The Complete Guide to Scraping Twitter with Python in 2022

Twitter is an incredibly valuable source of data with hundreds of millions of active users. But getting access to that data can be challenging. In this comprehensive 2500+ word guide, you‘ll learn how to leverage the power of Python to scrape insights from Twitter at scale.

Why Scrape Twitter Data?

With 238 million monetizable daily active users as of Q4 2022, Twitter generates over 500 million tweets per day. This real-time public conversation contains a wealth of insights around consumer sentiment, brand perception, trending topics, and emerging narratives.

Twitter scraping allows you to unlock these insights for a variety of uses:

Brand monitoring and social listening – Identify mentions of your brand, product or service. Analyze associated sentiment and conversations. Gain competitive intelligence.

Influencer and community analysis – Discover influential voices around topics. Map discussion communities and track engagement.

Trend analysis – Identify emerging trends, narratives, and events as they unfold on Twitter.

Market and political research – Analyze consumer attitudes, political affiliations, and demographic data.

Academic studies – Gather data for linguistic, sociological or cultural research and modeling.

For example, scraping tweets around new product launches can surface customer pain points and feature requests. Scraping politician handles during elections can reveal voting base demographics and shifting narratives.

The challenge is that the Twitter API has major limitations:

• Only returns last 7-10 days of tweets
• Rate limited to a few hundred requests per 15 minutes
• Needs approved developer access

Web scraping provides complete access to Twitter‘s publicly available data to unlock richer insights.

Is Web Scraping Twitter Legal?

The legality of scraping social media sites like Twitter exists in a gray area and depends on how the data will be used. Here are some guidelines:

• Public data only – Never scrape or use private, protected, or deleted data that requires a login. Only use what‘s publicly visible.

• Non-commercial use – Scraping for academic, journalistic, or personal research purposes typically faces fewer legal risks. But check Twitter‘s terms.

• Consult terms of service – While not legally binding, understand Twitter‘s ToS. Currently, their ToS permits scraping for personal use cases.

• Limit volume – Avoid repeatedly scraping the same data or users. Build in delays and throttling to reduce disruption.

• Delete data when requested – Honor any user requests to delete collected data under rights like the EU‘s right to erasure.

• Anonymize personal information – Scrub usernames, handles, photos, etc. from collected data to protect privacy.

• Consult a lawyer for commercial use – If monetizing the analysis or data, get legal advice to understand copyright and data protection risks.

So while scraping Twitter is generally allowed in most jurisdictions, you need to weigh risks and structure your activities responsibly.

Twitter Scraping Approaches

There are several ways to scrape Twitter using Python:

Official API

The advantage of the Twitter API is that it‘s simple to use and offers structured data. But rate limits severely restrict how much data you can collect. It also lacks access to historical tweets older than 7-10 days.

So the API works for small, targeted data needs – but not large-scale mining.

Scraping Libraries

Python libraries like snscrape, tweepy, and twitter-scraper handle the heavy lifting of scraping Twitter and outputting structured data. This makes it easy to get started. But you rely on the library maintainer for updates.

Custom Scrapers

For maximum control, you can build a custom scraper using Python libraries like Selenium, Beautiful Soup, Scrapy, etc. This requires more skill but lets you tailor the scraper to your needs.

Commercial Tools

Services like Phantombuster, Dexi.io, and Octoparse provide GUI interfaces or templates to scrape Twitter without coding. But they can get pricey at scale.

Overall, scraping libraries strike the right balance of ease-of-use and customization for most Twitter data mining needs. Let‘s focus on using snscrape.

Scraping Twitter with Snscrape

Snscrape is a popular Python scraping framework focused solely on social media sites. It handles JavaScript rendering, pagination, proxies, and more under the hood so you can focus on extracting the data.

Let‘s walk through a basic Twitter scraping workflow with snscrape step-by-step:

Step 1 – Import snscrape

Install the snscrape package:

pip install snscrape

And import the modules:

from snscrape.modules import twitter
import json

This gives us access to snscrape‘s Twitter-specific scraping tools.

Step 2 – Define query parameters

First, identify the Twitter data you want to extract. Some options:

• Keyword or hashtag to search for related tweets
• A user‘s handle to scrape their profile, tweets, followers
• A tweet ID to retrieve a single tweet object

Let‘s say we want to get mentions of avocados over the last week to analyze attitudes:

keyword = ‘avocados‘
start_date = ‘2023-02-01‘ 
end_date = ‘2023-02-07‘
limit = 1000

This will retrieve up to 1000 tweets containing "avocados" from February 1-7, 2024.

Step 3 – Create scraper instance

Now we can instantiate the TwitterSearchScraper module:

scraper = twitter.TwitterSearchScraper(keyword)
scraper.set_time_range(start_date, end_date)

We provide our keyword and date range. This initializes the scraper ready for data extraction.

