Cách thu thập dữ liệu Twitter (X) năm 2026 [Python + công cụ]

Q: Is it legal to scrape Twitter?

Twitter scraping exists in a legal gray area. While Twitter&#039;s Terms of Service prohibit automated data collection, scraping publicly available data isn&#039;t automatically illegal. Legal risks include: 1. Copyright infringement on user content 2. CFAA violations in some jurisdictions 3. GDPR compliance issues with personal data Safer approach: Use Twitter&#039;s official API when possible, consult legal counsel for large projects, and focus on publicly available data for legitimate research purposes.

Q: Do I need to know how to code to scrape X?

No, but coding provides better results. No-code options: Browser automation tools and visual workflow builders work for basic scraping but are limited in speed and flexibility. Coding solutions: Python with Selenium or specialized libraries offers greater control, better anti-bot handling, and higher success rates. Best approach: Start with no-code tools to test your needs, then learn basic Python scripting for serious data collection.

Q: What are the limitations of the free Twitter API?

Twitter&#039;s free API tier has severe restrictions: 1. Rate limits: Very low monthly request quotas 2. Historical data: Limited to recent tweets (typically past week) 3. Tính năng: No advanced analytics or engagement metrics 4. Access: Requires application approval Most research and business use cases exceed free tier limits, making paid plans or alternative methods necessary.

Q: What are the best practices for ethical Twitter scraping?

Essential practices: 1. Giới hạn tốc độ tải: 1-2 seconds between requests minimum 2. Respect robots.txt: Follow platform guidelines 3. Data minimization: Collect only necessary information 4. Proper delays: Use IP rotation and residential proxies 5. Error handling: Stop scraping if blocked or rate-limited Key principle: Always try official APIs first, then scrape responsibly with respect for Twitter&#039;s infrastructure and user privacy.

With more than 666 million active users, the former Twitter, or newly branded X, is one of the most popular social media platforms and a valuable source of information for businesses, researchers, and individuals. However, extracting and filtering data manually from the vast domain of Twitter data is overwhelming and non-functional.

Twitter scraping involves using software or scripts to collect data from the platform. You can analyze this data to gain invaluable insights into trending topics and hashtags, conversations, interactions happening on the platform, and user behavior.

The collected information can be meticulously analyzed for various purposes, such as sentiment analysis, market research, and social media monitoring. This article will delve into different aspects of scraping Twitter data using existing methods, from scripting to no-code software, associated costs, and legality and ethical terms.

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Mục lục

What Types of Data Can Be Extracted From Twitter?
Legality and Ethical Terms of Use
General Guide for Scraping Twitter
Twitter Scraping Tools and Methods
Advanced Scraping Techniques and Challenges
Câu hỏi thường gặp (FAQ)
Kết luận

What Types of Data Can Be Extracted From Twitter?

You can extract different types of Twitter data. Here are three main data types for Twitter scraping:

Tweets: You can capture specific data from filtered tweets based on profiles, such as their likes, replies, retweets, and specified URLs.
User Profiles: Anything from a public user profile is collectible, such as the user’s bio, profile description, number of tweets, retweets, number of followers/ followings, and profile image.
Keywords/Hashtags: You can collect tweets containing particular keywords, hashtags, or their combination. Refining your search by the number of likes or by looking up specific dates and times is also possible.

Legality and Ethical Terms of Use

When diving into the world of data scraping, it is essential to understand the legal and ethical boundaries involved.

According to the Twitter terms and regulations (Developer Agreement and Policy), scraping data without explicit permission is prohibited and declared by Twitter policy: “Scraping the Services without the prior consent of Twitter is expressly prohibited.

Any abuse of the Twitter API for these purposes will be subject to enforcement action, which may include suspension and termination of access.

