How Algorithmic Mislabeling Hides Helpful Content and What We Can Do About It (Part 2

Low-Value? High Stakes | How Algorithms Hide Helpful Content and What We Can Do About It (Part 2 | Google)

So, there I was, sipping coffee and minding my own high-value business once again, when Google decided to let me know that my entire website is officially “low-value.” I’m honored. Truly. To be misunderstood by an algorithm is the sincerest form of digital flattery. Of course, this dubious distinction means that I am not eligible for the Google AdSense program. What a shame, as I was in it previously, beginning in 2018 when I acquired this domain.

However, I had some unfortunate issues with my domain back in 2022 through the present, some of which I covered in other articles, such as Online Reputation Protection | The Ultimate Guide. However, those issues caused Google to revoke my AdSense connection. I’ve reapplied and been denied multiple times since. This time, it was for “thin content.” Since most online articles are around 800-1,100 words per the Content Powered website, and this article is almost 5,200 words, I think the more accurate term would’ve been “thick content.”

As the old song goes, “Baby, baby, where did our love go?” – Supremes

Hunter Storm’s History with Google | Why Is My Assessment of This Situation Valid?

Want a bit more irony to go along with my website’s “low-value” designation? Here is a brief summary of my history with Google. I have:

Had a Google account since its inception. In fact, one of my Google accounts was an invitation-only Beta account. How did I get this? A colleague from a Fortune 500 financial institution invited me to test it when this person left our company and went to Google back in the early 2000’s.
Used many of their products, both in Beta and in Production.
Conducted global enterprise information security risk assessments on Google (and countless other technology company) products in my professional capacity for a Fortune 100 financial institution. Explore my Professional Profile and Career Highlights Hub for more about my experience.
Have been in many conference calls with Google representatives as part of my official role in global enterprise, as well as emerging technology professional organizations. You can explore more about my background on my Professional Profile and Career Highlights Hub.

So clearly, I have no beef with Google at this point in time, as I both evaluate and implement their products. Moreover, I know people who work or used to work there. Many of them know me, if not personally, then certainly by professional reputation. If that’s the case, why did I choose to write this article instead of emailing one of them? Because I chose to follow Google’s formal documented process…mostly.

Reading Level and Industry Terminology

Heads-up, the rest of this article is written for a different technical level than Part 1 of this series, some of the articles, or my series such as Things Schools Should Actually Teach | The Ultimate Internet Survival Guide. Those articles are written with the average user in mind. Stick with this one, though, as I tried to bridge the gap in understanding while still providing the right information to those at every technical level.

Process Improvement and Educational Value

Google’s process for AdSense review only has a public forum posting. Since they wanted the feedback publicly, I chose to honor that and do a public posting.

School Is in Session | Learn About Technology Topics

Much of my blog is dedicated to providing educational technology topics such as artificial intelligence (AI), cybersecurity, quantum, emerging technologies, and more. Therefore, this article is a natural fit for my career expertise. Although, you will definitely find articles on entertainment, humor, and other topics.

Value-Add | Hunter Storm Official Site Blog Versus Google’s Community Forum

Now, before you misunderstand and think I’m calling Google out, I most certainly am not. The post would have been public in Google’s community forum, and so is this article. So, I chose to honor the spirit of their public forum form, if not the letter.

Now, I wasn’t in the meeting when they decided, but I’d venture to guess Google choose to have people post in a community forum for a few reasons. Most likely so that:

People could get helpful tips from one another’s’ questions.
Google could gather information about any potential bugs so they could develop proactive fixes.

Observant colleagues and industry insiders understand there may be other potential reasons, but I’m going to stick with these for the purposes of this article. Now, back to the Google community forum process.

So, I’ve always followed the process to the letter, and provided several public forum feedback responses regarding bugs I discovered with user-facing products such as Gmail, Google Calendar, components of the Google Webmaster interface, etc.

