How to Scrape Tweets from Twitter Using Python in 2025

If you’ve ever tried to keep up with what’s trending on Twitter (or “X,” as it’s now known), you know it’s like trying to drink from a firehose—except the firehose is blasting out over . For businesses, researchers, and anyone who wants to tap into the world’s real-time conversations, Twitter data is pure gold. But in 2025, scraping tweets isn’t as simple as it used to be. Between Twitter’s API paywalls, ever-changing site defenses, and the cat-and-mouse game with scrapers, getting the data you need can feel like hacking your way through a digital jungle. Luckily, Python is still the Swiss Army knife for scraping tweets—if you know which tools to use and how to dodge the latest obstacles. In this guide, I’ll walk you through the practical ways to scrape tweets from Twitter using Python (and a little help from Thunderbit), share the latest tips for getting around Twitter’s restrictions, and show you how to turn that raw tweet data into business insights that actually matter.

What Does It Mean to Scrape Tweets from Twitter Using Python?

Let’s keep it simple: scraping tweets from Twitter with Python means using code to automatically collect tweet data—like text, author, timestamp, likes, retweets, and more—so you can analyze it outside of Twitter’s website. Think of it as building your own custom Twitter dashboard, but with the power to slice, dice, and visualize the data however you want.

There are two main ways to do this:

• API-Based Scraping: Using Twitter’s official API (with libraries like Tweepy), you get structured data straight from Twitter’s servers. This method is stable and reliable, but comes with strict limits and, as of 2025, a hefty price tag.
• Web Scraping: Using tools like Snscrape or browser automation, you pull data directly from Twitter’s public web pages—no API key required. This can get you around some limits, but it’s more fragile and requires you to keep up with Twitter’s frequent changes.

Typical data fields you can collect include:

• Tweet text/content
• Tweet ID and URL
• Timestamp (date and time)
• Username and user profile info
• Engagement metrics (likes, retweets, replies, views)
• Hashtags and mentions
• Media links (images, videos)
• Conversation context (e.g., replies, threads)

Basically, if you can see it on Twitter’s website, there’s a way to scrape it—at least for now.

Why Scrape Tweets from Twitter? Key Business Use Cases

So why go through all this trouble? Because Twitter is where the world talks about everything—your brand, your competitors, the next big trend, and the occasional viral cat video. Here’s how teams are using scraped Twitter data in 2025:

And the ROI? Let’s just say compared to some other social platforms. If you’re not listening to what’s being said about your brand or industry on Twitter, you’re missing out on real-time, actionable intelligence.

Overview: All the Ways to Scrape Tweets from Twitter with Python

The Python ecosystem is packed with tools for scraping Twitter, but not all are created equal—especially after Twitter’s API changes and anti-scraping crackdowns. Here’s a quick comparison of your main options in 2025:

Let’s break down each approach.

Using Python Libraries: Tweepy, Snscrape, and More

Tweepy is your go-to for API-based scraping. It’s stable, well-documented, and gives you structured tweet data—if you’re willing to pay for API access. The catch? In 2025, , and full-archive access is locked behind pricey enterprise or academic plans.

Snscrape is the people’s champion: no API keys, no paywalls, just pure Python scraping of Twitter’s public web data. It’s perfect for historical tweets, large datasets, or when you want to avoid API limits. The downside? Twitter’s anti-scraping defenses mean Snscrape can break every few weeks, so you’ll need to keep it updated and be ready for some troubleshooting.

Other tools like Twint have fallen out of favor due to maintenance issues, so in 2025, Tweepy and Snscrape are your best bets for Python-based scraping.

Web Scraping Twitter: When and Why

Sometimes, the data you need isn’t available via the API or Snscrape—like scraping every reply in a thread, or pulling a list of followers. That’s when you roll up your sleeves and write a custom scraper using requests, BeautifulSoup, or browser automation (Selenium/Playwright). But be warned: Twitter’s anti-bot measures are no joke. You’ll need to handle logins, rotating tokens, dynamic content, and frequent site changes. It’s a high-maintenance, high-reward game.

For most users, it’s smarter to use a maintained tool (like Snscrape or Thunderbit) than to build your own scraper from scratch—unless you really love debugging broken scripts at 2am.

Thunderbit: The Fastest Way to Scrape Twitter Web Data

is my secret weapon for scraping Twitter in 2025—especially if you want results fast, without writing a single line of code. Here’s why Thunderbit stands out:

• 2-Click Extraction: Just open the Twitter page you want, click “AI Suggest Fields,” and Thunderbit’s AI figures out what to scrape (tweet text, author, date, likes, etc.). Hit “Scrape,” and you’re done.
• Handles Infinite Scroll & Subpages: Thunderbit auto-scrolls to load more tweets and can even visit each tweet’s page to grab replies or extra details.
• No-Code, Low Maintenance: The AI adapts to Twitter’s layout changes, so you don’t have to babysit your scraper.
• Structured Export: Export your data directly to Excel, Google Sheets, Airtable, or Notion—no extra steps.
• Cloud Scraping: For big jobs, Thunderbit can scrape up to 50 pages at once in the cloud, so you don’t have to leave your browser open.
• AI Data Enrichment: Add custom fields (like sentiment or topic labels) with AI prompts as you scrape.

