The famous social media platform X, formerly known as Twitter, can be a goldmine of information, offering invaluable insights to those organizations that can harness its power. From gauging public opinion, tracking trends, or interacting with your audience, there is so much that the site can do.

However, there is so much volume that it is easy to feel overwhelmed by the sheer number of conversations and trending topics, and the speed at which it moves.

Worry not. Learning the right skills to systematically scrape Twitter /  X can make this useful tool much more manageable. In this guide, we will go through the basics of Twitter / X scraping and how to most effectively collect and use data from the site.

What is a Twitter / X scraper?

A Twitter / X scraper is a software tool for extracting data such as tweets, poll voting information, and comments and likes from Twitter / X, to support organizations with anything from analysis to marketing, to monitoring.

Useful, right?

For those planning a web scraping project with Twitter / X, it is important to caveat that although it has many different uses, there are also ethical pitfalls that need to be avoided. It is very important that those looking to scrape Twitter / X carefully follow the site’s strict data usage rules.

In 2023, Twitter was acquired by entrepreneur Elon Musk, who rebranded the business to X. The significant leadership and brand changes included notable shifts such as the tightening of rules on access to its Application Programming Interface (API) access. Developers can use Twitter / X’s API – a set of rules that allows applications to communicate with each other – to build apps that can interact with Twitter / X data, from analyzing trends to automating posts.

X has firm policies for safeguarding the data of its users, and will not disclose or distribute users’ personal information. To ethically web scrape X, organizations must take these policies and best practices for data collection into account. Above all, it is vital to make sure that your scraping is legal and doesn’t violate user privacy rules.

Changes such as these impact data scrapers and third-party applications dealing with the site, and it is important that they adapt to new restrictions.

Datamam, the global specialist data extraction company, works closely with customers to get exactly the data they need through developing and implementing bespoke web scraping solutions.

 

Datamam’s CEO and Founder, Sandro Shubladze, says: “It’s important to note that web scraping should only be done with the respect to any individual whose data is collected.”

 

“The legality and ethical aspects of web scraping are not black and white but depend on a variety of factors. Think of it like driving a car – there are rules to follow to stay safe, but we don’t stop driving. We just make sure to do it safely.”

Do X’s rate limits prevent scraping?

Rate limits are the caps placed by APIs on the number of calls a user or application can make in any given time period. These are critical for X to prevent malicious users from degrading the service’s performance for everyone else.

When X’s ownership changed in 2023, significant increases to its rate-limiting rules were announced to set the permitted volume of scraping by the types of accounts reading tweets. The move is aimed at dissuading malicious data scraping and automated access, which might hurt X’s infrastructure and raise privacy and security concerns.

Stricter rate limits mean that users have less access to a higher volume of tweets. Users relying on X data will need to be more strategic about setting up their data collection, focusing on more targeted scraping so that each request is used efficiently to gather the most valuable data within the limits.

While rate limits can be a hassle when it comes to web scraping, they do not entirely prevent scraping. Instead, they call for a more thoughtful approach to collecting data.

Sandro Shubladze says: “X enhanced its rate-limiting rules to combat malicious scraping and preserve system integrity. These changes mean users have less access to tweet data, requiring more targeted scraping strategies. While these limits complicate large-scale data collection, they don’t entirely block it.”

 

“As long as scraping Twitter data is managed in an ethical way, it is still possible to get valuable data from the site.”

Why might organizations want to scrape X?

Social media contains a vast amount of data that can be used to help organizations gain insights into societal and consumer behaviors. Some of the types of data an organization can scrape from X are:

  • Profiles: This includes basic business information, location, and potentially their follower/following counts. These details can help in building demographic profiles.
  • Like and comment counts: These metrics are crucial for understanding the popularity and engagement levels of specific posts or topics.
  • Hashtags: By analyzing trending and specific hashtags, organizations can gauge the current interests, movements, or sentiments prevalent among users.
  • Tweets: The content of tweets themselves can be a rich source for sentiment analysis, trending topics, and direct feedback on products or events.
  • Lists: Lists curated by users can reveal communities and networks, offering insights into influential groups or opinion leaders within a niche.
  • Other Data: This could include the frequency of posts, the times at which users are most active, and the types of content (e.g., text, images, videos) that are most commonly posted or shared.

This data can be extremely useful for organizations looking to identify current trends across different demographics, which can help in tailoring product or marketing strategies. Social media scraping can also allow companies to monitor sentiment about their own brand and competitors across various channels, helping them manage their reputation strategy.

Insights gained from trending topics and popular content can guide organizations in creating engaging and relevant content for their audiences. X also provides data that can be analyzed to understand social phenomena, market trends, or public opinion.

Social media scraping, when conducted ethically and legally, becomes a powerful tool through which an organization interacts with its current and potential customers.

 

Says Sandro Shubladze: “Social media scraping provides real-time insights into brand perception. Companies can monitor feedback and comments to promptly address any negative sentiments and improve their overall reputation.”

How does an X Scraper work?

It is essential to understand how an X scraper works to be able to extract valuable data from X.

X accounts are made up of User Generated Content (UGC) in the form of text, image, video, or link tweets. Every tweet within X consists of metadata in the form of timestamps, likes, retweets, and replies, offering another layer of detail for the analysis. User profiles have details such as user bios, counts of followers, and so on.

