Web scraper robot – Robocorp
Get the code and run this example in your favorite editor on our Portal! When run, the robot will:open a real web browserhide distracting UI elementsscroll down to load dynamic contentcollect the latest tweets by a given Twitter usercreate a file system directory by the name of the Twitter userstore the text content of each tweet in separate files in the directorystore a screenshot of each tweet in the directoryBecause Twitter blocks requests coming from the cloud, this robot can only be executed on a local machine or triggered from Control Room using Robocorp script# ## Twitter web scraper example
# Opens the Twitter web page and stores some content.
*** Settings ***
Documentation Opens the Twitter web page and stores some content.
Library Collections
Library lenium
Library leSystem
Library botLogListener
*** Variables ***
${USER_NAME}= RobocorpInc
${NUMBER_OF_TWEETS}= 3
${TWEET_DIRECTORY}= ${CURDIR}${/}output${/}tweets/${USER_NAME}
${TWEETS_LOCATOR}= xpatharticle[descendant::span[contains(text(), “\@${USER_NAME}”)]]
*** Keywords ***
Open Twitter homepage
Open Available Browser {USER_NAME}
Wait Until Element Is Visible css:main
Hide element
[Arguments] ${locator}
Mute Run On Failure Execute Javascript
Run Keyword And Ignore Error… Execute Javascript… document. querySelector(‘${locator}’) = ‘none’
Hide distracting UI elements
@{locators}= Create List… header… \#layers > div… nav… div[data-testid=”primaryColumn”] > div > div… div[data-testid=”sidebarColumn”]
FOR ${locator} IN @{locators}
Hide element ${locator}
END
Scroll down to load dynamic content
FOR ${pixels} IN RANGE 200 2000 200
Execute Javascript rollBy(0, ${pixels})
Sleep 500ms
Execute Javascript rollTo(0, 0)
Get tweets
Wait Until Element Is Visible ${TWEETS_LOCATOR}
@{all_tweets}= Get WebElements ${TWEETS_LOCATOR}
@{tweets}= Get Slice From List ${all_tweets} 0 ${NUMBER_OF_TWEETS}
[Return] @{tweets}
Store the tweets
Create Directory ${TWEET_DIRECTORY} parents=True
${index} = Set Variable 1
@{tweets}= Get tweets
FOR ${tweet} IN @{tweets}
${screenshot_file}= Set Variable ${TWEET_DIRECTORY}/tweet-${index}
${text_file}= Set Variable ${TWEET_DIRECTORY}/tweet-${index}
${text}= Set Variable ${nd_element_by_xpath(“. //div[@lang=’en’]”)}
Capture Element Screenshot ${tweet} ${screenshot_file}
Create File ${text_file} ${text} overwrite=True
${index} = Evaluate ${index} + 1
*** Tasks ***
Store the latest tweets by given user name
[Teardown] Close Browser
The robot should have created a directory output/tweets/RobocorpInc containing images (screenshots of the tweets) and text files (the texts of the tweets) script explainedTasksThe main robot file () contains the tasks your robot is going to complete when run:*** Tasks ***
*** Tasks *** is the section the latest tweets by given user name is the name of the Twitter homepage, etc., are keyword calls. [Teardown] Close Browser ensures that the browser will be closed even if the robot fails to accomplish its ttings*** Settings ***
The *** Settings *** section provides short Documentation for the script and imports libraries (Library) that add new keywords for the robot to use. Libraries contain Python code for things like commanding a web browser (lenium) or creating file system directories and files (leSystem). Variables*** Variables ***
Variables provide a way to change the input values for the robot in one place. This robot provides variables for the Twitter user name, the number of tweets to collect, the file system directory path for storing the tweets, and a locator for finding the tweet HTML elements from the Twitter web ywordsThe Keywords section defines the implementation of the actual things the robot will do. Robocorp Lab’s Notebook mode works best when each keyword is annotated with the *** Keywords *** heading. *** Keywords ***
The Open Twitter homepage keyword uses the Open Available Browser keyword from the lenium library to open a browser. It takes one required argument; the URL to open:{USER_NAME}
The ${USER_NAME} variable is defined in the *** Variables *** section:${USER_NAME}= RobocorpInc
The value of the variable is RobocorpInc. When the robot is executed, Robot Framework replaces the variable with its value, and the URL becomes ** Keywords ***
The Hide element keyword takes care of getting rid of unnecessary elements from the web page when taking screenshots. The Mute Run On Failure keyword from the botLogListener library prevents the robot from saving a screenshot in case of failure (the default behavior on failure) when executing the Execute Javascript keyword. In this case, we are not really interested in these failures, so we decided to mute the failure Execute Javascript keyword executes the given JavaScript in the browser. The JavaScript expression contains a variable (${locator}) that is passed in as an argument for the Hide element keyword. *** Keywords ***
The Hide distracting UI elements keyword calls the Hide element keyword with locators pointing to all the elements we want the robot to hide from the web page. A for loop is used to loop through the list of locators. *** Keywords ***
