Tiktok Hot Live Scraper is a high-performance tool for tracking trending TikTok live streams and extracting real-time engagement metrics. It helps professionals analyze audience behavior, creator performance, and content trends using clean, structured TikTok live analytics data.
Created by Bitbash, built to showcase our approach to Scraping and Automation!
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This project collects and organizes real-time TikTok live stream data, focusing on audience engagement, creator insights, and trend signals. It solves the challenge of identifying high-performing live content and understanding viewer dynamics at scale. It is built for marketers, analysts, researchers, and growth teams who rely on accurate TikTok live analytics.
- Monitors trending TikTok live streams across multiple categories
- Captures engagement, viewer counts, and creator metadata
- Supports repeated collection rounds for temporal analysis
- Designed for scalable, high-reliability data collection
| Feature | Description |
|---|---|
| Live Stream Metrics | Collects viewer counts, likes, stream titles, and durations. |
| Creator Analytics | Extracts creator nicknames, categories, and public profile signals. |
| Audience Insights | Tracks real-time engagement patterns across collection rounds. |
| Trend Classification | Identifies popular categories and trending live content. |
| Smart Deduplication | Ensures unique streams across multiple monitoring rounds. |
| Resilient Execution | Built-in retries, throttling, and stability controls. |
| Field Name | Field Description |
|---|---|
| room_id | Unique identifier of the live stream room. |
| live_url | Direct URL to the TikTok live stream. |
| title | Title or headline of the live stream. |
| category | Content category of the live stream. |
| user_count | Number of active viewers during scraping. |
| like_count | Total likes recorded for the live stream. |
| owner_nickname | Display name of the stream creator. |
| tags | Tags describing content or trend attributes. |
| scraped_at | Timestamp when the data was collected. |
| round | Collection round number for time-based analysis. |
[
{
"room_id": "7555048599752215316",
"live_url": "https://tiktok.com/@creator/live",
"title": "Gaming Tournament Finals",
"category": "Gaming",
"user_count": 15420,
"like_count": 8750,
"owner_nickname": "ProGamer2024",
"tags": "Gaming, Popular, Trending",
"scraped_at": "2024-09-28T15:30:45.123Z",
"round": 1
}
]
Tiktok Hot Live Scraper/
├── src/
│ ├── main.py
│ ├── collectors/
│ │ ├── live_stream_collector.py
│ │ └── trend_classifier.py
│ ├── processors/
│ │ ├── deduplicator.py
│ │ └── metrics_calculator.py
│ └── config/
│ └── settings.example.json
├── data/
│ ├── sample_input.json
│ └── sample_output.json
├── requirements.txt
└── README.md
- Marketing teams use it to track trending live creators, so they can optimize influencer and campaign strategies.
- Content strategists use it to analyze engagement patterns, so they can schedule streams at peak times.
- Market researchers use it to study viewer behavior, so they can identify emerging TikTok live trends.
- Competitive analysts use it to monitor rival creators, so they can benchmark performance effectively.
How often can live streams be monitored? The scraper supports configurable collection rounds with intervals ranging from seconds to hourly checks, enabling both short-term and long-term trend analysis.
Does it collect private or sensitive data? No. Only publicly visible live stream metrics and creator information are captured.
Can it handle large-scale monitoring? Yes. The architecture is designed for multiple rounds and high-volume data collection while maintaining stability.
Is the output easy to integrate with analytics tools? The output is delivered in clean, structured JSON format, making it compatible with dashboards, databases, and reporting pipelines.
Primary Metric: Average response time of approximately 2 seconds per collection round.
Reliability Metric: Sustained success rate above 96% across large-scale monitoring runs.
Efficiency Metric: Capable of tracking dozens of live streams per round with minimal overhead.
Quality Metric: High data completeness with accurate engagement and viewer metrics captured consistently.
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