Thunderbit is perfect for business users, analysts, or anyone who wants to turn Twitter data into insights—without the technical headaches.

Step-by-Step Guide: How to Scrape Tweets from Twitter Using Python

Ready to get your hands dirty? Here’s how to scrape tweets in 2025, step by step.

Step 1: Set Up Your Python Environment

First, make sure you’re running Python 3.8 or higher. Install the necessary libraries:

1pip install tweepy snscrape pandas

Optional (for analysis/visualization):

1pip install matplotlib textblob wordcloud

If you’re using Tweepy, you’ll also need Twitter API credentials (bearer token). For Snscrape, you’re good to go—no keys required.

Step 2: Scrape Tweets Using Tweepy (API-Based)

a. Get Your API Credentials

Sign up for a and subscribe to a paid API tier (Basic is $100/month for 10k tweets). Grab your Bearer Token.

b. Authenticate and Search Tweets

1import tweepy
2client = tweepy.Client(bearer_token="YOUR_BEARER_TOKEN")
3query = "AcmeCorp -is:retweet lang:en"
4response = client.search_recent_tweets(
5    query=query,
6    tweet_fields=["created_at", "public_metrics", "author_id"],
7    max_results=100
8)
9tweets = response.data
10for tweet in tweets:
11    print(tweet.text, tweet.public_metrics)

• Limitations: You’ll only get tweets from the past 7 days unless you have academic or enterprise access.
• Pagination: Use response.meta['next_token'] to fetch more results.
• Rate Limits: Watch out for 429 errors—if you hit your quota, you’ll need to wait.

Step 3: Scrape Tweets Using Snscrape (No API Required)

a. Basic Usage

1import snscrape.modules.twitter as sntwitter
2import pandas as pd
3query = "AcmeCorp since:2025-10-01 until:2025-10-31"
4tweets_list = []
5for i, tweet in enumerate(sntwitter.TwitterSearchScraper(query).get_items()):
6    tweets_list.append([
7        tweet.id, tweet.date, tweet.user.username, tweet.content,
8        tweet.replyCount, tweet.retweetCount, tweet.likeCount
9    ])
10    if i >= 999:  # Limit to 1000 tweets
11        break
12df = pd.DataFrame(tweets_list, columns=[
13    "TweetID", "Date", "Username", "Text", "Replies", "Retweets", "Likes"
14])
15print(df.head())

• No API keys, no 7-day limit, and you can scrape historical tweets.
• Limitations: Snscrape can break when Twitter changes its site. If you get errors, update the package (pip install --upgrade snscrape) or check for fixes.

b. Scrape by User or Hashtag

1# All tweets from @elonmusk
2scraper = sntwitter.TwitterUserScraper("elonmusk")
3# All tweets with #WorldCup
4scraper = sntwitter.TwitterHashtagScraper("WorldCup")

Step 4: Handling Twitter’s Scraping Restrictions

Twitter’s not a fan of scrapers, so be prepared for:

• Rate Limits: Slow down your requests (add time.sleep() in loops) or break your queries into smaller chunks.
• IP Blocking: Avoid running scrapers from cloud servers; use residential proxies if you’re scraping at scale.
• Guest Token Issues: If Snscrape fails to get a guest token, try updating the package or using a browser session cookie.
• Changing Page Structure: Be ready to update your code or switch tools if Twitter changes its site.
• Legal/Ethical Concerns: Always scrape responsibly—stick to public data, respect rate limits, and follow Twitter’s terms.

If you find yourself spending more time fixing your scraper than actually analyzing data, consider switching to a maintained tool or supplementing with Thunderbit.

Step 5: Scrape Twitter Web Data with Thunderbit

Sometimes you just want the data—no code, no drama. Here’s how to do it with :

1. Install the and log in.
2. Go to the Twitter page you want to scrape (profile, search, hashtag, replies, etc.).
3. Click the Thunderbit icon, then “AI Suggest Fields.” The AI will propose fields like tweet text, author, date, likes, etc.
4. Hit “Scrape.” Thunderbit will auto-scroll, collect tweets, and display them in a table.
5. (Optional) Scrape Subpages: Select tweets and click “Scrape Subpages” to collect replies or thread details.
6. Export your data to Excel, Google Sheets, Notion, or Airtable—free and unlimited.
7. Schedule recurring scrapes if you want to monitor trends or mentions over time.

Thunderbit’s AI adapts to Twitter’s changes, so you don’t have to. It’s a huge time-saver for business users and analysts.