The Scroll down to load dynamic content keyword ensures that the dynamic content is loaded before the robot tries to store the tweets. It scrolls down the browser window, starting from 200 pixels from the top of the page, until 2000 pixels down, in 200-pixel steps. The Sleep keyword provides some time for the dynamic content to load. Finally, the web page is scrolled back to the top. *** Keywords ***
The Get tweets keyword collects the tweet HTML elements from the web page using the Get WebElements keyword. The Get Slice From List keyword is used to limit the number of elements before returning them using the [Return] keyword. *** Keywords ***
The Store the tweets keyword stores the text and the screenshot of each tweet. It uses the Create Directory keyword to create a file system directory, the Set Variable keyword to create local variables, the Capture Element Screenshot keyword to take the screenshots, the Create File keyword to create the files, and the Evaluate keyword to evaluate a Python file paths are constructed dynamically using variables:${TWEET_DIRECTORY}/tweet-${index}
SummaryYou executed a web scraper robot, congratulations! During the process, you learned some concepts and features of the Robot Framework and some good practices:Defining Settings for your script (*** Settings ***)Documenting scripts (Documentation)Importing libraries (Collections, lenium, leSystem, botLogListener)Using keywords provided by libraries (Open Available Browser)Creating your own keywordsDefining arguments ([Arguments])Calling keywords with argumentsReturning values from keywords ([Return])Using predefined variables (${CURDIR})Using your own variablesCreating loops with Robot Framework syntaxRunning teardown steps ([Teardown])Opening a real browserNavigating to web pagesLocating web elementsHiding web elementsExecuting Javascript codeScraping text from web elementsTaking screenshots of web elementsCreating file system directoriesCreating and writing to filesIgnoring errors when it makes sense (Run Keyword And Ignore Error)June 28, 2021
Is Web Scraping Illegal? Depends on What the Meaning of the Word Is
Depending on who you ask, web scraping can be loved or hated.
Web scraping has existed for a long time and, in its good form, it’s a key underpinning of the internet. “Good bots” enable, for example, search engines to index web content, price comparison services to save consumers money, and market researchers to gauge sentiment on social media.
“Bad bots, ” however, fetch content from a website with the intent of using it for purposes outside the site owner’s control. Bad bots make up 20 percent of all web traffic and are used to conduct a variety of harmful activities, such as denial of service attacks, competitive data mining, online fraud, account hijacking, data theft, stealing of intellectual property, unauthorized vulnerability scans, spam and digital ad fraud.
So, is it Illegal to Scrape a Website?
So is it legal or illegal? Web scraping and crawling aren’t illegal by themselves. After all, you could scrape or crawl your own website, without a hitch.
Startups love it because it’s a cheap and powerful way to gather data without the need for partnerships. Big companies use web scrapers for their own gain but also don’t want others to use bots against them.
The general opinion on the matter does not seem to matter anymore because in the past 12 months it has become very clear that the federal court system is cracking down more than ever.
Let’s take a look back. Web scraping started in a legal grey area where the use of bots to scrape a website was simply a nuisance. Not much could be done about the practice until in 2000 eBay filed a preliminary injunction against Bidder’s Edge. In the injunction eBay claimed that the use of bots on the site, against the will of the company violated Trespass to Chattels law.
The court granted the injunction because users had to opt in and agree to the terms of service on the site and that a large number of bots could be disruptive to eBay’s computer systems. The lawsuit was settled out of court so it all never came to a head but the legal precedent was set.
In 2001 however, a travel agency sued a competitor who had “scraped” its prices from its Web site to help the rival set its own prices. The judge ruled that the fact that this scraping was not welcomed by the site’s owner was not sufficient to make it “unauthorized access” for the purpose of federal hacking laws.
Two years later the legal standing for eBay v Bidder’s Edge was implicitly overruled in the “Intel v. Hamidi”, a case interpreting California’s common law trespass to chattels. It was the wild west once again. Over the next several years the courts ruled time and time again that simply putting “do not scrape us” in your website terms of service was not enough to warrant a legally binding agreement. For you to enforce that term, a user must explicitly agree or consent to the terms. This left the field wide open for scrapers to do as they wish.
Fast forward a few years and you start seeing a shift in opinion. In 2009 Facebook won one of the first copyright suits against a web scraper. This laid the groundwork for numerous lawsuits that tie any web scraping with a direct copyright violation and very clear monetary damages. The most recent case being AP v Meltwater where the courts stripped what is referred to as fair use on the internet.