General Guide for Scraping Twitter

After a brief introduction to Twitter scraping, it’s time to explore the process of scraping through Twitter data. Thus, we’ve compiled a simple and comprehensive guide to Twitter scraping for you. Please follow the steps below:

First, you need to have the right scraping tools. There are plenty of options to choose from. So, determine which option suits your budget and preferences.
Download and install the scraping tool on your system.
Make sure there is plenty of storage space available on your device and that you have a reliable internet connection.
After installing, log in using your Twitter account details.
Adjusting parameters for scraping data from Twitter is an important step that allows you to extract data based on keywords, hashtags, dates and times, locations, URLs, etc.
After executing the scraper tool, a large amount of data will be left behind. You can export the data to different file formats (xlsx, CSV, JSON, etc.).
In the final step, you should analyze the exported data to gain insights into your topic of interest.

Twitter Scraping Tools and Methods

We have reviewed some available scraping tools across the internet, from the official Twitter scraper to third-party services and even open-source Python libraries, and listed them below.

4.1. API-Based Twitter Scrapers

The first method that we are going to take a look at is API-based Twitter scrapers, which include Twitter API V2, Apify, Brightdata, and Scrapingdog.

4.1.1. Twitter API V2

Twitter API v2 is the latest version of Twitter`s API, the official and one of the most commonly used APIs for developers building apps with social interaction or researchers/individuals who collect data for their specific purpose. The use of new APIs enables the effortless monitoring and analysis of live conversations on social networks.

Recently, Twitter has added some new features, such as endpoints, payload options for tweet posts, conversation identifier sets, and annotations. These changes are quite impressive. However, the new pricing structure has raised serious concerns for developers and third-party apps. With the new pricing structure, access to services has dramatically decreased, and prices have risen drastically.

The Twitter/X API v2 pricing plans have three levels: Free, Basic, and Enterprise.

In the free tier, developers can post up to 1500 tweets per month, designed for write-only use and testing the Twitter API.
The basic tier costs $100 per month and allows developers to post up to 3,000 tweets per month at the user level and 50,000 tweets (with a read limit of 10,000) at the app level.
The Enterprise tire includes more advanced features designed for businesses. However, the enterprise plan will charge developers/businesses an exorbitant price of nearly 42000$ a month.

4.1.2. Apify

Through Apify’s Twitter Scraper, you can extract information from publicly available Twitter data such as hashtags, threads, replies, images, and more. Recent changes to Twitter have put new limits on viewing and scraping tweets on this platform, as users will only extract public information up to 100 tweets per profile. This scraper cannot scrape the latest tweets but can retrieve the most liked ones. Extracted data can be accessible in HTML, JSON, Excel, and CSV formats.

The following figure illustrates the monthly service costs by Apify. It also offers a 10% discount for the annual plan. For more information, visit Apify pricing.

4.1.3. Brightdata

Bright Data is a data collection platform that offers web scraping tools such as proxy servers, APIs, and no-code solutions. Bright Data’s Web Scraper gives users the ability to extract data from public Twitter profiles, including images, videos, tweets, hashtags, and more.

Prices begin with a monthly 500$ for 151000 page loads. Bright Data Twitter scraper data collector is compatible with all web services and outputs its data in Excel format. It also offers a 7-day trial, and you can test the platform before paying 500 bucks.

Ready to take your Twitter scraper to the next level?

Try IPv6 proxies

4.1.4. Scrapingdog

Scrapingdog is a web scraping API that helps you scrape any website, including Twitter. It allows you to scrape tweets using tweet IDs or scrape public pages to extract details such as number of followers, number of followers, and website links.

It costs you 0.0009$ per page to scrape Twitter in the standard plan, which is among the best value over price compared to the other top Twitter scrapers. They have also provided a free trial; you can cancel your subscription anytime and refund your money easily. For more information about how to scrape data using Scrapingdog, you can visit Twitter Scraping API documentation.

4.2. Python Libraries and Packages for Scraping Twitter

Now that you are familiar with the Twitter API and apps like Apify, it is time to take a look at the Python libraries and packages for Twitter scraping.

4.2.1. Tweepy

Tweepy is an open-source Python package that allows developers to access Twitter endpoints smoothly and transparently. However, you should be aware that Twitter has imposed limitations on the number of requests sent to the X/Twitter API, where 900 requests are allowed every 15 minutes. In this section, we aim to take a look at Tweepy’s functionality and give a simple example.