New Days, New Ways, Better Outcomes

However, choosing to publish my feedback here instead provides many additional benefits:

Educational value for readers because I can delve into the topic at length here, but there would be a limitation on the public Community Forum post.
Ability to link to other, related articles here on my Hunter Storm Official Site to provide additional technical, cybersecurity, and other details to help readers learn more about the topic.
Provides a centralized hub to document the process and dive into technical details because my first article in this series with Yandex was more conversational. That’s partly for the average reader, and partly because I didn’t know anyone who works there at Yandex yet, so I kept the discussion light.
Due to my history with Google, their focus on innovation, as well as their technological breadth and depth, I thought they would appreciate the technical dive we will do here. I was also fairly certain they would understand my rationale for posting here and would clearly understand my helpful intent.
This page will be the perfect spot to provide an update on any changes or non-sensitive communications that may result. I’m hoping to be able to virtually hand out another Doing It Right Award | Recognition for the Unsung Heroes to Google as a result of this post.

Technological alliances and partnerships such as these are a deeply embedded part of my career field; but they are generally conducted in private consultations. However, this is not a sensitive topic, nor will I divulge any confidential information I may or may not have been privy to in my official capacity outside this website.

My website is merely a professional portfolio and research space for me personally, so this article will retain the information classification “public,” although we may use some of my personal “internal use” data regarding this website and the process for educational purposes. It’s a delicate balance, but maintaining information compartmentalization is a crucial part of the cybersecurity discipline. Check back because I will add updates and screenshots as time permits.

Google and Yandex

As a cybersecurity and risk management professional, I am bound to conduct accurate, ethical, thorough evaluations. A while back, I wrote a similar article about my experiences with Yandex. It would be unethical for me to mention Yandex on my blog but give Google a pass. Since trust is paramount in my roles, I am professionally honor-bound to write about this experience.

Algorithmic bias is inherent in every algorithm. When you’ve spent decades creating deeply researched technical documentation and user-focused content that actually helps people navigate the real risks of modern life, like data breaches, identity theft, and the psychological impacts of online manipulation, the last thing you expect is for a search engine to flag your work as “low-value.” And yet, here we are. Algorithmic mislabeling is the bane of modern online life, and I am on a personal mission to make sure the algorithms get it right.

Call to Action for All Search Engines

Don’t think I’m calling out Google. They just happened to be the second search engine I found with this issue, so they made the perfect example for this post. Again, check back to see what happens. I trust Google will do at least as well as Yandex. which truly impressed me with the way they handled the low-value flag.

Anyway, this algorithmic mislabeling could have happened on any search engine. Yes, I see you over there, shuffling your virtual feet in the back row. Yes, you, working at one of the places where the infrastructure is best-in-class. However, it’s impossible to converse with anyone except an automated response system, and there is no support path. Or maybe you work at the place with phenomenal customer service that does respond with actual humans, who even escalate and track tickets, but the tickets get stuck in engineering and never see the light of day again.

Every Rose Has Its Thorn | Tech Challenges Are the Same Everywhere

Every company has its challenges. This is an industry-wide issue related to AI and algorithmic mislabeling, as you can see in How Algorithmic Mislabeling Hides Helpful Content and What We Can Do About It | Part 1 of this series in my recent experience with Yandex. The best we can do it work together to do better next time.

Hunter Storm, “The Consigliere,” Is Here

So, I’m here to be the self-appointed “technology consigliere” and work to broker a level playing field for all, while not interfering in competitive advantage. That means providing a fair and unbiased view of all, with unvarnished feedback. We can paint the metaphorical furniture after we build it together.

Why I Wrote This Article

I wanted this piece to be part of an industry-wide call to action. From my seat as a 31-year technology and cybersecurity veteran, I’ve seen the good, the bad, and the ugly from the inside out. Read more about my adventures in technology in my post, Sympathy for the Devil.

From my perspective, making this article series the best one on algorithmic mislabeling means crafting it to be accessible to most people, not just those of us who devour white papers on our vacations. Since folks at Google know me, then maybe this article can be the catalyst that fixes this problem for other people before it goes too far.

A Heart for Technology and Helping Others

As I said in Part 1 of this series, I can’t tell you how many times I’ve been someplace like a mobile phone store and seen some pitiful person with a phone issue that the store couldn’t fix. Then, I stepped in, and I fixed it for them. Some of them actually followed me on Facebook and showed up at my events later. It was kind of embarrassing because I didn’t even want to tell them who I was. Nevertheless, they were grateful because most people never bother to help. This article is me bothering to help you.