Step 4 – Scrape tweet data

With the scraper configured, we iterate through extracted tweets:

for i,tweet in enumerate(scraper.get_items()):

    if i >= limit:
        break 

    tweet_data = json.loads(tweet.json())

    print(tweet_data[‘content‘])
    print(tweet_data[‘user‘][‘location‘])  

This prints the text and location of each tweet while enforcing our limit.

We can also write JSON lines to a file:

with open(‘avocado_tweets.jsonl‘, ‘w‘) as f:

    for i,tweet in enumerate(scraper.get_items()):

        if i >= limit:
            break

        f.write(tweet.json())

This stores tweets in a JSON lines file for easy downstream analysis.

Step 5 – Scrape other data

To collect different Twitter data, simply swap out the scraper instance:

Hashtags

scraper = twitter.TwitterHashtagScraper(‘#WorldCup‘)

Users

scraper = twitter.TwitterUserScraper(‘elonmusk‘) 

Single tweets

scraper = twitter.TwitterTweetScraper(1516924810421964800)

The process remains the same – instantiate a relevant scraper, iterate through results, and write to file.

And that‘s the foundation for scraping data from Twitter with snscrape! Let‘s look at more advanced techniques.

Augmenting Scraped Tweets with Replies and Retweets

By default, snscrape retrieves just the original tweet objects that match your query. But you often want to analyze replies and retweets to understand the full conversation.

Fortunately, snscrape makes it easy to recursively scrape those as well:

for tweet in scraper.get_items():

    print(json.loads(tweet.json())[‘content‘])

    for reply in tweet.replies:
        print(json.loads(reply.json())[‘content‘])  

    for retweet in tweet.retweets:
        print(json.loads(retweet.json())[‘content‘])

This navigates through the tree of replies and retweets under each tweet. The output preserves the full conversation context.

Scraping Users Who Tweeted

In some cases, understanding who tweeted is just as important as what they tweeted.

We can augment tweets with user data like so:

for tweet in scraper.get_items():

    user_data = scraper.get_user(tweet.user) 

    print(user_data.username)
    print(user_data.followersCount)
    print(user_data.created)

This extracts metadata for each tweeting user, like username, follower count, and account creation date.

These types of augmentations supercharge your Twitter data analysis!

Comparing Snscrape to Other Scraping Libraries

Snscrape is a great choice for scraping Twitter. But there are situations where alternatives might be better suited:

Tweepy – Good for simple streaming based on keywords/users. But no built-in support for proxies, pagination, or tweet trees.

TwitterScraper – Lightweight and fast. But no proxy support and limited to basic tweet scraping.

Twint – Provides a command line interface. But no longer maintained and lacks proxy/JavaScript support.

Scraptweet – Focused specifically on users, followers, posts. Lacks support for general search scraping.

Twitter API – Official API is easiest to use. But harsh restrictions on data access.

So snscrape hits the sweet spot of enough power and customization for most Twitter mining needs. But it‘s worth exploring other libraries in case they better fit your use case.

Best Practices for Avoiding Twitter Blocks

Twitter tries to detect and block scrapers and bots to protect its infrastructure. Here are some tips to scrape under the radar:

• Use residential proxies – Rotate different residential IP addresses to mimic real users and distribute requests.

• Implement random delays – Add 3-10 second delays between scraping pages or tweets to throttle traffic.

• Vary user agents – Rotate multiple browser user agent strings so your scraper appears more human.

• Check for blocks – Monitor if IPs and user agents are getting blocked and adjust your approach accordingly.

• Scrape during low-traffic periods – Hit Twitter in the early AM when overall site traffic is lower.

• Limit historical depth – Only scrape back as far as you need to reduce strain on Twitter‘s servers.

• Use multiple tools – Have a fallback scraping approach ready in case your main tool stops working or gets blocked.

• Stay updated on ToS changes – Keep an eye out for any Twitter policy updates that may impact scraping.

Following scraping best practices helps avoid disruptions in your data collection. For large scraping projects, it‘s also wise to consult a lawyer to ensure your specific use case is permitted.

Conclusion

Scraping Twitter opens up a world of possibilities for analyzing public social data at scale. In this comprehensive guide, you learned:

• Twitter scraping powers everything from academic studies to market research to brand monitoring.

• Scraping public data generally falls within Twitter‘s terms of service, but stay aware of data protection laws.

• Snscrape provides an easy way to scrape Twitter in Python with more flexibility than the official API.

• With snscrape, you can scrape tweets, hashtags, user profiles, conversations, and more.

• Techniques like proxy rotation and traffic throttling are key for stable, large-scale Twitter data mining.

The insights buried within Twitter‘s mountain of data are now at your fingertips. Follow this guide to start scraping Twitter with Python and unveil online narratives, trends, and consumer opinions that impact your business or research.