Twitter Trends Scraper

Overview

The Twitter Trends Scraper automates the collection of trending topics, hashtags, and tweet volumes from Twitter (X). It extracts structured data in real-time or from historical snapshots, eliminating the need for manual tracking. This is useful for marketers, researchers, and analysts who need to monitor conversations, identify emerging trends, and gather reliable data for decision-making. The scraper can track trends globally or for specific countries.

Key Features

• Real-Time & Historical Tracking: Capture live trends or data from past hours/days.
• Proxy Support: Maintain stability and avoid rate limits during large-scale scraping.
• Multiple Export Formats: Download results as CSV, JSON, or Excel for analysis.
• Developer-Friendly: Integrates with custom Python scripts, GitHub repositories, and CLI tools.
• Ready-to-Use Automation: Available as the Apify Twitter Trends Scraper for no-code operation.

How to Use

You can use the ready-to-use Apify actor or implement custom scripts.

Using the Apify Actor:
1. Access: Sign up on Apify to use the Apify Twitter Trends Scraper.
2. Configure Input: Set parameters like location (global or country-specific) and time period (live, hourly, daily).
3. Run: Start the actor. It will scrape trending topics, volumes, and engagement data.
4. Export: Download the results in CSV, JSON, or Excel format.
5. Automate (Optional): Schedule runs or integrate the data into dashboards and analytics pipelines.

Input/Output

Input Configuration (example):
The actor accepts a JSON input configuration. The startUrls array defines the target pages on getdaytrends.com for scraping. Boolean flags like live, hour1, hour24, day2, etc., control which time periods to fetch.

{
  "day2": false,
  "day3": false,
  "hour1": false,
  "hour12": false,
  "hour24": false,
  "hour3": false,
  "hour6": false,
  "live": false,
  "startUrls": [
    { "url": "https://getdaytrends.com/", "method": "GET" },
    { "url": "https://getdaytrends.com/2025-09-22/1/", "method": "GET" }
  ]
}

Output Data (example):
The scraper returns an array of trend objects in JSON format. Each object contains the trend name, tweet volume, and timestamp.

[
  {
    "time": "2025-09-22T05:53:04.664851Z",
    "timePeriod": "Custom",
    "trend": "Guana",
    "volume": "65.3K tweets"
  },
  {
    "time": "2025-09-22T05:53:04.664851Z",
    "timePeriod": "Custom",
    "trend": "#Navratri2025",
    "volume": "23.8K tweets"
  }
]

Extractable Data Points:
* Trending Topic/Hashtag
* Tweet Volume
* Country/Region
* Time Period (Live, Hourly, Daily)
* Engagement Signals (Retweets, Likes)
* Basic Follower Metrics