This is the heart of the article. Not the glossary, the search rankings. or even the subtle roast of algorithmic gatekeepers and AI automated monitoring search engine environments. I’m that person who sees someone struggling with their phone in a store, and instead of walking past, I fix it. It’s the digital equivalent of a stranger pulling over to help change a tire in the rain (I’ve actually done this, but it was in the snow), with expert-level skills and zero expectation of anything in return.

That’s what makes this article different. It isn’t clickbait. It’s not content marketing. It’s a mission statement wrapped in a tutorial. So yeah, if Google and other search engine providers are reading my site now, I want to make sure what they’re seeing is worth watching. I’m not just building an article. I’m building a beacon, one that guides lost users, frustrated developers, and confused content creators back to clarity. I’ve done this for years behind the scenes, helping strangers without fanfare. This article is the lighthouse version of that help. This time, it is on the record, in public, and optimized for everyone who’s ever been told their work or questions didn’t matter. So, let’s bring it fully into the light, no silent suffering, and definitely no more getting flagged for trying to help.

What You’ll Get from This Article

If you’ve ever wondered why your in-depth, helpful, and even life-saving content seems to vanish into the void, this article is for you. Whether you’re developer, systems administrator, content creator, digital strategist, business owner, stakeholder, or simply someone trying to find answers online, this guide breaks it all down:

- How algorithms actually evaluate content, and why they often get it wrong
- A real-world case study of how helpful, human-centered content was flagged as “low-value”
- Why this affects everyone, not just business owners, creators
- A comprehensive, alphabetized glossary of algorithmic and search engine optimization (SEO) -related terms
- A checklist to help creators, users, and developers identify and respond to algorithmic misfires
- A call to action: how we can fix this system together

This isn’t a rant, it’s a resource. Because when good content gets hidden and demonetized, everyone loses.

Flagged by the AI Algorithm

Recently, Google’s algorithm flagged my website under this designation. While frustrating, this moment offers an opportunity to explore something far more important than rankings: how machines evaluate content, why some genuinely helpful resources fall through the cracks, and what we, as creators and users, can do about it. I’ve worked at the intersection of cybersecurity and user education for nearly two decades, advising on cybersecurity risk, legal and regulatory compliance, AI trust and safety, the ethical use of technology, and much more. My work has been featured across advisory boards, national forums, and Fortune 100 companies.

So, when the algorithms flagged my entire website as “low-value,” it raised important questions. Not only questions about my content, but about how these systems evaluate “value” in the first place.

Case in Point | The Flag That Sparked This Piece

I’ve been writing detailed, human-focused pieces for years. This has been my version of digital volunteer work to help people stay safe online using my career expertise to help end users who would never get to interact with someone in my line of work. Recently, three of my articles were flagged by Yandex as “low-value”:

The Ultimate Guide to Safeguarding Your Identity After a Data Breach
The Hidden Dangers of the Internet (from my Things School Should Actually Teach series)
Hunter Storming is the New Rickrolling (a layered, satirical piece combining culture, humor, and digital strategy)

Each one was comprehensive, helpful, and filled with real-world insights. And yet, the algorithm decided they weren’t worth showing to users.

Why This Affects Everyone

This isn’t just about me, or about Google. It’s about what happens when machines dictate visibility in human systems. If you’re a:

Developer: You need to know how your scoring models may penalize nuance.
Systems Administrator: You want your internal content and KBs to surface relevant answers.
End User: You deserve to access accurate, understandable, and useful information, not whatever happens to check the algorithm’s boxes.

The long-term impact? Content that doesn’t pander to the algorithm dies in the dark. Articles that help people through data breaches, identity theft, career issues, risk management, or trust and safety never get seen. And users suffer for it.

Checklist | Spotting and Surviving Algorithmic Mislabeling

If you’re creating content, managing systems, or just trying to understand why certain things don’t show up in search, this checklist is for you.

