What is parsing

Date:   8/31/2024 5:11:32 AM ( 26 h ago)

Parsing is an automated collection and systematization of information from open sources using scripts. Another name for this process is web scraping.

Scripts that collect and systematize information are called parsers. They work like this:

• search for sources using specified parameters - for example, you can give the parser a list of sites, and it will find pages with prices on them;
• extract the necessary information from sources - a few lines of text, a link or an amount;
• transform information - for example, a parser can take a fragment from  an HTML documentand transform it into text without code;
• save information in the required format - for example, as a list or table in Excel.

How advise  parsers work in different programming languages ​​- Python, javascr1pt, PHP 5 and others. You can read more about the principles of the javascr1pt parser here.

The point of parsing is to speed up routine work. To collect and save a list of a thousand articles on a site in a table, a person will spend hours. A parser will do this work in a few minutes. A parser speeds up work hundreds of times and makes fewer mistakes than a person.

How to Use Proxies from Proxy Sites to Scrap Twitter

Proxy sites know from experience that social media scraping is one of the main reasons to turn to proxy rental. Twitter plays a primary role here as a source of data for research agencies, marketing managers, network experts, etc.

Collecting this information at scale (a process known as web scraping) can be a time-consuming, resource-intensive, and challenging task. This is due to Twitter’s limitations on parsing speed and data volumes, as well as its cybersecurity mechanisms. Buying a proxy for a specific city is a way to bypass these obstacles, helping to optimize and automate scraping sessions.

What are proxies for social networks for?

Proxies act as intermediate links between the computer and the external environment. When users send a request for data from social networks through them, it first gets to the proxy and only then goes to Twitter. The same story happens with the response from the server. The data is sent to geotargeted proxies, which then send it to the PC. This maneuver gives a lot of advantages in web scraping.

Renting a proxy for social networks masks IP, provides privacy, and gives a chance to cope with Twitter's limits on requests and data processing. It is doubly practical if the proxy site can provide IP rotation for web scraping automation programs. Thanks to this, people can administer several parallel "machine" requests and avoid detection by Twitter.

To summarize the criteria for selecting proxies for social networks in general and Twitter in particular, we indicate:

• Renting proxies is worth the money if the former are taken from IP whitelists, have sufficient speed and provide a choice of locations;
• It makes sense to buy a proxy of a specific city with subsequent rotation of IP addresses to reduce the risk of restrictions during the scraping process; 
• It would be irrational to work with free proxies, as they are too slow and unsafe for important tasks.

Scraping options when renting proxies

Once you've purchased your Twitter proxies, it's time to configure them for your web scraping tool of choice. The details vary, but you'll generally need to find the right combination of IPs, ports, and names for your particular scraper.

Here are the standard options:

1. If you choose Python-based Scrapy, you can use middleware to manage the proxy. Enter the proxy specifications in the Scrapy settings file, and then Scrapy will automatically pass the request based on that;
2. Another way is to write a personal scraper using BeautifulSoup from the same Python arsenal. Write the route to Twitter accounts, all HTML elements with the required information (for example, the tweets themselves, followers and likes) and a way to download and save the results of the scraping session;
3. Some rely on Twitter's own API. This option is tempting, but it's best considered with caution. It's a convenient path to structured datasets, but it's expensive and doesn't yield much.

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URL:   http://www.curezone.org/blogs/fm.asp?i=2466250

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