an impossible calculus. Founded in 2020 to fill the vacuum left by consolidated and shuttered local newspapers, The Current aimed to hold public servants accountable through in-depth reporting. That mission required time—time that was instead consumed by the repetitive mechanics of digital publishing. Reporters spent hours each week crafting SEO-optimized headlines, generating newsletter summaries and managing metadata. Unlike larger outlets with dedicated digital teams, The Current‘s journalists handled everything themselves.

Andrew Haeg, network product manager at the Institute for Nonprofit News, describes this as the “fried and frozen” problem plaguing small newsrooms: Journalists are fried from running at maximum capacity and frozen by the fear of wasting scarce resources on technology that doesn’t deliver. For Catron, the question wasn’t whether AI could help—it was whether any AI tool could be trusted with journalism. That search led her to Nota, an AI platform built by journalists specifically for newsroom workflows, trained on journalism data rather than the open internet. “That was my selling point with Nota,” she says. “I don’t know what the other ones do. I just know I trust this one right now.”

Starting with skepticism and a single feature

Catron’s approach to implementation reflected her newsroom’s caution. Rather than rolling out Nota’s full suite of AI tools at once, she began with headline optimization—a single feature that would let her team evaluate the system’s understanding of journalism without risking editorial integrity.

Nota’s headline tool offers three suggestions that editors can accept, revise or ignore. Over time, the system learns from these editorial choices, tailoring future suggestions to match the newsroom’s voice and preferences. This design aligned with Catron’s requirement for human oversight at every step.

The setup took less than an hour. Nota integrates directly into WordPress and other content management systems, eliminating the software installation and training overhead that often derail technology adoption in resource-strapped newsrooms. Catron uploaded 10-15 representative articles to establish The Current’s tone, then began testing suggestions against her own editorial judgment.

“It’s only as good as what we put in it,” she says. Well-reported and well-written articles yielded better AI outputs, reinforcing rather than replacing the newsroom’s commitment to quality journalism. Skilled editors remained essential for catching the small errors that occasionally surfaced.

Expanding to SEO tasks once trust was established

After several weeks of testing headlines, Catron began adding Nota’s other publishing tools to The Current‘s workflow. The platform generates SEO-optimized tags, slugs and meta descriptions—tasks that previously required editorial attention but rarely benefited from editorial expertise.

Unlike general-purpose AI tools that might hallucinate facts or inject marketing language, Nota works exclusively from content journalists have already written and fact-checked. The system doesn’t generate original copy. Instead, it reformats and optimizes existing work for different distribution channels.

This distinction mattered to Catron’s team. Nota’s underlying model is trained on journalism-specific data from sources the company has explicit rights to use. Josh Brandau, Nota’s CEO and former chief marketing officer at the Los Angeles Times, says this targeted training produces hallucination rates “way less than 10 percent”—a crucial improvement over general-purpose tools.

The journalism focus shows in features designed specifically for newsroom needs: C2PA content tagging for transparency, platform-specific formatting for social media and integration with newsletter tools. Each feature addresses a task that consumes time without requiring deep editorial judgment—exactly the kind of work Catron wanted to automate.

Building social media capacity without hiring staff

The Current began using Nota’s social media tools for approximately half of its posts. The platform generates platform-specific captions for X, Instagram and Facebook, adapting tone and length to match each network’s conventions while maintaining the newsroom’s voice.

For a small team juggling reporting, editing and community engagement, this represented significant capacity expansion. Social media presence matters for audience development and reader trust, but crafting effective posts requires time The Current couldn’t consistently spare.

Nota’s social tools work from published articles, generating multiple caption options that editors review and approve. Like the headline feature, the system learns from editorial choices over time, reducing the revision needed for future suggestions.

Catron sees additional potential in using Nota for community calendar updates—a feature that could both serve readers and open new revenue streams, but one her newsroom currently lacks capacity to maintain. The AI-assisted approach could make previously impossible projects feasible.

Establishing editorial guardrails and transparency policies

As The Current expanded its use of Nota, Catron established clear policies about AI use and transparency. The newsroom made AI usage policies explicit early in the implementation, recognizing that audiences increasingly expect disclosure even when trust benefits remain unclear.

Research shows that while AI disclosure alone may not increase audience trust, the absence of disclosure damages credibility. The Current’s approach pairs transparency with consistent human oversight—every AI-generated headline, excerpt and social post receives editorial review before publication.

This human-in-the-loop approach addresses both quality control and ethical concerns. Nota operates on a closed-loop system that doesn’t train on newsroom content without explicit consent. For publications with strict privacy commitments to sources and subjects, this data handling stands apart from general-purpose AI tools that may use submitted content for model training.

The platform employs enterprise-grade security aligned with SOC 2 Type II standards, including data encryption in transit and at rest, secure authentication with single sign-on support and role-based access controls. Transparency features like usage reports and granular access controls help newsrooms maintain oversight of how their content is handled.

What didn’t work—and how they adapted

The documentation doesn’t specify particular implementation challenges The Current encountered beyond Catron’s initial skepticism about AI tools in general. The newsroom’s measured, feature-by-feature rollout appears to have prevented the adoption friction that often undermines technology implementations.

However, Brandau acknowledges broader challenges facing newsrooms considering AI adoption: “It’s a process. To see the full value you need to use it at scale, consistently.” Small newsrooms must balance the upfront time investment in training AI systems against immediate publishing pressures—the same resource constraints that make efficiency tools necessary in the first place.

The results

The Current now uses Nota to handle most SEO tasks that previously consumed hours of staff bandwidth each week—generating headlines, excerpts, tags and slugs. The team uses Nota’s social media suggestions for approximately half of their posts.

“I don’t even remember how much we spend on it a month, but I’m sure it has saved me that much time,” Catron says.

Specific time savings or productivity metrics beyond this qualitative assessment are not documented in available materials.

What’s next for The Current

Catron plans to integrate AI literacy into The Current‘s summer internship program, teaching the next generation of journalists both how to use AI tools effectively and how to maintain healthy skepticism about their limitations.

“I’m going to put the fear of God into them about a few things,” she says with a laugh. “AI can allow us to get better and grow more trust. But it could also kill that trust in milliseconds.”

At the network level, INN has leveraged Nota to expand member content reach across its 500-plus nonprofit newsrooms. INN newsrooms generate approximately 26,000 stories monthly, many under open licenses for republication, but insufficient resources often limit wider distribution. INN uses Nota to create automated workflows that summarize member stories into editorially vetted excerpts for services like Text Rural—an SMS news service for rural communities—and On the Ground, a weekly digest of local political stories. The entire process requires just 15-30 minutes weekly for review and approval.

“You could effectively support a really good product for your audience in minimal time,” Haeg says. “That just wouldn’t be possible without AI—in this case, Nota.”

Small newsrooms considering similar implementations can explore Nota’s grant-backed pricing at heynota.com. Qualifying outlets with fewer than seven full-time employees and annual revenue under $250,000 can access the platform for $99 monthly.

The post How a skeptical Georgia newsroom adopted AI without compromising standards appeared first on The Media Copilot.