To begin, install the Tweepy package using the “pip install Tweepy” command on your Python IDE and then import Tweepy as well. Registering your client application with Twitter is the next step. Create a new application. Once completed, you will receive a bearer token.

pip install tweepy
import tweepy

1 2	pip install tweepy import tweepy

Next, you must create a “Client” instance to pass the consumer bearer token you have gotten from the Twitter API.

In the query variable, we specified a field, a mention, and a hashtag as demonstrated.

client = tweepy.Client(bearer_token='bearer_token')
query = 'query @mentions #hashtags'

client = tweepy.Client(bearer_token='bearer_token')

query = 'query @mentions #hashtags'

To search for tweets from the past seven days, you can use the search_recent_tweets feature available in Tweepy. To specify the data you’re looking for, you need to pass a search query.

recent_tweets = client.search_recent_tweets(query=query, tweet_fields=&#91;'tweet_field_1’, 'tweet_field_2'], max_results=100)

1 2	recent_tweets = client.search_recent_tweets(query=query, tweet_fields=['tweet_field_1’, 'tweet_field_2'], max_results=100)

If you have access to the academic research product track, you can retrieve tweets older than 7 days. From the complete archive of publicly available tweets.

tweets = client.search_all_tweets(query=query, tweet_fields=&#91;'tweet_field_1’, 'tweet_field_2'], max_results=100)

1 2	tweets = client.search_all_tweets(query=query, tweet_fields=['tweet_field_1’, 'tweet_field_2'], max_results=100)

You can export the results using the following code.

for tweet in tweets.data:
    print(tweet.text)
    if len(tweet.context_annotations) &gt; 0:
        print(tweet.context_annotations)

for tweet in tweets.data:

print(tweet.text)

if len(tweet.context_annotations) > 0:

print(tweet.context_annotations)

There are also plenty of functions in Tweepy capable of performing various tasks in more complex and specific cases.

4.2.2. Snscrape

Another way to get information from Twitter without relying on an API is through Snscrape. It allows you to retrieve basic information like user profiles, tweet content, sources, etc. Unlike Tweepy, there are no limits on the number of tweets you can scrape or the dates of tweets, and you can extract old Twitter data. Since Snscrape is not connected to the Twitter API, it lacks functionality at the level of Tweepy. Check our complete guide to Snscrape.

In this section, we also review a basic example of scraping some data from Twitter using Snscrape in Python.

First, you should install Snscrape. Note that you must have Python 3.8 or higher installed to make it work.

pip install snscrape

1 2	pip install snscrape

In the next step, install the following libraries.

import snscrape.modules.twitter as sntwitter
import pandas as pd

import snscrape.modules.twitter as sntwitter

import pandas as pd

We send a query (in our case, “query”) using the “TwitterSearchScraper(query).get_items” function and obtain elements from the search just like the results from the Twitter search bar.

query = "query"
 
for tweet in sntwitter.TwitterSearchScraper(query).get_items():
   print(vars(tweet))
   break

query = "query"

for tweet in sntwitter.TwitterSearchScraper(query).get_items():

print(vars(tweet))

break

There are other methods that can be used for scraping data from Twitter, such as: TwitterSearchScraper, TwitterUserScraper, TwitterProfileScraper, TwitterHashtagScraper, TwitterTweetScraperMode, TwitterTweetScraper, TwitterListPostsScraper, TwitterTrendsScraper.

Advanced Scraping Techniques and Challenges

When basic scraping methods hit their limits with Twitter’s sophisticated defenses, advanced techniques become essential. Twitter’s modern interface relies heavily on JavaScript rendering and implements multiple layers of bot detection, making traditional HTTP requests insufficient for reliable data collection.

Handling Dynamic Content with Headless Browsers

Twitter’s timeline loads dynamically through JavaScript, meaning the content you see isn’t present in the initial HTML response. Headless browsers simulate real user interactions, rendering JavaScript and handling dynamic content loading.