Content Signals That May Trigger Algorithmic Mislabeling:

Deeply educational content
Content that has not undergone keyword optimization
Humorous or satirical tone
No clickbait hook or teaser
Discusses uncomfortable truths or critical thinking topics (cybersecurity, privacy, manipulation, fraud, ethical gray areas, etc.)
Uses coined terms or unique cultural references

Technical and SEO Flags That Can Work Against You:

Low backlink volume (especially for niche topics)
High bounce rate (often because the article answers the question quickly!)
Not formatted in Accelerated Mobile Pages (AMP) or optimized for mobile layout
Long paragraphs or dense info that natural language processing (NLP) systems don’t parse easily

How to Prevent Algorithmic Mislabeling:

Use schema markup and metadata to reinforce clarity
Add subheadings to aid NLP parsing
Include definitions or context for coined terms or layered jokes
Maintain your tone, but back it up with traditional trust signals (citations, references, headers)
Politely request evaluation, just know the answer might be, “We’ll use your work for training instead.”

A Call to Build Something Better

Algorithmic mislabeling problems probably aren’t going away, but they can be improved. If you’re building systems that classify content, rank information, or surface results, you need human insight to make those tools serve the public. Especially when it comes to safety, fraud prevention, health, and educational equity. As someone who’s worked in cybersecurity, AI, risk management, hybrid threat detection, and content design for over 20 years, I’ve seen the patterns. I know how systems fail people, and how to fix them.

To Search Engines, AI Devs, and Tech Teams Everywhere:

If you want your ranking models to better reflect human needs and not just metrics, I’m available for consultation. Let’s stop burying the content that helps the most.

Why I Bother (And Why You Might Too)

Let me be honest: I didn’t write this because of a search label due to algorithmic mislabeling. In fact, I had no emotion over the label, only curiosity. Instead, I wrote this because I’ve spent decades quietly helping people who didn’t know who else to ask.

The grandmother locked out of her email after a phishing scam.
The dad trying to fix his kid’s iPad during a meltdown at the mobile store.
The single mom who didn’t understand why her Instagram was hacked but was too embarrassed to ask anyone.
The aspiring writer whose blog stopped getting traffic and thought they had done something wrong.

I’ve helped global enterprises, charitable organizations, family, friends, and even strangers fix things behind the scenes, phones, accounts, security settings, even reputations. They were always surprised someone would take the time. I never wanted credit. But now, it’s time to put the help in writing, for everyone who can’t ask in person.

This is my way of saying: I see you. And you’re not foolish for feeling like the system doesn’t work the way it should. You’re right. It doesn’t. And now we’re going to fix it, together.

How to Share, Use, and Build on This Resource

If this article resonated with you, the following sections will give you actionable steps on what you can do. Need to prevent algorithmic mislabeling? Take the steps below.

Content Creators:

Use the checklist to review your own articles.
Add schema, clarify your structure, and maintain your tone.
Link to this article and share it everywhere to help explain the problems with algorithms hiding content.

Developers & Engineers:

Share this internally with ranking, NLP, or trust and safety teams.
Rethink how you measure “value” and who you might be excluding.
Reach out to me if you want direct input. I consult for teams solving real-world AI and algorithmic mislabeling problems, as well as AI, cybersecurity, policy, legal and regulatory challenges, and more.

Curious Humans:

Bookmark this article.
Use the glossary to understand the jargon.
Share it with a friend who’s struggling with invisibility, online or off.

This isn’t just a resource. It’s a declaration:

Helpful content shouldn’t be punished for being human.
People who try to help others shouldn’t be buried by bots.

Glossary | Algorithmic Mislabeling, Content Labeling, & Visibility

This A to Z Glossary is built for developers, sysadmins, content creators, and the quietly overwhelmed users who just want to understand what the heck happened to the internet. It’s designed to be the most useful, educational, and complete reference on the topic.

Algorithm: A set of rules or calculations used by machines to make decisions, like ranking web pages or suggesting content. Not inherently intelligent, just fast and pattern-hungry.

Algorithmic Bias: When an algorithm makes skewed or unfair decisions due to flaws in the training data, developer assumptions, or systemic inequities. Often invisible until real people suffer the consequences.

Algorithmic Mislabeling: When content is incorrectly flagged (e.g., as spam, low value, irrelevant) due to bot limitations or faulty logic. This is also known as systemic mislabeling, inaccurate labeling, etc.

