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Web Scraping X (Formerly Twitter) Data with Python & Selenium (Updated Article)

  • Published Date
  • August 28, 2024
  • Read
  • 4min

Table of Contents

Today, we will be exploring how to scrape X (formerly Twitter) and extract valuable information by web scraping Twitter using the versatile and powerful Python programming language. In the era of information overload, it’s crucial to harness the power of data to gain insights, make informed decisions, and fuel innovation.

Social media, particularly Twitter, has become an indispensable source of information, opinions, and trends. Learning to extract data from Twitter can provide us with a treasure trove of real-time information, enabling us to analyze public sentiment, track market movements, and uncover emerging trends.

This comprehensive guide will walk you through the process of web scraping Twitter, step by step, so that even if you are a beginner, you’ll quickly gain the confidence and know-how to get valuable insights from the sea of tweets. We will be using Python & Selenium to Scrape Twitter.com! Also, if you don’t want to read the blog till the very end and want to extract data from Twitter right away, you can check our Twitter Scraper API which provides you the output in parsed JSON data.

Setting up the prerequisites for scraping Twitter

In this tutorial, we are going to use Python 3.x. I hope you have already installed Python on your machine. If not then you can download it from here. Also, create a folder in which you will keep the Python script. Then create a Python file where you will write the code. 
mkdir twitter
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Then create a Python file inside this folder. I am naming it twitter.py. You can use any name you like. Along with this, we have to download third-party libraries like BeautifulSoup(BS4), Selenium, and a chromium driver. This setup is essential for tasks like scraping Twitter with Selenium.

Installation

For installing BeautifulSoup use the below-given command. 
pip install beautifulsoup4
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For installing Selenium use the below-given command. 
pip install selenium
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Selenium is a popular web scraping tool for automating web browsers. It is often used to interact with dynamic websites, where the content of the website changes based on user interactions or other events.

Whereas BS4 will help us parse the data from the raw HTML we are going to download using Selenium

Remember that you will need the exact version of the Chromium driver as your Chrome browser. Otherwise, it will not run.

Testing the setup

Just to make sure everything works fine, we are going to set up our Python script and test it by downloading data from this page. 
    from bs4 import BeautifulSoup
from selenium import webdriver
import time



PATH = 'C:\Program Files (x86)\chromedriver.exe'



target_url = "https://twitter.com/scrapingdog"


driver=webdriver.Chrome(PATH)

driver.get(target_url)
time.sleep(5)



resp = driver.page_source
driver.close()

print(resp)
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Let me explain step by step what each line means.

  • The first three lines import the necessary libraries: BeautifulSoup for parsing HTML and XML, Selenium for automating web browsers, and time for setting a delay.
  • The fourth line sets the path to the chromedriver executable. This is the location where your Chrome driver is installed.
  • Then the fifth line sets the target URL to the Scrapingdog Twitter page that we want to scrape.
  • The sixth line creates a new instance of the ChromeDriver using the path specified in the PATH variable.
  • Then using .get() method of the Selenium browser will navigate to the target page.
  • The next line sets a delay of 5 seconds to allow the page to fully load before continuing with the script.
  • Using .page_source function we get the HTML content of the current page and store it in the resp variable.
  • Then using .close() method we are closing down the browser window. This step will save your server from crashing if you are going to make multiple requests at a time.
  • The last line prints the HTML content of the page to the console.

Once you run this code you will get raw HTML on the console. This test ensures our setup is ready for scraping Twitter with Beautiful Soup and Selenium.

What exactly are we going to extract from a Twitter page?

It is always better to decide in advance what exactly you want to extract from the page. For this tutorial, we are going to scrape these data points.
    • Profile Name
    • Profile Handle
    • Profile Description
    • Profile Category
    • Website URL
    • Joining date
    • Number of Followers
    • Following Count
I have highlighted these data points in the above image. Please refer to that if you have any confusion.

Let’s start scraping Twitter

Continuing with the above code, we will first find the locations of each element and then extract them with the help of BS4. We will use .find() and .find_all() methods provided by the BS4. If you want to learn more about BS4 then you should refer to BeautifulSoup Tutorial.