Playwright vs Selenium: Playwright offers better performance and more reliable handling of modern web applications, while Selenium remains the established choice with extensive community support.

Here’s a practical example using Playwright to scrape dynamically loaded tweets:

import asyncio
from playwright.async_api import async_playwright
import json

async def scrape_twitter_profile(username):
    async with async_playwright() as p:
        # Launch headless browser
        browser = await p.chromium.launch(headless=True)
        context = await browser.new_context(
            user_agent="Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"
        )
        
        page = await context.new_page()
        
        try:
            # Navigate to profile
            await page.goto(f"https://twitter.com/{username}")
            
            # Wait for tweets to load
            await page.wait_for_selector('&#91;data-testid="tweet"]', timeout=10000)
            
            # Scroll to load more tweets
            for i in range(3):
                await page.evaluate("window.scrollTo(0, document.body.scrollHeight)")
                await page.wait_for_timeout(2000)
            
            # Extract tweet data
            tweets = await page.evaluate("""
                () =&gt; {
                    const tweetElements = document.querySelectorAll('&#91;data-testid="tweet"]');
                    return Array.from(tweetElements).map(tweet =&gt; {
                        const textElement = tweet.querySelector('&#91;data-testid="tweetText"]');
                        const timeElement = tweet.querySelector('time');
                        return {
                            text: textElement ? textElement.innerText : '',
                            timestamp: timeElement ? timeElement.getAttribute('datetime') : '',
                            url: window.location.href
                        };
                    });
                }
            """)
            
            return tweets
            
        except Exception as e:
            print(f"Error scraping {username}: {e}")
            return &#91;]
        
        finally:
            await browser.close()

# Usage
tweets = asyncio.run(scrape_twitter_profile("elonmusk"))
print(json.dumps(tweets&#91;:3], indent=2))

import asyncio

from playwright.async_api import async_playwright

import json

async def scrape_twitter_profile(username):

async with async_playwright() as p:

# Launch headless browser

browser = await p.chromium.launch(headless=True)

context = await browser.new_context(

user_agent="Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"

)

page = await context.new_page()

try:

# Navigate to profile

await page.goto(f"https://twitter.com/{username}")

# Wait for tweets to load

await page.wait_for_selector('[data-testid="tweet"]', timeout=10000)

# Scroll to load more tweets

for i in range(3):

await page.evaluate("window.scrollTo(0, document.body.scrollHeight)")

await page.wait_for_timeout(2000)

# Extract tweet data

tweets = await page.evaluate("""

() => {

const tweetElements = document.querySelectorAll('[data-testid="tweet"]');

return Array.from(tweetElements).map(tweet => {

const textElement = tweet.querySelector('[data-testid="tweetText"]');

const timeElement = tweet.querySelector('time');

return {

text: textElement ? textElement.innerText : '',

timestamp: timeElement ? timeElement.getAttribute('datetime') : '',

url: window.location.href

};

});

}

""")

return tweets

except Exception as e:

print(f"Error scraping {username}: {e}")

return []

finally:

await browser.close()

# Usage

tweets = asyncio.run(scrape_twitter_profile("elonmusk"))

print(json.dumps(tweets[:3], indent=2))

Key advantages of headless browsers include handling JavaScript rendering, managing cookies and sessions automatically, and bypassing basic bot detection through realistic browser fingerprints.

Resource considerations: Headless browsers consume significantly more memory and CPU compared to simple HTTP requests. For large-scale operations, consider running multiple browser instances across different servers or using browser pools.

Bypassing Anti-Scraping Measures

Twitter employs sophisticated bot detection that goes beyond simple rate limiting. Understanding these measures helps develop effective countermeasures.

Common Anti-Scraping Techniques

Rate Limiting: Twitter monitors request frequency per IP address, implementing both short-term (requests per minute) and long-term (daily quotas) limits. Learn more in Web Scraping Rate Limiting: The Fix.

IP Blocking: Suspicious IP addresses get temporarily or permanently blocked. Datacenter IPs face more scrutiny than residential addresses.

