How To Scrape Tweets in 2024?

Twitter is among the most popular social media networks, with 436 million monthly active users. The platform is a goldmine of data that serves as a crucial resource for businesses, providing insights into consumer behavior. 

However, with over 762 million tweets users post daily, manually scraping a significant amount of data can take time and effort. This is where tools like Snscrape come in.

Snscrape enables users to automate organizing and collecting tweets. It helps businesses transform unstructured data into structured and functional data.

This article discusses scraping tweets with Snscrape, including the ethical scraping practices you must adhere to.

🔑  Key Takeaways

• Twitter already has an API. However, scraping tools like Snscrape let you do more with the platform than the API allows.

• Unlike Twitter API, Snscrape has no limits to the number of tweets you can extract. This means you can retrieve the oldest data related to the item you’re looking for.

• With Twitter scraping, companies can track if their brand is getting talked about and see if customers have complaints that need addressing.
• Scraping tweets is harmless but always consider its potential legal implications.

Twitter Scraping: What is it?

Twitter API is often used to compose tweets, visit profiles, and access data about followers. This tool allows users to gather Twitter’s main data points such as:

• Tweets
• Entities
• Places
• Users

What most people do not know is Twitter scraping lets you do more with the platform than the API allows. Scraping tools like Snscrape create an unofficial Twitter API that helps you extract the following fields:

• Contents
• Timestamps
• Likes
• Usernames
• Hashtags
• Replies
• Reposts
• URLs

Twitter scraping works by using tools to connect to servers. After the user specifies the data they want to collect, the tool starts gathering the required data by sending requests to the server. The collected data will then be stored in a file or database.

Read on to understand how Twitter scraping works and the things you need to start scraping.

What You Need to Scrape Tweets

Scraping tweets using Snscrape requires the installation of Snscrape and some libraries. Here are the requirements and simple steps to install them:

1. Python (3.8 or higher). Download the latest version of Python on your system. Follow the installation instructions properly.

2. Snscrape. To install Snscrape, use the command below and import the library:

pip install snscrapeimport snscrape.modules.twitter as sntwitter

Use this to get the development version:

pip3 install git+https://github.com/JustAnotherArchivist/snscrape.git

3. Other Libraries. Depending on your project goals, you may need to install additional libraries like Pandas DataFrame. 

To install Pandas, launch Command Prompt. Enter the command below on the terminal:

pip install pandas

This launches the pip installer, and the files will be downloaded. After that, Pandas will be ready to operate on your computer.

Once all the prerequisites are installed and set up, proceed to the following section to learn how to start scraping tweets.

4 Steps to Scrape Tweets with Snscrape

Snscrape is an approach for scraping data from Twitter that doesn’t require an API. It also extracts data from prominent social media platforms like Instagram, Facebook, and Reddit.

Unlike Twitter API, Snscrape has no limits to the number of tweets you can extract. This means you can retrieve the oldest data related to the item you’re looking for.

Here is a simple guide to scraping tweets using Snscrape with the hashtag #Elonmusk:

Step 1: Import the installed libraries in your Python script.

import snscrape.modules.twitter as sntwitterimport pandas as pd

Step 2: Use the code below to scrape tweets with the hashtag #Elonmusk.

import itertoolsdf = pd.DataFrame()tweets = sntwitter. TwitterSearchScraper(‘“#Elonmusk” ‘)df = pd.DataFrame(itertools.slice(tweets, 100))

Step 3: Filter the DataFrame to retain desired attributes like date, content, etc.

df = df[[‘date’, ‘id’, ‘content’, ‘user’]]

Step 4: You can export the DataFrame to a CSV file to save the scraped tweets.

df.to_csv( ‘scraped-tweets.csv’, index=False)

Use Cases of Twitter Scraping

Now that you know how Twitter scraping works, it’s time to nail down some excellent reasons to scrape tweets. Check them below:

• Brand Monitoring

Companies use Twitter scraping to monitor what people say about their brand. They can track if their brand is getting talked about and see if customers have complaints that need addressing.

• Competitive Intelligence

Twitter scraping helps companies keep an eye on their competitors. Doing this lets them see what other companies do, like their marketing tricks and what is said about them.

• Sentiment Analysis

Researchers can scrape Twitter to determine how people feel about different things. It helps make better marketing plans, create products people like, and handle public relations better.

• Political Analysis

There are roughly 8,000 tweets sent daily. That said, it’s safe to say it’s a good place to determine what people think about politics. Researchers scrape Twitter to look at trends, see if political campaigns work, and understand public opinion on political issues.

The Legality of Scraping Tweets

Although Elon Musk isn’t exactly happy about Twitter scraping, it is legal if you don’t break copyright laws or use private data.

When you scrape data online, you essentially take information that someone has put out there and use it for different purposes. While this is harmless, it’s vital to consider the potential legal implications of this strategy moving ahead.

Twitter has also Terms of Service that you must follow. If not, your account could get banned, or legal actions could be taken against you. By being aware of these risks and following the law, you can scrape tweets and use them to benefit your business.

Bottom Line

With millions of monthly active users, it’s unsurprising that Twitter is now a valuable source of information. You can gain a competitive advantage by scraping tweets that contain information such as the demographics of people who liked or reposted the tweet.

Tools like Snscrape help simplify this process while ensuring ethical scraping practices are followed. There are places like the European Union where strict data protection laws such as GDPR exist.

FAQs

By Muninder Adavelli

Muninder Adavelli is a core team member and Digital Growth Strategist at Techjury. With a strong background in marketing and a deep understanding of technology's role in digital marketing, he brings immense value to the TechJury team.