Previously, for academic, personal, or information aggregation people could rely on fair use and use web scrapers. The court now gutted the fair use clause that companies had used to defend web scraping. The court determined that even small percentages, sometimes as little as 4. 5% of the content, are significant enough to not fall under fair use. The only caveat the court made was based on the simple fact that this data was available for purchase. Had it not been, it is unclear how they would have ruled. Then a few months back the gauntlet was dropped.
Andrew Auernheimer was convicted of hacking based on the act of web scraping. Although the data was unprotected and publically available via AT&T’s website, the fact that he wrote web scrapers to harvest that data in mass amounted to “brute force attack”. He did not have to consent to terms of service to deploy his bots and conduct the web scraping. The data was not available for purchase. It wasn’t behind a login. He did not even financially gain from the aggregation of the data. Most importantly, it was buggy programing by AT&T that exposed this information in the first place. Yet Andrew was at fault. This isn’t just a civil suit anymore. This charge is a felony violation that is on par with hacking or denial of service attacks and carries up to a 15-year sentence for each charge.
In 2016, Congress passed its first legislation specifically to target bad bots — the Better Online Ticket Sales (BOTS) Act, which bans the use of software that circumvents security measures on ticket seller websites. Automated ticket scalping bots use several techniques to do their dirty work including web scraping that incorporates advanced business logic to identify scalping opportunities, input purchase details into shopping carts, and even resell inventory on secondary markets.
To counteract this type of activity, the BOTS Act:
Prohibits the circumvention of a security measure used to enforce ticket purchasing limits for an event with an attendance capacity of greater than 200 persons.
Prohibits the sale of an event ticket obtained through such a circumvention violation if the seller participated in, had the ability to control, or should have known about it.
Treats violations as unfair or deceptive acts under the Federal Trade Commission Act. The bill provides authority to the FTC and states to enforce against such violations.
In other words, if you’re a venue, organization or ticketing software platform, it is still on you to defend against this fraudulent activity during your major onsales.
The UK seems to have followed the US with its Digital Economy Act 2017 which achieved Royal Assent in April. The Act seeks to protect consumers in a number of ways in an increasingly digital society, including by “cracking down on ticket touts by making it a criminal offence for those that misuse bot technology to sweep up tickets and sell them at inflated prices in the secondary market. ”
In the summer of 2017, LinkedIn sued hiQ Labs, a San Francisco-based startup. hiQ was scraping publicly available LinkedIn profiles to offer clients, according to its website, “a crystal ball that helps you determine skills gaps or turnover risks months ahead of time. ”
You might find it unsettling to think that your public LinkedIn profile could be used against you by your employer.
Yet a judge on Aug. 14, 2017 decided this is okay. Judge Edward Chen of the U. S. District Court in San Francisco agreed with hiQ’s claim in a lawsuit that Microsoft-owned LinkedIn violated antitrust laws when it blocked the startup from accessing such data. He ordered LinkedIn to remove the barriers within 24 hours. LinkedIn has filed to appeal.
The ruling contradicts previous decisions clamping down on web scraping. And it opens a Pandora’s box of questions about social media user privacy and the right of businesses to protect themselves from data hijacking.
There’s also the matter of fairness. LinkedIn spent years creating something of real value. Why should it have to hand it over to the likes of hiQ — paying for the servers and bandwidth to host all that bot traffic on top of their own human users, just so hiQ can ride LinkedIn’s coattails?
I am in the business of blocking bots. Chen’s ruling has sent a chill through those of us in the cybersecurity industry devoted to fighting web-scraping bots.
I think there is a legitimate need for some companies to be able to prevent unwanted web scrapers from accessing their site.
In October of 2017, and as reported by Bloomberg, Ticketmaster sued Prestige Entertainment, claiming it used computer programs to illegally buy as many as 40 percent of the available seats for performances of “Hamilton” in New York and the majority of the tickets Ticketmaster had available for the Mayweather v. Pacquiao fight in Las Vegas two years ago.
Prestige continued to use the illegal bots even after it paid a $3. 35 million to settle New York Attorney General Eric Schneiderman’s probe into the ticket resale industry.
Under that deal, Prestige promised to abstain from using bots, Ticketmaster said in the complaint. Ticketmaster asked for unspecified compensatory and punitive damages and a court order to stop Prestige from using bots.
Are the existing laws too antiquated to deal with the problem? Should new legislation be introduced to provide more clarity? Most sites don’t have any web scraping protections in place. Do the companies have some burden to prevent web scraping?
As the courts try to further decide the legality of scraping, companies are still having their data stolen and the business logic of their websites abused. Instead of looking to the law to eventually solve this technology problem, it’s time to start solving it with anti-bot and anti-scraping technology today.
