How to Scrape Twitter Followers Using Python and Proxies

Twitter is a popular social media platform with millions of users around the world. As a marketer or researcher, you might want to scrape Twitter data to gather insights, monitor competitors, or perform sentiment analysis.

In this blog post, we will show you how to scrape Twitter followers using Python and proxies. Proxies are necessary because Twitter might block your IP address if you make too many requests in a short period of time. By using proxies, you can distribute your requests across different IP addresses to avoid detection.

Prerequisites

Before we start, make sure you have the following installed on your system:

• Python 3
• Tweepy library
• csv library

You can install these libraries using the following commands in your command prompt:

pip install tweepy
pip install csv

Step 1: Import Libraries

First, you need to import the required libraries into your Python script. Here is the code to import the libraries:

import tweepy
import csv
import random

• tweepy: for accessing the Twitter API
• csv: for writing the scraped data to a CSV file
• random: for randomly choosing a proxy

Step 2: Set up Proxies

Twitter might block your IP address if you make too many requests in a short period of time. By using proxies, you can distribute your requests across different IP addresses to avoid detection. You can find free proxy lists online. Here is an example list of proxies:

proxies = ['http://10.10.1.10:3128', 'https://10.10.1.11:1080', 'http://10.10.1.10:80']

Step 3: Authenticate with Twitter API

To access the Twitter API, you need to authenticate using your Twitter API credentials. You can create a Twitter developer account and create an app to get your credentials. Here is the code to authenticate with the Twitter API:

def scrape_followers(screen_name):
 # Open CSV file for writing
 with open(f'{screen_name}_followers.csv', 'w', newline='', encoding='utf-8') as file:
 writer = csv.writer(file)
 writer.writerow(['Name', 'Username'])
 
 # Loop through pages of followers
 for follower in tweepy.Cursor(api.followers, screen_name=screen_name).items():
 # Choose a random proxy
 proxy = {'http': random.choice(proxies)}
 api = tweepy.API(auth, proxy=proxy)
 
 # Extract follower information and save to CSV file
 writer.writerow([follower.name, follower.screen_name])

The function takes a Twitter screen name as a parameter and opens a CSV file for writing. It loops through the pages of followers for the screen name and chooses a random proxy for each request. It then extracts the follower name and username, and saves the data to the CSV file.

Alternatives Solution

If you don’t know how to use Python, there are other web scrapers available for scraping Twitter data. One popular web scraper is Octoparse. Octoparse is a free web scraper that allows you to extract data from websites using a point-and-click interface. Here’s how you can use Octoparse to scrape Twitter followers:

1. Go to the Octoparse website and create an account.
2. Download and install the Octoparse software on your computer.
3. Open Octoparse and create a new task.
4. Enter the URL of the Twitter account you want to scrape.
5. Use the point-and-click interface to select the follower name and username.
6. Run the scraper and download the data as a CSV file.

Conclusion

Scraping Twitter data can provide valuable insights for marketers, researchers, and data analysts. However, it is important to scrape responsibly and ethically, and to comply with Twitter’s terms of service.

In this blog post, we demonstrated how to scrape Twitter followers using Python and proxies, which can help you avoid detection and ensure that your requests are not blocked by Twitter.

Using Tweepy library, we authenticated with the Twitter API and set up proxies to distribute our requests across different IP addresses. We then created a function to scrape followers from a Twitter account and save the data to a CSV file.