Case Study: Precision YouTube Discovery—Finding Consistent "Macro" Creators
By khoanc, at: Feb. 23, 2026, 7 p.m.
Estimated Reading Time: __READING_TIME__ minutes
YouTube is the "long-game" of social media. While other platforms thrive on fleeting moments, YouTube rewards sustained, predictable output.
This case study explores a robust automation pipeline we developed to identify YouTube channels that have achieved "Macro" status and maintain a high-frequency publishing schedule.
Our mission: Identify channels with 200,000+ subscribers that adhere to a strict weekly posting schedule.
The Challenge: Parsing Consistency in a Sea of Content
Identifying large channels is straightforward, but verifying "consistency" is technically complex. YouTube's data often presents dates in relative formats (e.g., "3 weeks ago"), which are difficult to analyze programmatically. To solve this, Glinteco built a logic engine that translates relative time into linear data to detect gaps in a creator's upload history.
The Solution: A Two-Step Verification Pipeline
We utilized a combination of the streamers/youtube-scraper and streamers/youtube-channel-scraper to create a filtered funnel. This ensures we only spend resources deep-scraping channels that already pass the initial size requirement.
Step 1: Broad Search and Size Filtering
First, we cast a wide net across specific niches. We pull basic channel metadata and apply a parsing function to normalize subscriber counts (converting "200K" or "1.5M" into pure integers).
# Glinteco Logic: Normalizing Subscriber Strings
def parse_subscribers(sub_value):
sub_str = str(sub_value).upper().replace(",", "").strip()
if "M" in sub_str:
return int(float(sub_str.replace("M", "")) * 1_000_000)
elif "K" in sub_str:
return int(float(sub_str.replace("K", "")) * 1_000)
return int(sub_str)
Step 2: Temporal Analysis for Weekly Consistency
Once we have a shortlist of channels with 200k+ subscribers, we perform a deep dive into their video and "Shorts" history. The script converts upload dates into ISO Week Numbers. A channel is only "qualified" if the gap between active weeks is minimal (defined by our WEEKLY_TOLERANCE_GAP).
# Glinteco Logic: Detecting Gaps in Weekly Posting
def is_weekly_poster(videos):
active_weeks = set()
for vid in videos:
dt = parse_relative_date(vid.get("date"))
if dt:
year, week, _ = dt.isocalendar()
active_weeks.add((year, week))
# Sort and check for linear gaps
sorted_weeks = sorted(list(active_weeks), reverse=True)
# If the gap between week X and week Y > 1, the streak is broken
return check_gaps(sorted_weeks)
The Results: High-Value Creator Intelligence
By automating the date parsing and linear gap analysis at Glinteco, we transformed a week-long manual vetting process into a 10-minute automated run.
Why Weekly Consistency Matters
For brands, a weekly poster represents a reliable "content heartbeat." These creators have predictable audience peaks, making them the ideal partners for time-sensitive product launches or sponsored integrations. Our script ensures that our clients' marketing budgets are never spent on "fading" channels that have stopped regular production.
