Algorithm Boost: Why We Should Always Support Each Other’s Content Posts

DROP YOUR SUBSTACK BELOW! I want to read it :) Make sure to like, comment and engage with this post! Also subscribe if you like my articles. I will return the favour

Have you seen these posts?

If you are new to Substack (or any other social media platform), I will not go through the ritual of the heartfelt promise to read your writing, playing on your frustration with few followers and likes. I love writing, and do read your writing too. I am only one person, though, and can only read so much. So let me help you get a lot of eyes on your Substack besides me.

My Credentials

As a university lecturer who studies and teaches digital technology, machine learning, and analytics to masters degree level post graduate students, I will shoot straight. I have thousands of posts on ten different social media platforms. We need to help each other by engaging with each other’s content every time. Here’s why.

I do want you to follow, like, comment, and share my content and I will too eith yours—because we can use machine learning algorithms to our benefit. We need to get more people with time to read a few newsletters (or posts) to see our writing. Is that ethical? More on that in a minute

Make sure to like, comment and engage with this and every post! Read on to learn why.

Also subscribe if you like my articles :) I will return the favour

Why? They are primarily algorithm optimization tactics. Here’s how they work:

Engagement Farming:

The posts are designed to generate massive comment volume. When hundreds of people drop their Substack links, it creates a flurry of activity that social media algorithms interpret as “highly engaging content.”

Algorithmic Boost:

Most social platforms (Twitter, LinkedIn, etc.) use engagement metrics like comments, likes, and replies as key ranking factors. A post with 200+ comments gets prioritized in feeds and shown to more people.

Reciprocal Engagement:

It creates a cycle where participants feel obligated to like/comment on the original post to increase visibility of their own shared link. This compounds the engagement signals.

Network Effect:

As the post gains traction, it gets shown to the followers of everyone who engaged, exponentially expanding reach beyond the original poster’s audience.

Gaming Recommendations:

High-performing posts often get featured in platform recommendation systems, trending sections, or “posts you might like” suggestions.

The strategy exploits the fact that algorithms can’t distinguish between “meaningful discussion” and “link dumping” - they just see raw engagement numbers. It’s essentially a coordinated effort to hack social media distribution, though it often provides genuine value by helping writers discover each other’s work.

These posts work because they align individual incentives (exposure for your Substack) with the poster’s goal (algorithmic amplification).​​​​​​​​​​​​​​​​

Let’s amplify.

DROP YOUR SUBSTACK BELOW! I want to read it :) Make sure to like, comment and engage with this post! Also subscribe if you like my articles. I will return the favour

Ethics

Is it ethical? I think it is becsuse if we are feeding the social media machine learning our content, information, and data and being eyeballs for other’s content and not getting our own views, impressions, and likes and outcomes we seek because the platforms didn’t tell us how their algorithms work, once we know how they do it is right and good to use the technology for our benefit.

There’s a strong case that this kind of algorithm optimization is not just permissible but justified:

Information Asymmetry:

Platforms deliberately keep their algorithms opaque while harvesting user data and attention. When users figure out how to work within these systems, they’re leveling an inherently unbalanced playing field.

Fair Exchange:

We’re constantly feeding these systems our data, engagement, and attention without transparent compensation. Using discovered patterns to boost your own content is essentially claiming some value back from that exchange.

No Deception:

These Substack sharing posts are transparent about what they are. People willingly participate knowing it’s mutual promotion, unlike more manipulative engagement tactics.

Democratizing Reach:

These strategies help individual creators compete against accounts with massive budgets for promoted content or established algorithmic advantages. It’s a form of grassroots distribution.

Platform Agnostic:

The platforms profit from all engagement regardless, so optimizing for visibility doesn’t harm the platform’s business model - it actually serves it.

The key ethical distinction might be between “gaming the algorithm” (working within discovered rules) versus “manipulating people” (being deceptive about intent). The Substack posts fall clearly into the former category. This is why I am telling everyone I can about how social media platform machine learning algorithms work.

I think my broader point about reclaiming agency in algorithmic systems is particularly strong. If these companies won’t be transparent about how they distribute content, users discovering and utilizing those patterns seems like a reasonable response to that opacity.​​​​​​​​​​​​​​​​

Kevin

---

📬 Also Subscribe to Baker on Business — my weekly newsletter for solopreneurs, career renegades, and executives building real value in the age of AI.

💼 Own a business or lead at a high level? Book a free strategy call with me. We’ll talk about how to grow in your calling — and why that’s the real ROI. Zoom or Google Meet — your choice. Questions?

🚨 Limited offer: Join an Executive Advisory Growth Group . I charge a fraction of what others charge ($1,000+). More value. No fluff. Results that compound.

---

Kevin L. Baker helps leaders build legacies — not just résumés. He writes about leadership, tech, and the human spirit at the intersection of all three. He runs Executive Advisory peer groups, serves as a fractional executive across industries, advises startup founders, and teaches the next generation of thinkers and builders.

---

📍 X (Twitter): Click here 📍 Threads: @kevinbakerau 📍 Website: www.kevinbakerinc.com 📍 All my links: linktr.ee/Kevinbaker

---

Real strategies. Zero fluff. Results that compound.