Social Search Is Here: A Practical AI Workflow to Find Winning Short-Form Content (Without Guessing)

If your content strategy still starts with “let’s brainstorm 20 hooks,” you’re working too hard.

Short-form platforms are no longer just distribution—they’re discovery engines. People search inside TikTok and Instagram the same way they used to search Google, and brands are reacting by shifting effort toward creator-style distribution and faster iteration [1]. Meanwhile, video continues to be one of the most adopted marketing formats across businesses [2], and short-form publishing volume keeps rising—which makes “copy what went viral once” a losing strategy [3].

So what does work?

A repeatable research workflow that turns messy internet content into structured patterns you can execute. That’s exactly what conthunt is built for: search → collect → analyze → cluster → extract patterns → produce briefs.

Below is a practical workflow you can run every week—whether you’re a solo creator or a team running multiple brands.

The Problem: “Content Ideas” Don’t Scale. Systems Do.

Most teams fall into one of these traps:

1. Inspiration scrolling (hours lost, zero structure)
2. Vanity benchmarking (copy big creators; miss why it works)
3. Spreadsheet chaos (links everywhere, no insights)
4. Trend chasing (too late, too saturated)

The fix is a system that answers four questions fast:

• What is working right now in my niche?
• Which patterns repeat across different creators?
• What makes viewers stay (structure, edits, pacing)?
• How do I turn patterns into briefs my team can produce?

The conthunt Workflow (Weekly)

Step 1: Start With “Search Buckets,” Not Random Keywords

Instead of one keyword like “skincare,” create search buckets that represent viewer intent.

Example buckets for a skincare creator:

• “how to get rid of acne scars”
• “dark spots hyperpigmentation routine”
• “retinol purge vs breakout”
• “glass skin routine”
• “dermatologist reacts”

Example buckets for a SaaS tool:

• “how I automate content”
• “best ai tools for marketing”
• “content calendar template”
• “reels hooks that work”
• “how I grew to 10k followers”

Goal: 10–20 buckets that reflect what people want, not what you sell.

Pro tip: if a bucket sounds like a YouTube title, it’s usually a good bucket.

Step 2: Pull a “Discovery Set” (20–50 Videos per Bucket)

For each bucket:

• Run a search in conthunt.
• Save top results into a Board (one board per bucket, or group buckets into one “Weekly Research” board).
• Tag quickly:
  • format (talking head, montage, screen recording)
  • creator type (expert, influencer, brand, meme account)
  • angle (tutorial, reaction, storytime, teardown)

You’re building a dataset, not watching videos for fun.

Step 3: Run Video Analysis on the Winners

Now use the platform like a lab.

Pick the top 10–20 videos from your discovery set (the ones with obvious traction, strong hooks, or repeating comments). Run video analysis to extract:

• the hook (first 1–3 seconds)
• structure (setup → proof → steps → payoff)
• pacing/edits (cuts, captions, overlays, B-roll)
• CTA style (comment bait, save/share prompt, follow for part 2)
• key claims (what’s being promised)

This is where AI shines: it reduces “I think this is good” to explicit components you can reuse.

Step 4: Cluster Into Patterns (Not “Trends”)

Trends expire. Patterns repeat.

Create clusters like:

• Myth-busting (“Stop doing X. Do this instead.”)
• Before/after proof (fast visual payoff)
• 3-step frameworks (“Do these 3 things…”)
• Expert reaction (“Dermatologist reacts to…”)
• Relatable pain (“If your skin does this…”)
• Tool teardown (“I tested X so you don’t have to.”)

Then score each cluster with:

• frequency (how often it appears)
• freshness (how recently top videos were posted)
• variety (does it work across many creators, or just one big account?)
• production cost (easy vs heavy editing)

Your output is a Pattern Board—a collection of clusters with examples.

Step 5: Extract the “Creative DNA” of Each Pattern

For each cluster, write a mini spec:

Pattern name: Myth-busting teardown
Viewer promise: “You’ve been doing it wrong; here’s the fix.”
Hook formulas:

• “If you’re doing X, stop.”
• “This is why your Y isn’t working.”
• “The internet lied about Z.”

Structure template:

1. Contrarian claim
2. Quick evidence (demo, screenshot, stat, personal result)
3. The correct method (2–4 steps)
4. Simple CTA (“Save this,” “Comment ‘routine’”)

Production notes:

• tight captions
• cut every 1–1.5 seconds
• show proof early (first 5 seconds)

This is the difference between content that “feels inspired” and content that’s engineered.

A Real Example: Turning Research Into 5 Script Briefs

Let’s say you’re researching “hyperpigmentation routine.”

After clustering, you find 3 repeating patterns:

1. Dermatologist reacts
2. 3-step routine with proof
3. Myth-busting ingredient order

Now produce 5 briefs:

Brief 1 — Myth-busting

Hook: “Your niacinamide isn’t helping because you’re layering it wrong.”
Beat 1: show common layering mistake
Beat 2: corrected order (AM + PM)
Beat 3: “Do this for 14 days”
CTA: “Comment ‘ORDER’ and I’ll send the routine.”

Brief 2 — Proof first

Hook: “Dark spots in 30 days—here’s what actually changed.”
Beat 1: before/after
Beat 2: 2 product categories (not brands)
Beat 3: 1 lifestyle factor
CTA: “Save this.”

Brief 3 — Expert reaction

Hook: “Dermatologist reacts to the ‘lemon on face’ trend.”
Beat 1: stitch reaction
Beat 2: why it’s harmful
Beat 3: safe alternative
CTA: “Drop the next trend to react to.”

Brief 4 — “What I stopped doing”

Hook: “I stopped doing this one thing and my pigmentation faded.”
Beat 1: reveal habit (over-exfoliating)
Beat 2: replacement routine
Beat 3: timeline expectations
CTA: “Follow for part 2.”

Brief 5 — Comment-driven

Hook: “If you have pigmentation + oily skin, don’t follow dry-skin routines.”
Beat 1: identify wrong routine
Beat 2: correct routine
Beat 3: list product types
CTA: “Comment your skin type.”

Notice: none of these require “genius ideas.” They’re assembled from repeatable patterns.

How to Measure If Your Research System Works

A research system should improve:

• Speed: fewer hours to pick ideas
• Consistency: less randomness; more repeatable formats
• Iteration: you can test 10 hooks quickly
• Learning: every week you build a better library

Use these simple metrics:

• Hook retention proxy: views in first hour / follower count
• Saves and shares (best indicator for evergreen how-to)
• Comment quality (questions, personal stories, “this helped”)
• Cluster hit rate: how many posts per cluster outperform baseline

Your “Do This Every Monday” Checklist

1. Pick 10–20 search buckets
2. Pull 200–500 candidate videos total
3. Analyze top 20–40
4. Cluster into 5–10 patterns
5. Write 10 briefs (2 per pattern)
6. Ship content; collect results
7. Feed results back into next week’s research

That’s the loop.

Final Thought: Your Advantage Is Not “More Content.” It’s Better Research.

Most creators and teams are producing content with weak inputs. Strong inputs come from structure: searchable libraries, pattern recognition, and briefs that reduce guesswork.

The platforms keep changing. But patterns—human attention patterns—don’t.

Build the system once. Run it weekly. Compound forever.

References

[1] Social search behavior and shifting discovery patterns among younger audiences.
[2] Broad adoption of video as a marketing tool among businesses.
[3] Short-form video performance and publishing volume insights from large-scale analysis.