Case Study: Automating Influencer Discovery for High-Engagement Brands

At Glinteco, we receive a high volume of requests for custom social media scraping solutions. Clients often find that while social platforms are vast, the data is "noisy". They don’t just need a list of users; they need a list of active, high-value creators who meet specific performance thresholds.

This case study highlights a common request: finding the "Active Middle" on Instagram, users with a solid baseline of 5,000 followers who maintain a daily posting schedule.

The Challenge: The Search for Consistency

For many of our clients, a large follower count is meaningless if the account is dormant. Manual discovery is a bottleneck:

The Problem: Finding 10 active creators might take 2 hours of manual scrolling.
The Goal: Automate the identification of users who are currently "trending" and highly engaged.
The Criteria: 5,000+ followers and content published within the last 24 hours.

The Solution: Python + Apify SDK

To solve this at scale, we utilize the Apify Python SDK. This allows us to trigger the apify/instagram-scraper and immediately pipe the data into a custom filtering engine.

1. Programmatic Extraction

Instead of using a dashboard, we initiate the scrape via the apify-client. This allows us to target specific hashtags or geographical locations to find our initial pool of candidates.

from apify_client import ApifyClient from datetime import datetime, timedelta

client = ApifyClient("YOUR_API_TOKEN")

# Targeting high-intent fitness hashtags run_input = { "hashtags": ["wellnessjourney", "fitfluencer"], "resultsLimit": 500, }

# Run the actor run = client.actor("apify/instagram-scraper").call(run_input=run_input)

2. The Glinteco "Active Filter"

Once the raw data is retrieved, we apply a logic layer to isolate creators who hit our client's exact requirements. This is where we separate the "ghost accounts" from the daily posters.

# Setup our time window (24 hours) cutoff_time = datetime.now() - timedelta(days=1) verified_leads = []

for item in client.dataset(run["defaultDatasetId"]).iterate_items(): followers = item.get("followersCount", 0) last_post_ts = item.get("latestPostTimestamp") if last_post_ts: last_post_date = datetime.fromtimestamp(last_post_ts) # Applying the 5k follower and 24h activity rule if followers >= 5000 and last_post_date >= cutoff_time: verified_leads.append({ "username": item.get("username"), "followers": followers, "status": "Daily Poster" })

Why This Works

By filtering for daily posters, we ensure that our clients are reaching out to creators who are currently in "production mode." These users are more likely to check their notifications, engage with brand inquiries, and have a community that is actively watching their stories and posts.