Implementation of Scrapy framework to crawl Twitter data

Implementation of Scrapy framework for crawling Twitter data

With the development of the Internet, social media has become one of the platforms widely used by people. As one of the largest social networks in the world, Twitter generates massive amounts of information every day. Therefore, how to use existing technical means to effectively obtain and analyze data on Twitter has become particularly important.

Scrapy is a Python open source framework designed to crawl and extract data on specific websites. Compared with other similar frameworks, Scrapy has higher scalability and adaptability, and can well support large social network platforms such as Twitter. This article will introduce how to use the Scrapy framework to crawl Twitter data.

1. Set up the environment

Before starting the crawling work, we need to configure the Python environment and Scrapy framework. Taking the Ubuntu system as an example, you can use the following command to install the required components:

sudo apt-get update && sudo apt-get install python-pip python-dev libxml2-dev libxslt1-dev zlib1g-dev libffi-dev libssl-dev
sudo pip install scrapy

2. Create project

The first step to use the Scrapy framework to crawl Twitter data is to create A Scrapy project. Enter the following command in the terminal:

scrapy startproject twittercrawler

This command will create a project folder named "twittercrawler" in the current directory, which includes some automatically generated files and folders.

3. Configuration project

Open the Scrapy project and we can see a file named "settings.py". This file contains various crawler configuration options, such as crawler delay time, database settings, request headers, etc. Here, we need to add the following configuration information:

ROBOTSTXT_OBEY = False
USER_AGENT = 'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/68.0.3440.106 Safari/537.36'
DOWNLOAD_DELAY = 5
CONCURRENT_REQUESTS = 1

The function of these configuration options is:

• ROBOTSTXT_OBEY: Indicates whether to follow the robots.txt protocol, set here to False, do not follow this agreement.
• USER_AGENT: Indicates the browser type and version used by our crawler.
• DOWNLOAD_DELAY: Indicates the delay time of each request, which is set to 5 seconds here.
• CONCURRENT_REQUESTS: Indicates the number of requests sent at the same time. It is set to 1 here to ensure stability.

4. Create a crawler

In the Scrapy framework, each crawler is implemented through a class called "Spider". In this class, we can define how to crawl and parse web pages and save them locally or in a database. In order to crawl data on Twitter, we need to create a file called "twitter_spider.py" and define the TwitterSpider class in it. The following is the code of TwitterSpider:

import scrapy
from scrapy.http import Request

class TwitterSpider(scrapy.Spider):
    name = 'twitter'
    allowed_domains = ['twitter.com']
    start_urls = ['https://twitter.com/search?q=python']

    def __init__(self):
        self.headers = {
            'Accept-Encoding': 'gzip, deflate, br',
            'Accept-Language': 'en-US,en;q=0.5',
            'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/68.0.3440.106 Safari/537.36',
            'X-Requested-With': 'XMLHttpRequest'
        }

    def parse(self, response):
        for tweet in response.xpath('//li[@data-item-type="tweet"]'):
            item = {}
            item['id'] = tweet.xpath('.//@data-item-id').extract_first()
            item['username'] = tweet.xpath('.//@data-screen-name').extract_first()
            item['text'] = tweet.xpath('.//p[@class="TweetTextSize js-tweet-text tweet-text"]//text()').extract_first()
            item['time'] = tweet.xpath('.//span//@data-time').extract_first()
            yield item

        next_page = response.xpath('//a[@class="js-next-page"]/@href').extract_first()
        if next_page:
            url = response.urljoin(next_page)
            yield Request(url, headers=self.headers, callback=self.parse)

In the TwitterSpider class, we specify the domain name and starting URL of the website to be crawled. In the initialization function, we set the request header to avoid being restricted by anti-crawlers. In the parse function, we use XPath expressions to parse the obtained web pages one by one and save them into a Python dictionary. Finally, we use the yield statement to return the dictionary so that the Scrapy framework can store it locally or in a database. In addition, we also use a simple recursive function to process the "next page" of Twitter search results, which allows us to easily obtain more data.

5. Run the crawler

After we finish writing the TwitterSpider class, we need to return to the terminal, enter the "twittercrawler" folder we just created, and run the following command to Start the crawler:

scrapy crawl twitter -o twitter.json

This command will start the crawler named "twitter" and save the results to a file named "twitter.json".

6. Conclusion

So far, we have introduced how to use the Scrapy framework to crawl Twitter data. Of course, this is just the beginning, we can continue to extend the TwitterSpider class to obtain more information, or use other data analysis tools to process the obtained data. By learning the use of the Scrapy framework, we can process data more efficiently and provide more powerful support for subsequent data analysis work.

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