Analyzing and Visualizing Scraped Tweets Data with Python

Once you’ve got your tweets, it’s time to turn that data into insights. Here’s a quick workflow:

1. Load Data into pandas

1import pandas as pd
2df = pd.read_csv("tweets.csv")  # Or .xlsx if you exported from Thunderbit

2. Clean and Preprocess

1df['Date'] = pd.to_datetime(df['Date'])
2df['CleanText'] = df['Text'].str.replace(r'http\S+', '', regex=True)

3. Analyze Hashtags

1from collections import Counter
2hashtags = Counter()
3for text in df['Text']:
4    hashtags.update(part[1:] for part in text.split() if part.startswith('#'))
5print(hashtags.most_common(10))

4. Plot Tweet Frequency

1import matplotlib.pyplot as plt
2df.set_index('Date', inplace=True)
3tweets_per_day = df['Text'].resample('D').count()
4tweets_per_day.plot(kind='line', title='Tweets per Day')
5plt.show()

5. Sentiment Analysis

1from textblob import TextBlob
2df['Polarity'] = df['CleanText'].apply(lambda x: TextBlob(x).sentiment.polarity)
3df['SentimentLabel'] = pd.cut(df['Polarity'], bins=[-1, -0.1, 0.1, 1], labels=['Negative','Neutral','Positive'])
4print(df['SentimentLabel'].value_counts())

6. Visualize Top Hashtags

1top10 = hashtags.most_common(10)
2labels, counts = zip(*top10)
3plt.barh(labels, counts)
4plt.xlabel("Count")
5plt.title("Top 10 Hashtags")
6plt.show()

The possibilities are endless—track engagement, spot influencers, monitor sentiment, or build dashboards for your team.

From Scraping to Business Value: Turning Twitter Data into Insights

Scraping tweets is just the start. The real magic happens when you use that data to drive decisions:

• Brand Monitoring: Set up alerts for negative sentiment spikes and respond before a PR crisis erupts.
• Competitor Tracking: Spot product launches or customer complaints about rivals and adjust your strategy in real time.
• Trend Spotting: Identify emerging topics before they hit the mainstream and position your brand as a thought leader.
• Lead Generation: Find tweets with buying intent and reach out to prospects while they’re still in the market.
• Campaign Measurement: Track hashtag usage and engagement to measure ROI and optimize future campaigns.

With tools like Thunderbit, you can even schedule scrapes and push data directly into Google Sheets or Airtable, making it easy to build live dashboards or trigger automated workflows.

Conclusion & Key Takeaways

Scraping tweets from Twitter using Python in 2025 is a moving target—but with the right tools and strategies, it’s absolutely doable (and more valuable than ever). Here’s what to remember:

• Python is still king for tweet scraping, but you need to choose the right tool for the job—API (Tweepy) for stability, Snscrape for flexibility, or Thunderbit for speed and ease.
• Twitter’s defenses are tough, so be ready to update your tools, use proxies, and scrape responsibly.
• Thunderbit is a game-changer for non-coders and business users, offering two-click scraping, AI-powered data structuring, and seamless exports.
• The real value is in the analysis— use pandas, matplotlib, and AI to turn raw tweets into actionable business insights.
• Always respect Twitter’s terms and user privacy. Scrape ethically and use data for good.

Want to see how easy scraping can be? , or check out more guides on the .

Happy scraping—and may your tweet data always be fresh, structured, and full of insights.

FAQs

1. Is it legal to scrape tweets from Twitter using Python?
Scraping public tweets for analysis is generally allowed, but you must respect Twitter’s terms of service and privacy policies. Avoid scraping private data, don’t overload their servers, and use the data responsibly—especially if you plan to publish or share it.

2. What’s the difference between using Tweepy and Snscrape for scraping tweets?
Tweepy uses Twitter’s official API, which is stable but limited and now requires a paid subscription. Snscrape scrapes public web data without API keys, offering more flexibility but requiring more maintenance due to Twitter’s frequent site changes.

3. How do I avoid getting blocked when scraping Twitter?
Throttle your requests (add delays), avoid scraping from cloud servers (use residential IPs if possible), and don’t scrape too much at once. If you hit rate limits or get blocked, pause and try again later.

4. Can Thunderbit scrape replies, threads, or user lists from Twitter?
Yes! Thunderbit’s subpage scraping feature lets you collect replies, thread details, or even follower lists—just select the rows and click “Scrape Subpages.” It’s the easiest way to get structured data from complex Twitter pages.

5. How can I analyze and visualize scraped tweet data?
Load your data into pandas, clean and preprocess it, then use libraries like matplotlib, seaborn, or wordcloud for visualization. For sentiment analysis, try TextBlob or VADER. Thunderbit exports directly to Excel, Google Sheets, or Airtable for easy integration with your analysis workflows.

Want to learn more about web scraping, data analysis, or automating your business workflows? Dive into more tutorials on the , or subscribe to our for hands-on demos and tips.

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