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Prevent Web Scraping – A Step by Step Guide – DataDome
Who uses web scraper bots, and why?
Your content is gold, and it’s the reason visitors come to your website. Threat actors also want your gold, and use scraper bot attacks to gather and exploit your web content—to republish content with no overhead, or to undercut your prices automatically, for example.
Online retailers often hire professional web scrapers or use web scraping tools to gather competitive intelligence to craft future retail pricing strategies and product catalogs.
Threat actors try their best to disguise their bad web scraping bots as good ones, such as the ubiquitous Googlebots. DataDome identifies over 1 million hits per day from fake Googlebots on all customer websites.
Read more: TheFork (TripAdvisor) blocks scraping on its applications
The anatomy of a scraping attack
Scraping attacks contain three main phases:
Target URL address and parameter values: Web scrapers identify their targets and make preparations to limit scraping attack detection by creating fake user accounts, masking their malicious scraper bots as good ones, obfuscating their source IP addresses, and more.
Run scraping tools & processes: The army of scraper bots run on the target website, mobile app or API. The often intense level of bot traffic will often overload servers and result in poor website performance or even downtime.
Extract content and data: Web scrapers extract proprietary content and database records from the target and store it in their database for later analysis and abuse.
Figure 1: OAT-011 indicative diagram. Source: OWASP.
Common protection strategies against web scraping
Common anti crawler protection strategies include:
Monitoring new or existing user accounts with high levels of activity and no purchases
Detecting abnormally high volumes of product views as a sign of non-human activity
Tracking the activity of competitors for signs of price and product catalog matching
Enforcing site terms and conditions that stop malicious web scraping
Employing bot protection capabilities with deep behavioral analysis to pinpoint bad bots and prevent web scraping
Site owners commonly use “” files to communicate their intentions when it comes to scraping. files permit scraping bots to traverse specific pages; however, malicious bots don’t care about files (which serve as a “no trespassing” sign).
A clear, binding terms of use agreement that dictates permitted and non-permitted activity can potentially help in litigation. Check out our terms and conditions template for precise, enforceable anti-scraping wording.
Scrapers will do everything in their power to disguise scraping bots as genuine users. The ability to scrape publicly available content, register fake user accounts for malicious bots, and pass valid HTTP requests from randomly generated device IDs and IP addresses, deems traditional rule-based security measures, such as WAFs, ineffective against sophisticated scraping attacks.
How DataDome protects against website and content scraping
A good bot detection solution will be able to identify visitor behavior that shows signs of web scraping in real time, and automatically block malicious bots before scraping attacks unravel while maintaining a smooth experience for real human users. To correctly identify fraudulent traffic and block web scraping tools, a bot protection solution must be able to analyze both technical and behavioral data.
“Bots were scraping our website in order to steal our content and then sell it to third parties. Since we’ve activated the [DataDome bot] protection, web scraper bots are blocked and cannot access the website. Our data are secured and no longer accessible to bots. We are also now able to monitor technical logs in order to detect abnormal behaviors such as aggressive IP addresses or unusual queries. ”
Head of Technical Dept., Enterprise (1001-5000 employees)
DataDome employs a two-layer bot detection engine to help CTOs and CISOs protect their websites, mobile apps, and APIs from malicious scraping bots & block web scraping tools. It compares every site hit with a massive in-memory pattern database, and uses a blend of AI and machine learning to decide in less than 2 milliseconds whether to grant access to your pages or not.
DataDome is the only bot protection solution delivered as-a-service. It deploys in minutes on any web architecture, is unmatched in brute force attack detection speed and accuracy, and runs on autopilot. You will receive real-time notifications whenever your site is under scraping attack, but no intervention is required. Once you have set up a whitelist of trusted partner bots, DataDome will take care of all unwanted traffic and stop malicious bots from crawling your site in order to prevent website scraping.
Want to see is scraper bots are on your site? You can test your site today. (It’s easy & free. )
Frequently Asked Questions about robot web scraping
Is Web scraping legal?
So is it legal or illegal? Web scraping and crawling aren’t illegal by themselves. After all, you could scrape or crawl your own website, without a hitch. … Big companies use web scrapers for their own gain but also don’t want others to use bots against them.
What is a scraping robot?
Web scraping protection: How to protect your website against crawler and scraper bots. back to the blog. Web Scraping is an automated bot threat where cybercriminals collect data from your website for malicious purposes, such as content reselling, price undercutting, etc.
How do you bot a web site to scrape?
Here are the basic steps to build a crawler:Step 1: Add one or several URLs to be visited.Step 2: Pop a link from the URLs to be visited and add it to the Visited URLs thread.Step 3: Fetch the page’s content and scrape the data you’re interested in with the ScrapingBot API.More items…•Jun 17, 2020