AMP (Accelerated Mobile Pages): A stripped-down HTML format designed for faster loading on mobile devices. Not always friendly to longform or interactive content.

B Backlinks: Links from one site to another. Seen as a measure of credibility by most search engines. Fewer backlinks mean lower trust scores, even if your content is excellent.

Bounce Rate: The percentage of users who leave a page without clicking further. Ironically penalizes articles that answer the user’s question efficiently.

Bot: An automated script or software agent that crawls, indexes, or interacts with web content. Sometimes helpful, often confused.

Canonical Tag: A bit of HTML that tells search engines which version of a page to treat as the original to avoid duplicate content penalties.

Clickbait: Sensationalized content designed to attract clicks rather than provide value. Ironically, often rewarded by engagement-hungry algorithms.

Content Farm: A site that mass-produces low-cost articles designed to game search engines rather than inform readers.

Crawler (or Spider): The part of a search engine that “reads” websites to index and evaluate them. Limited in understanding nuance or context.

Data Breach: A security incident where sensitive information is exposed or stolen. Usually affects large groups, often leads to identity theft.

De-Indexed: When a page is removed from search engine results. Can happen intentionally (by the creator) or algorithmically (without notice).

DNX (“Digitally excommunicated”): A satirical term for when a search engine effectively makes your work invisible without directly banning it.

Engagement Metrics: Data points like clicks, time on page, and social shares that algorithms use to judge content “equality.” Often gamed or misunderstood.

Expertise, Authoritativeness, Trustworthiness (E-A-T): Google’s framework for evaluating content creators and websites. Easy to name, hard to quantify.

Featured Snippet: A highlighted summary of an answer at the top of search results. Can massively boost visibility, or steal clicks from the original article.

Filter Bubble: A digital environment where algorithms tailor what you see based on past behavior, limiting exposure to diverse content or viewpoints.

Google: Largest search engine in the world.

Googlebot: Google’s proprietary crawler, tasked with reading and indexing the internet. Also notoriously opaque about how it ranks pages.

Hidden Content: Text or links that are not visible to users but exist in the code, sometimes used for manipulation, sometimes for formatting. Search engines may penalize this if abused.

Human-Centered Design: An approach to systems that prioritizes usability and real-world needs over purely technical solutions. Often at odds with automated ranking systems.

Indexing: The process of storing and organizing content so it can appear in search results. Just because something is online doesn’t mean it’s indexed.

Intent (Search Intent): The reason behind a user’s query. Algorithms try to infer this but often misinterpret nuance, especially in longform or unconventional content.

JavaScript Rendering: When dynamic content is created or changed via JavaScript. Some crawlers don’t fully render JS, leading to incomplete indexing.

Keyword Stuffing: Overusing targeted terms in an attempt to manipulate search rankings. Penalized now, though still seen in low-quality sites.

Link Juice: A slang term for the value passed from one site to another through backlinks. A key part of how algorithms share credibility.

Low Value Page: A designation (used by some engines like Yandex) for content that appears to lack usefulness or depth, often a result of flawed evaluation metrics.

Metadata: Data that describes other data. In SEO, includes title tags, meta descriptions, and structured info that helps bots understand a page.

NLP (Natural Language Processing): How machines attempt to “understand” human language. Powerful, but often tone-deaf to satire, layered meaning, or cultural references.

Organic Results: Unpaid search engine listings, as opposed to ads. Where most helpful content wants to live, but the competition is brutal.

Pogo-Sticking: When users quickly click into a result and bounce back to search results. The system can misread this as a sign of low value, even if the page gave them what they needed fast.

Query Matching: How search engines compare user input to indexed content. Literal matching may miss intent or context, leading to inferior results.

Ranking Factors: The signals (over 200, in Google’s case) that influence how search engines rank a page. Some are known, many are secret, most are evolving.

Rich Snippet: Enhanced search results with extra info like star ratings, prices, or event details, driven by structured data or schema markup.

S Schema Markup: A language of tags added to HTML to help search engines better understand a page’s content. Helps clarify intent and structure.