CAPTCHA Challenges: Automated CAPTCHA presentation when bot-like behavior is detected. Modern CAPTCHAs use behavioral analysis beyond simple image recognition.

Browser Fingerprinting: Analysis of browser characteristics including user agent, screen resolution, installed plugins, and JavaScript execution patterns.

Effective Countermeasures

Proxy Rotation Strategy: Using services like RapidSeedbox provides access to residential IP pools that appear as legitimate user traffic. Residential proxies from their 6.9+ million IP network significantly reduce detection compared to datacenter proxies.

import random
import time
from itertools import cycle

# Proxy rotation setup
proxy_list = &#91;
    "http://user:pass@proxy1.rapidseedbox.com:8080",
    "http://user:pass@proxy2.rapidseedbox.com:8080",
    "http://user:pass@proxy3.rapidseedbox.com:8080"
]

proxy_cycle = cycle(proxy_list)

def get_next_proxy():
    return next(proxy_cycle)

# Implement in your scraper
async def scrape_with_rotation():
    for i in range(10):
        proxy = get_next_proxy()
        # Configure your browser/session with new proxy
        # Perform scraping request
        # Add random delay
        await asyncio.sleep(random.uniform(5, 15))

import random

import time

from itertools import cycle

# Proxy rotation setup

proxy_list = [

"http://user:[email protected]:8080",

"http://user:[email protected]:8080"

]

proxy_cycle = cycle(proxy_list)

def get_next_proxy():

return next(proxy_cycle)

# Implement in your scraper

async def scrape_with_rotation():

for i in range(10):

proxy = get_next_proxy()

# Configure your browser/session with new proxy

# Perform scraping request

# Add random delay

await asyncio.sleep(random.uniform(5, 15))

User Agent Rotation: Vary browser signatures to avoid pattern detection. Use real user agent strings from different browsers and operating systems.

USER_AGENTS = &#91;
    "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36",
    "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36",
    "Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:121.0) Gecko/20100101 Firefox/121.0"
]

def get_random_user_agent():
    return random.choice(USER_AGENTS)

USER_AGENTS = [

"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36",

"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36",

"Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:121.0) Gecko/20100101 Firefox/121.0"

]

def get_random_user_agent():

return random.choice(USER_AGENTS)

Behavioral Patterns: Mimic human browsing behavior with variable delays, realistic scrolling patterns, and occasional non-scraping activities.

Session Management: Maintain consistent sessions with proper cookie handling and avoid creating too many new sessions from the same IP.

Advanced Detection Avoidance

Request Timing: Implement exponential backoff when encountering rate limits. Start with longer delays and gradually reduce them based on success rates.

Geolocation Consistency: When using proxies, ensure your requests maintain geographic consistency. Don’t jump between countries rapidly.

Browser Fingerprint Management: Use tools like undetected-chromedriver or stealth plugins to reduce browser fingerprinting effectiveness.

from selenium import webdriver
from selenium_stealth import stealth

def create_stealth_driver():
    options = webdriver.ChromeOptions()
    options.add_argument("--headless")
    options.add_argument("--no-sandbox")
    options.add_argument("--disable-dev-shm-usage")
    
    driver = webdriver.Chrome(options=options)
    
    stealth(driver,
        languages=&#91;"en-US", "en"],
        vendor="Google Inc.",
        platform="Win32",
        webgl_vendor="Intel Inc.",
        renderer="Intel Iris OpenGL Engine",
        fix_hairline=True,
    )
    
    return driver

from selenium import webdriver

from selenium_stealth import stealth

def create_stealth_driver():

options = webdriver.ChromeOptions()

options.add_argument("--headless")

options.add_argument("--no-sandbox")

options.add_argument("--disable-dev-shm-usage")

driver = webdriver.Chrome(options=options)

stealth(driver,

languages=["en-US", "en"],

vendor="Google Inc.",

platform="Win32",

webgl_vendor="Intel Inc.",

renderer="Intel Iris OpenGL Engine",

fix_hairline=True,

)

return driver

Error Handling: Implement graceful error handling that can distinguish between temporary blocks, permanent bans, and technical issues.