First, let’s start by finding the position of the profile name.

Extracting Profile name

Here you will find that there are four elements with a div tag and class r-1vr29t4 but the name of the profile is the first one on the list. As you know .find() function of BS4 is a method used to search for and retrieve the first occurrence of a specific HTML element within a parsed document.

With the help of this, we can extract the name of the profile very easily.

    l=list()
o={}

soup=BeautifulSoup(resp,'html.parser')

try:
    o["profile_name"]=soup.find("div",{"class":"r-1vr29t4"}).text
except:
    o["profile_name"]=None
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  • Here we have declared one empty list l and one empty object o.
  • Then we created a BeautifulSoup object. The resulting soup the object is an instance of the BeautifulSoup class, which provides several methods for searching and manipulating the parsed HTML document
  • Then using .find() method we are extracting the text.

Nothing complicated as of now. Pretty straightforward.

Extracting profile handle

For extracting the profile handle we are going to use the same technique we just used above while extracting the name.

In this case, the handle is located inside a div tag with class r-1wvb978. But again there are almost 10 elements with the same tag and class.

Once you will search for this class in the Chrome developer tool you will find that the element where the handle is stored is first in the list of those 10 elements. So, using .find() method we can extract the first occurrence of the HTML element.

try:
    o["profile_handle"]=soup.find("div",{"class":"r-1wvb978"}).text
except:
    o["profile_handle"]=None
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With this code, we can easily scrape the handle name.

Extracting Profile Bio

This one is pretty simple.

try:
    o["profile_bio"]=soup.find("div",{"data-testid":"UserDescription"}).text
except:
    o["profile_bio"]=None
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Extracting Profile Category, website link, and joining date

As you can see all the there data elements are stored inside this div tag with attribute data-testid and value UserProfileHeader_Items. So, our first job would be to find this. 
    profile_header = soup.find("div",{"data-testid":"UserProfileHeader_Items"})
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Now you will notice that the profile category is stored inside a span tag with attribute data-testid and value UserProfessionalCategory. 
  try:
    o["profile_category"]=profile_header.find("span",{"data-testid":"UserProfessionalCategory"}).text
except:
    o["profile_category"]=None
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We can use .find() method on the element profile_header to search for this element. 
try:
    o["profile_category"]=profile_header.find("span",{"data-testid":"UserProfessionalCategory"}).text
except:
    o["profile_category"]=None
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Similarly, you can see that the website link is stored inside a a tag. So, I just have to find the a tag inside profile_header. 
 try:
    o["profile_website"]=profile_header.find('a').get('href')
except:
    o["profile_website"]=None
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I have used .get() method of BS4 to retrieve the value of an attribute from a tag object.

The joining date can also be found inside profile_header with span tag and attribute data-testid and value as UserJoinDate.

try:
    o["profile_joining_date"]=profile_header.find("span",{"data-testid":"UserJoinDate"}).text
except:
    o["profile_joining_date"]=None
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Extracting following and followers count

The following and followers elements can be found under a tag with class r-rjixqe. You will find seven such elements. I have even highlighted that in the above image. But following count and followers count are the first two elements. So, all we have to do is use .find_all() method of BS4. Remember .find_all() will always return a list of elements matching the given criteria.

    try:
    o["profile_following"]=soup.find_all("a",{"class":"r-rjixqe"})[0].text
except:
    o["profile_following"]=None

try:
    o["profile_followers"]=soup.find_all("a",{"class":"r-rjixqe"})[1].text
except:
    o["profile_followers"]=None
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I have used 0 for the following count because it is the first one on the list and 1 for followers because it is the second one on the list.

With this, our process of scraping Twitter and extracting multltiple data points is over.