Search Visibility: A metric showing how likely your content is to appear in search results. Can drop drastically with algorithmic changes or penalties.

SERP (Search Engine Results Page): The list of results shown for a given query. Where the battle for visibility is won or lost.

Thin Content: Pages with little useful information or substance. Often used to justify “low-value” flags, but subjective in practice.

Trust Signals: Elements like author bios, citations, SSL, and backlinks that suggest a page is credible. Not always aligned with actual usefulness.

UX (User Experience): The overall feel of a site or system. Search engines now use UX signals (like mobile usability or Core Web Vitals) as part of rankings.

Visibility Penalty: An unofficial term for what happens when an algorithm quietly demotes your content, even if you’ve done nothing “wrong.”

Web Crawler: Another term for a bot or spider that indexes content across the web. Different search engines use different crawlers with different rules.

XML Sitemap: A file that tells search engines what your website structure is, as well as which pages are important. Helps with crawling and indexing.

Yandex: A Russian-based search engine with its own crawler, index, and page evaluation model. Recently became part of this article’s origin story.

Zero-Click Search: When a user finds the answer to their query directly on the SERP, without clicking any links. Good for users, bad for creators.

Final Thoughts | To the Gatekeepers (Human or Machine)

If you’ve made it this far, maybe you’re from Yandex, Bing, Google, or some other search engine team. Maybe you’re a policy advisor, an engineer, or someone monitoring my site for “patterns.” Good. Welcome. You’ve already decided my site is valuable enough to train your models on, so I hope you’ll now also consider this message as part of that training. When:

Algorithms punish people for being helpful, the system is broken.
The rules favor repetition over insight, the web becomes hollow.
Humor, depth, and humanity are flagged as flaws, we all lose.

As someone who’s worked across cybersecurity, content strategy, and AI training, I’ve spent years studying the very patterns and oversights that lead to this kind of mislabeling. If you’re building algorithms to assess content value, especially when it comes to user safety, digital literacy, or threat mitigation, I’m available for consultation. Let’s make search better for everyone.

An Open Offer to Google, All Other Search Engine Providers, and Any Organization That Uses AI and Algorithms

I’ll gladly help your algorithm learn, one “low-value” flag at a time. Something tells me they may actually read this post and take me up on this offer. Why am I offering consultation? Because I want the next creator to be heard instead of hidden. Let’s fix this together before the system buries too many more voices due to algorithmic mislabeling. Let’s build systems that see people, not just patterns. And let’s make sure the next person who bothers to contribute to the digital ecosystem is seen because they cared, not in spite of it.

Hunter Storm | High-Value Voice

Until then, I’ll be over here writing high quality content that humans appreciate, even if your AI, algorithms, and bots think I’m throwing digital confetti into the void.

Good News | Helpful Humans at Yandex Fixed the AI Algorithmic Mislabeling

The morning after the email exchange in Part 1 of this post, Yandex restored two of the articles. Yandex corrected the third article the next day. Whether this shift occurred through internal escalation, quiet oversight, or resonance from the message itself, I acknowledge the movement, and I respect the possibility of change. This is an excellent example of a company, or maybe just one person, getting it right. Taking the time to review a flag, then taking the action to remove it. In the greater scheme of things, people outside technology might not understand the significance of what may appear to be a small action, but these actions have major ripples under the surface in our world.

Let’s see if Google meets or beats their record, or if this helpful and well-intentioned article sits here in silence.

Doing It Right Award

I saluted the fine people who fixed my issues at Yandex with my unofficial Doing It Right Award. To those working quietly behind the scenes (at Google and elsewhere): I see you. And I appreciate both you and the integrity it takes to revisit a decision. This isn’t a victory. It’s a moment of alignment, and a reminder that we’re not adversaries. Sometimes it just takes a whisper in the right frequency to recalibrate the entire system.

Discover More from Hunter Storm

Enjoy this article on algorithmic mislabeling? Want to learn more about search engines? Interested in diving deeper into the workings of the World Wide Web? Enjoy learning about Internet history? Then check out some of my other articles:

Hunter Storm ™

How Algorithmic Mislabeling Hides Helpful Content and What We Can Do About It (Part 2 | Google)