The combination of residential proxies from services like RapidSeedbox, proper browser stealth techniques, and realistic behavioral patterns creates a robust foundation for large-scale Twitter data collection while minimizing detection risks.

Remember that Twitter’s anti-bot measures continue evolving, so successful scraping requires ongoing adaptation of these techniques based on current platform behavior.

Câu hỏi thường gặp (FAQ)

Is it legal to scrape Twitter?

Twitter scraping exists in a legal gray area. While Twitter’s Terms of Service prohibit automated data collection, scraping publicly available data isn’t automatically illegal.
Legal risks include:
1. Copyright infringement on user content
2. CFAA violations in some jurisdictions
3. GDPR compliance issues with personal data
Safer approach: Use Twitter’s official API when possible, consult legal counsel for large projects, and focus on publicly available data for legitimate research purposes.

Do I need to know how to code to scrape X?

No, but coding provides better results.
No-code options: Browser automation tools and visual workflow builders work for basic scraping but are limited in speed and flexibility.
Coding solutions: Python with Selenium or specialized libraries offers greater control, better anti-bot handling, and higher success rates.
Best approach: Start with no-code tools to test your needs, then learn basic Python scripting for serious data collection.

What are the limitations of the free Twitter API?

Twitter’s free API tier has severe restrictions:
1. Rate limits: Very low monthly request quotas
2. Historical data: Limited to recent tweets (typically past week)
3. Tính năng: No advanced analytics or engagement metrics
4. Access: Requires application approval
Most research and business use cases exceed free tier limits, making paid plans or alternative methods necessary.

How can I scrape old tweets?

Historical Twitter data requires specialized tools since regular browsing only shows recent content.
Best tool: Snscrape – Python library accessing tweets from years back with date range filtering.
Other options:
1. TwitterScraper library
2. Academic Research API (institutional access required)
3. Third-party historical data services
Tip: Historical scraping is slower and requires careful rate limiting to avoid blocks.

What are the best practices for ethical Twitter scraping?

Essential practices:
1. Giới hạn tốc độ tải: 1-2 seconds between requests minimum
2. Respect robots.txt: Follow platform guidelines
3. Data minimization: Collect only necessary information
4. Proper delays: Use IP rotation and residential proxies
5. Error handling: Stop scraping if blocked or rate-limited
Key principle: Always try official APIs first, then scrape responsibly with respect for Twitter’s infrastructure and user privacy.

Kết luận

Twitter is a worthwhile source of sociological information across the web. By leveraging the information scraped from Twitter, you can tailor your plans to boost your sales and improve your marketing strategies. In this article, we have presented an in-depth overview of different aspects and methods of Twitter scraping for extracting data that can be valuable to businesses or research.

To sum up, according to the new limitations imposed on Twitter API v2, along with high costs, selecting the best scraper would be challenging. You can benefit from more advanced features on the Twitter API or third-party apps and Python libraries (Tweepy) that are directly connected to the Twitter API.

But the number of requests you can make is strictly limited. On the other hand, if you seek to scrape publicly available data and the basic features satisfy your needs, options like the Snscrape Python library can be a great choice.

Twitter (X) Scraping in 2026: The Ultimate Guide

Mục lục

What Types of Data Can Be Extracted From Twitter?

Legality and Ethical Terms of Use

General Guide for Scraping Twitter

Twitter Scraping Tools and Methods

4.1. API-Based Twitter Scrapers

4.1.1. Twitter API V2

4.1.2. Apify

4.1.3. Brightdata

Ready to take your Twitter scraper to the next level?

4.1.4. Scrapingdog

4.2. Python Libraries and Packages for Scraping Twitter

4.2.1. Tweepy

4.2.2. Snscrape

Advanced Scraping Techniques and Challenges

Handling Dynamic Content with Headless Browsers

Bypassing Anti-Scraping Measures

Common Anti-Scraping Techniques

Effective Countermeasures

Advanced Detection Avoidance

Câu hỏi thường gặp (FAQ)

Kết luận

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