Complete Code

You can of course extract more data like tweets, profile pictures, etc. But the complete code for all the information we have scraped in this tutorial will look like this. 
    from bs4 import BeautifulSoup
from selenium import webdriver
import time



PATH = 'C:\Program Files (x86)\chromedriver.exe'



l=list()
o={}

target_url = "https://twitter.com/scrapingdog"


driver=webdriver.Chrome(PATH)

driver.get(target_url)
time.sleep(2)



resp = driver.page_source
driver.close()

soup=BeautifulSoup(resp,'html.parser')

try:
    o["profile_name"]=soup.find("div",{"class":"r-1vr29t4"}).text
except:
    o["profile_name"]=None

try:
    o["profile_handle"]=soup.find("div",{"class":"r-1wvb978"}).text
except:
    o["profile_handle"]=None

try:
    o["profile_bio"]=soup.find("div",{"data-testid":"UserDescription"}).text
except:
    o["profile_bio"]=None

profile_header = soup.find("div",{"data-testid":"UserProfileHeader_Items"})

try:
    o["profile_category"]=profile_header.find("span",{"data-testid":"UserProfessionalCategory"}).text
except:
    o["profile_category"]=None

try:
    o["profile_website"]=profile_header.find('a').get('href')
except:
    o["profile_website"]=None

try:
    o["profile_joining_date"]=profile_header.find("span",{"data-testid":"UserJoinDate"}).text
except:
    o["profile_joining_date"]=None

try:
    o["profile_following"]=soup.find_all("a",{"class":"r-rjixqe"})[0].text
except:
    o["profile_following"]=None

try:
    o["profile_followers"]=soup.find_all("a",{"class":"r-rjixqe"})[1].text
except:
    o["profile_followers"]=None


l.append(o)

print(l)
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Using Scrapingdog for scraping Twitter

The advantages of using Scrapingdog Web Scraping API are:
  • You won’t have to manage headers anymore.
  • Every request will go through a new IP. This keeps your IP anonymous.,
  • Our API will automatically retry on its own if the first hit fails. Scrapingdog uses residential proxies to scrape Twitter. This increases the success rate of scraping Twitter or any other such website.
    • You have to sign up for the free account to start using it. It will take just 10 seconds to get you started with Scrapingdog.
Once you sign up, you will be redirected to your dashboard. The dashboard will look somewhat like this.
You have to use your API key. Now, you can paste your Twitter page link to the left and then select JS Rendering as Yes. After this click on Copy Code from the right. Now use this API in your script to scrape Twitter. 
    from bs4 import BeautifulSoup
import requests



l=list()
o={}


resp=requests.get('https://api.scrapingdog.com/scrape?api_key=xxxxxxxxxxxxxxxxxxxx&url=https://twitter.com/scrapingdog')



soup=BeautifulSoup(resp.text,'html.parser')

try:
    o["profile_name"]=soup.find("div",{"class":"r-1vr29t4"}).text
except:
    o["profile_name"]=None

try:
    o["profile_handle"]=soup.find("div",{"class":"r-1wvb978"}).text
except:
    o["profile_handle"]=None

try:
    o["profile_bio"]=soup.find("div",{"data-testid":"UserDescription"}).text
except:
    o["profile_bio"]=None

profile_header = soup.find("div",{"data-testid":"UserProfileHeader_Items"})

try:
    o["profile_category"]=profile_header.find("span",{"data-testid":"UserProfessionalCategory"}).text
except:
    o["profile_category"]=None

try:
    o["profile_website"]=profile_header.find('a').get('href')
except:
    o["profile_website"]=None

try:
    o["profile_joining_date"]=profile_header.find("span",{"data-testid":"UserJoinDate"}).text
except:
    o["profile_joining_date"]=None

try:
    o["profile_following"]=soup.find_all("a",{"class":"r-rjixqe"})[0].text
except:
    o["profile_following"]=None

try:
    o["profile_followers"]=soup.find_all("a",{"class":"r-rjixqe"})[1].text
except:
    o["profile_followers"]=None


l.append(o)

print(l)
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With Scrapingdog API for web scraping, you won’t have to worry about any Chrome drivers. It will be handled automatically for you. You just have to make a normal GET request to the API.

Conclusion

In this article, we managed to scrape certain Twitter profile data. With the same scraping technique, you can scrape publicly available tweets and profile information from Twitter.

You# can take advantage of some Twitter Python libraries with which you can scrape any tweet in normal text. You won’t even have to parse the data.

I hope you like this little tutorial and if you do then please do not forget to share it with your friends and on your social media.

Additional Resources

Here are a few additional resources that you may find helpful during your web scraping journey:

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