How to Scrape Twitter Tweets with Python

Extract tweets data from Twitter

🐍 Using Python

Overview

Learn how to scrape Twitter tweets using Python. This comprehensive guide will walk you through the entire process, from setup to implementation.

What You'll Learn

  • • Setting up your development environment
  • • Installing the required HTTP client
  • • Authenticating with the ScrapeCreators API
  • • Making requests to Twitter
  • • Handling responses and errors
  • • Best practices for production use

What You'll Get

  • • Access to tweets data
  • • JSON formatted responses
  • • Real-time data access
  • • Scalable solution
  • • Error handling patterns
  • • Performance optimization tips

Prerequisites

1. API Key

First, you'll need a ScrapeCreators API key to authenticate your requests.

Sign up at app.scrapecreators.com to get your free API key with 100 requests.

2. Development Environment

Make sure you have the following installed:

  • • Python and its dependencies
  • • A code editor (VS Code, Sublime, etc.)
  • • Basic understanding of API requests
  • • Command line interface access

Step 1: Install HTTP Client

Requests is a simple HTTP library for Python

pip
pip install requests

Step 2: API Implementation

Now let's make a request to the Twitter API using Python. Replace YOUR_API_KEY with your actual API key.

Python
import requests

API_KEY = 'YOUR_API_KEY'

def scrape():
    headers = {
        'x-api-key': API_KEY,
        'Content-Type': 'application/json'
    }

    params = {
        'url': 'https://x.com/i/communities/1926186499399139650'
    }

    try:
        response = requests.get(f'https://api.scrapecreators.com/v1/twitter/community/tweets', headers=headers, params=params)
        response.raise_for_status()

        data = response.json()
        print('Response:', data)
        return data
    except requests.exceptions.RequestException as e:
        print('Error:', e)
        return None

# Usage
result = scrape()

Step 3: Testing Your Code

API Parameters

This endpoint accepts the following parameters:

urlRequired(string)

Community URL

Example: https://x.com/i/communities/1926186499399139650

Run Your Code

Execute your script to test the API connection. You should see a JSON response with Twitter tweets data.

✅ Success: You should receive a structured JSON response containing the requested data.

Expected Response

Here's an example of the JSON response you'll receive:

Sample Response
{
  "success": true,
  "tweets": [
    {
      "id": "1940874916771123735",
      "source": "<a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>",
      "view_count": "8660",
      "bookmark_count": 10,
      "bookmarked": false,
      "created_at": "Thu Jul 03 20:46:54 +0000 2025",
      "conversation_id_str": "1940874916771123735",
      "display_text_range": [
        0,
        27
      ],
      "favorite_count": 173,
      "favorited": false,
      "full_text": "You just need 1 viral tweet https://t.co/Z4Q5jtsxAp",
      "is_quote_status": false,
      "lang": "en",
      "possibly_sensitive": false,
      "possibly_sensitive_editable": true,
      "quote_count": 2,
      "reply_count": 55,
      "retweet_count": 3,
      "retweeted": false,
      "user_id_str": "73647967",
      "id_str": "1940874916771123735",
      "user": {
        "__typename": "User",
        "id": "VXNlcjo3MzY0Nzk2Nw==",
        "rest_id": "73647967",
        "affiliates_highlighted_label": {},
        "avatar": {
          "image_url": "https://pbs.twimg.com/profile_images/1590652776719286274/eeualJ2p_normal.jpg"
        },
        "core": {
          "created_at": "Sat Sep 12 14:00:05 +0000 2009",
          "name": "Mike in’ Software",
          "screen_name": "mikeinsoftware"
        },
        "dm_permissions": {},
        "is_blue_verified": true,
        "legacy": {
          "default_profile": true,
          "default_profile_image": false,
          "description": "Senior SWE👨‍💻 Indie hacking my way to financial freedom 👀 Follow for free early access to next project",
          "entities": {
            "description": {
              "urls": []
            }
          },
          "fast_followers_count": 0,
          "favourites_count": 7532,
          "followers_count": 1173,
          "friends_count": 990,
          "has_custom_timelines": true,
          "is_translator": false,
          "listed_count": 8,
          "media_count": 518,
          "normal_followers_count": 1173,
          "pinned_tweet_ids_str": [
            "1945599110876176561"
          ],
          "possibly_sensitive": false,
          "profile_interstitial_type": "",
          "statuses_count": 2931,
          "translator_type": "none",
          "withheld_in_countries": []
        },
        "location": {
          "location": ""
        },
        "media_permissions": {},
        "parody_commentary_fan_label": "None",
        "profile_image_shape": "Circle",
        "professional": {
          "rest_id": "1928057904189559014",
          "professional_type": "Creator",
          "category": [
            {
              "id": 1055,
              "name": "Software developer/Programmer/Software engineer",
              "icon_name": "IconBriefcaseStroke"
            }
          ]
        },
        "privacy": {
          "protected": false
        },
        "relationship_perspectives": {},
        "tipjar_settings": {
          "is_enabled": false,
          "bandcamp_handle": "",
          "bitcoin_handle": "",
          "cash_app_handle": "",
          "ethereum_handle": "",
          "gofundme_handle": "",
          "patreon_handle": "",
          "pay_pal_handle": "",
          "venmo_handle": ""
        },
        "verification": {
          "verified": false
        }
      }
    }
  ]
}

Verify Response Structure

Check that your response includes the expected fields:

  • ✓success(boolean)
  • ✓tweets(object)

Best Practices

1

Error Handling

Implement comprehensive error handling and retry logic for failed requests. Log errors properly for debugging.

2

Caching

Cache responses when possible to reduce API calls and improve performance. Consider data freshness requirements.

3

Security

Never expose your API key in client-side code. Use environment variables and secure key management practices.

Performance Tips

Batch Requests

When scraping multiple tweets, consider batching requests to maximize throughput while staying within rate limits.

Async Processing

Use asynchronous processing in Python to handle multiple requests concurrently and improve overall performance.

Common Use Cases

Market Research

Analyze Twitter tweets to understand market trends, competitor analysis, and audience insights.

Content Analytics

Track performance metrics, engagement rates, and content trends across Twitter tweets.

Lead Generation

Identify potential customers and business opportunities throughTwitter data analysis.

Troubleshooting

Common Errors

401 Unauthorized

Check your API key is correct and properly formatted in the x-api-key header.

402 Payment Required

You ran out of credits and need to buy more.

404 Not Found

The resource might not exist or be private.

500 Server Error

Temporary server issue. Implement retry logic with exponential backoff.

Frequently Asked Questions

How much does it cost to scrape Twitter tweets?

ScrapeCreators offers 100 free API calls to get started. After that, pricing starts at $10 for 5k requests with volume discounts available.

Is it legal to scrape Twitter data?

Scraping publicly available data is fair game, and we only collect public data. So anything that you can see in an incognito browser is what we collect.

How fast can I scrape Twitter tweets?

There is no rate limit! So you can scrape as fast as you want!

What data format does the API return?

All API responses are returned in JSON format, making it easy to integrate with any programming language or application.

Can I use this with other Python frameworks?

Yes! This tutorial focuses on core Python HTTP concepts that work with any framework. The API calls remain the same regardless of your specific Python setup.

How do I handle large datasets?

For large datasets, implement pagination, use streaming responses where available, and consider storing data in a database for efficient querying.

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