There are a number of ways to scrape X, some of which include:

  • API Scraping: The most efficient and recommended way to scrape X is through its API, which provides structured data and is designed to handle large volumes of requests while respecting the platform’s terms of service. More on this can be read in our detailed guide on API scraping.
  • No-Code Scraping: For those without programming skills, no-code tools offer a user-friendly interface to extract data from X. These tools often use the API but manage the technical aspects internally.
  • Web Scraping with Python: For more customized needs, Python can be used to write scripts that directly scrape data from web pages. This method requires more technical expertise but offers flexibility and power. You can learn more about web scraping with Python in our Python web scraping guide.

A step-by-step guide to scraping X

1. Set-up and planning

Before you start coding, determine the specific data you want to scrape. This will guide your approach and tool selection.

2. Create an X Developer account

First, you need access to the X API, which requires a developer account. You can set this up on X’s developer platform.

3. Scrape user profiles, Tweets, likes, and comments

Once you have access, you can start scraping Twitter data. Here’s how you might use Python with Tweepy to collect user profile information:

import tweepy
# Set up authentication
auth = tweepy.OAuthHandler("YOUR_CONSUMER_KEY", "YOUR_CONSUMER_SECRET")
auth.set_access_token("YOUR_ACCESS_TOKEN", "YOUR_ACCESS_TOKEN_SECRET")
api = tweepy.API(auth)
# Define the username
username = "twitter_user"
# Fetch the user profile
user_profile = api.get_user(screen_name=username)
# Print user details
print("User ID:", user_profile.id_str)
print("Name:", user_profile.name)
print("Bio:", user_profile.description)

4. Parse and process the data

After scraping, the data will likely be in JSON format. Here’s how you might parse and simplify this data for analysis:

import json
# Sample JSON data from Twitter API
tweet_data = '{"created_at": "Wed Oct 10 20:19:24 +0000 2018", "id_str": "1050118621198921728",

"text": "To make progress, you have to learn to be comfortable being uncomfortable. #motivation", "user":

{"id": 2244994945, "name": "Life Coach"} }'

# Parse JSON
data = json.loads(tweet_data)
# Extract and print desired info
print("Tweet ID:", data['id_str'])
print("Tweet Text:", data['text'])
print("Author Name:", data['user']['name'])

5. Handling errors/rate limiting and data cleaning

When scraping at scale, you’ll likely hit API rate limits. Here’s how to handle rate limits and errors:

try:
# Attempt to fetch tweets
tweets = api.search_tweets(q='python', count=100)
except tweepy.TweepError as e:
print("Tweepy error: ", e) 

For data cleaning, you might want to remove duplicates and irrelevant data:

unique_tweets = {tweet.id: tweet for tweet in tweets}.values() # Removes duplicates
clean_tweets = [tweet for tweet in unique_tweets if 'python' in tweet.text.lower()] # Filter relevant tweets

6. Store or use the data for analysis

Finally, store your cleaned data. Here’s a basic way to save the data to a CSV file for later analysis:

import csv
# Define file with headers
with open('tweets.csv', 'w', newline='', encoding='utf-8') as file:
writer = csv.writer(file)
writer.writerow(["ID", "User", "Text"])

# Write data
for tweet in clean_tweets:
writer.writerow([tweet.id, tweet.user.screen_name, tweet.text])

By following these steps and utilizing the provided code snippets, you should have a comprehensive understanding of how to effectively set up, execute, and manage a X scraping project.

“Mastering X scraping involves leveraging the X API for structured and efficient data extraction, using no-code tools for ease of use, or employing Python scripts for customized solutions,” says Sandro Shubladze.

 

“Each method offers unique advantages, from the API’s robust data handling to the flexibility of Python.”

What are the benefits and challenges of scraping Twitter data?

One benefit of scraping Twitter data is that it’s a dynamic social platform comprised of active users, who constantly talk and react to daily events or trends. Scraping this data allows organizations to obtain valuable real-time insights on public sentiment, enabling them to act fast on changing opinions and trends.

By analyzing the data gathered from X, businesses can measure the effectiveness of marketing campaigns and keep an eye on their brand sentiment and shifting consumer behaviors. It becomes possible to analyze and determine how people might react to a certain topic or product.

While there are many benefits, there are also some challenges that need to be addressed. For example, building and maintaining a legislated, technically compliant X scraper is complex. It requires active management in order to adjust with changes in X APIs and data structures.

As we’ve mentioned already, the site’s measures against data privacy breaches are very advanced. Rate limits set a maximum number of API requests per user, constricting the scale at which a scraper can collect data.

Protecting user privacy is of utmost importance when scraping Twitter data. Ensuring ethical web scraping practices is necessary, to avoid breaching its rules. Personal data should never be collected, stored, or used without users’ content.

It is important to have a level of technical expertise to comply with legal standards, which can be daunting for businesses looking at web scraping X for the first time. This is where a custom-built solution like Datamam can make a significant difference.

Datamam specializes in developing tailored data scraping solutions that not only meet the specific needs of a business but also ensure that the scraping practices are compliant with legal and ethical standards. Datamam can create a solution that specifically targets the data your business needs, optimizing both the efficiency of data collection and the relevance of the data collected.

With an in-depth understanding of data privacy laws and X’s scraping policies, Datamam ensures that all data is collected in an ethical and compliant manner. The experienced team can also provide ongoing support to adapt to any changes in X’s platform or data policies, ensuring your data collection remains effective and uninterrupted.

Employing a custom solution like Datamam can significantly simplify this process, allowing businesses to focus on leveraging the insights gained from the data rather than the complexities of collecting it. For more information on how we can support with X scraping projects for your organization, contact Datamam today!