Real-Time TikTok Trend Monitoring Developer
Budget: $1,500 – $3,000 USD
TikTok Scraper & Monitoring Tool – Project Scope
We are hiring a developer or team to build a TikTok scraping and monitoring tool. The system should track TikTok sounds, videos, and user profiles in near real-time. The goal is to identify and monitor high-growth TikTok trends as early as possible.
Core Requirements:
Scraper & Data Collection:
Scrape TikTok sound metadata: sound name, creator, total usage, original and most recent post date
Scrape video metadata: post ID, user ID, username, video link, profile link, views, likes, comments, saves, shares, post date
Track new sound IDs and user accounts automatically based on video content
Monitor user profiles that interact with tracked sounds and log their new uploads
Discovery Sources (Seed Sounds):
Pull initial sounds from:
TikTok Creative Center (U.S. region)
TikTok Viral 50 Chart (U.S.)
TokHits.com daily and weekly trending charts
Monitoring Logic:
Monitor tracked sounds and user profiles every 10–15 minutes
Detect new videos and engagement patterns
Automatically add new sound IDs and users based on observed usage
Avoid duplicate processing of sounds, users, and videos
Trend Detection and Scoring:
Score sounds based on rapid increases in engagement or usage
Detect high-engagement videos using low-use sounds
Flag when 3+ videos use the same sound in under 30 minutes
Prioritize trends influenced by high-reach users
Hashtag & FYP Discovery:
Scrape videos from TikTok hashtags such as #fyp, #viral, #musicdiscovery, #popmusic, #singersongwriter
Extract and monitor new Sound IDs from those videos
Dashboard Requirements:
Simple table layout like TokHits.com showing sortable sound rankings
Display: sound name, artist, usage count, score, trend direction, growth history
Filters: time window (24h, 7d), region (U.S.), sound type
Option for detail view per sound with: time-series chart, hashtags, country data
Export current view as CSV or Excel
Performance and Infrastructure:
Use Playwright or Puppeteer for JS-rendered scraping
Integrate CAPTCHA solving (e.g. 2Captcha, CapMonster)
Support proxy rotation (residential, datacenter, or rotating)
Enable async or multithreaded scraping
Implement retry logic and logging for failed requests
Data Storage:
Maintain databases for sounds, users, and videos with time-stamped metrics
Track usage velocity and engagement ratios over time
Automatically prevent duplicate entries
Provide daily CSV/Excel report of top trending sounds
Alerts:
Trigger real-time alerts (log/email/webhook) when:
A sound grows rapidly
A tracked user uploads a high-engagement video
A sound clusters in multiple new videos quickly
MVP Success Criteria:
Seed scraping works and refreshes automatically
Discovery expands database based on usage
Scoring logic identifies and surfaces top-growing sounds
UI/dashboard displays real-time ranked sounds with filters
Export and alert features work
CAPTCHA and proxy solutions are functional
Deliverables:
Fully working scraping + monitoring tool
Source code with documentation
Basic UI for viewing and exporting data
Setup instructions
To Qualify:
Must have experience scraping dynamic, JavaScript-rendered websites
Should be familiar with Playwright, Puppeteer, proxies, and CAPTCHA handling
Must include examples of past scraping/monitoring tools
Should provide an estimated timeline and cost for delivering an MVP
We are hiring a developer or team to build a TikTok scraping and monitoring tool. The system should track TikTok sounds, videos, and user profiles in near real-time. The goal is to identify and monitor high-growth TikTok trends as early as possible.
Core Requirements:
Scraper & Data Collection:
Scrape TikTok sound metadata: sound name, creator, total usage, original and most recent post date
Scrape video metadata: post ID, user ID, username, video link, profile link, views, likes, comments, saves, shares, post date
Track new sound IDs and user accounts automatically based on video content
Monitor user profiles that interact with tracked sounds and log their new uploads
Discovery Sources (Seed Sounds):
Pull initial sounds from:
TikTok Creative Center (U.S. region)
TikTok Viral 50 Chart (U.S.)
TokHits.com daily and weekly trending charts
Monitoring Logic:
Monitor tracked sounds and user profiles every 10–15 minutes
Detect new videos and engagement patterns
Automatically add new sound IDs and users based on observed usage
Avoid duplicate processing of sounds, users, and videos
Trend Detection and Scoring:
Score sounds based on rapid increases in engagement or usage
Detect high-engagement videos using low-use sounds
Flag when 3+ videos use the same sound in under 30 minutes
Prioritize trends influenced by high-reach users
Hashtag & FYP Discovery:
Scrape videos from TikTok hashtags such as #fyp, #viral, #musicdiscovery, #popmusic, #singersongwriter
Extract and monitor new Sound IDs from those videos
Dashboard Requirements:
Simple table layout like TokHits.com showing sortable sound rankings
Display: sound name, artist, usage count, score, trend direction, growth history
Filters: time window (24h, 7d), region (U.S.), sound type
Option for detail view per sound with: time-series chart, hashtags, country data
Export current view as CSV or Excel
Performance and Infrastructure:
Use Playwright or Puppeteer for JS-rendered scraping
Integrate CAPTCHA solving (e.g. 2Captcha, CapMonster)
Support proxy rotation (residential, datacenter, or rotating)
Enable async or multithreaded scraping
Implement retry logic and logging for failed requests
Data Storage:
Maintain databases for sounds, users, and videos with time-stamped metrics
Track usage velocity and engagement ratios over time
Automatically prevent duplicate entries
Provide daily CSV/Excel report of top trending sounds
Alerts:
Trigger real-time alerts (log/email/webhook) when:
A sound grows rapidly
A tracked user uploads a high-engagement video
A sound clusters in multiple new videos quickly
MVP Success Criteria:
Seed scraping works and refreshes automatically
Discovery expands database based on usage
Scoring logic identifies and surfaces top-growing sounds
UI/dashboard displays real-time ranked sounds with filters
Export and alert features work
CAPTCHA and proxy solutions are functional
Deliverables:
Fully working scraping + monitoring tool
Source code with documentation
Basic UI for viewing and exporting data
Setup instructions
To Qualify:
Must have experience scraping dynamic, JavaScript-rendered websites
Should be familiar with Playwright, Puppeteer, proxies, and CAPTCHA handling
Must include examples of past scraping/monitoring tools
Should provide an estimated timeline and cost for delivering an MVP