Scraping XML Tables with R

A couple of my good friends also recently started a sports analytics blog. We’ve decided to collaborate on a couple of studies revolving around NBA data found at www.basketball-reference.com. This will be the first part of that project!

Data scientists need data. The internet has lots of data. How can I get that data into R? Scrape it!

People have been scraping websites for as long as there have been websites. It’s gotten pretty easy using R/Python/whatever other tool you want to use. This post shows how to use R to scrape the demographic information for all NBA and ABA players listed at www.basketball-reference.com.

Here’s the code:

###### Settings

library(XML)

 ###### URLs

url<-paste0("http://www.basketball-reference.com/players/",letters,"/")

len<-length(url)

 ###### Reading data

tbl<-readHTMLTable(url[1])[[1]]

 for (i in 2:len)

    {tbl<-rbind(tbl,readHTMLTable(url[i])[[1]])}

 ###### Formatting data

colnames(tbl)<-c("Name","StartYear","EndYear","Position","Height","Weight","BirthDate","College")

tbl$BirthDate<-as.Date(tbl$BirthDate[1],format="%B %d, %Y")

Created by Pretty R at inside-R.org

And here’s the result:Result

Source: http://www.r-bloggers.com/scraping-xml-tables-with-r/

A Content Marketer's Guide to Data Scraping

As digital marketers, big data should be what we use to inform a lot of the decisions we make. Using intelligence to understand what works within your industry is absolutely crucial within content campaigns, but it blows my mind to know that so many businesses aren't focusing on it.

One reason I often hear from businesses is that they don't have the budget to invest in complex and expensive tools that can feed in reams of data to them. That said, you don't always need to invest in expensive tools to gather valuable intelligence — this is where data scraping comes in.

Just so you understand, here's a very brief overview of what data scraping is from Wikipedia:

    "Data scraping is a technique in which a computer program extracts data from human-readable output coming from another program."

Essentially, it involves crawling through a web page and gathering nuggets of information that you can use for your analysis. For example, you could search through a site like Search Engine Land and scrape the author names of each of the posts that have been published, and then you could correlate this to social share data to find who the top performing authors are on that website.

Hopefully, you can start to see how this data can be valuable. What's more, it doesn't require any coding knowledge — if you're able to follow my simple instructions, you can start gathering information that will inform your content campaigns. I've recently used this research to help me get a post published on the front page of BuzzFeed, getting viewed over 100,000 times and channeling a huge amount of traffic through to my blog.

Disclaimer: One thing that I really need to stress before you read on is the fact that scraping a website may breach its terms of service. You should ensure that this isn't the case before carrying out any scraping activities. For example, Twitter completely prohibits the scraping of information on their site. This is from their Terms of Service:

    "crawling the Services is permissible if done in accordance with the provisions of the robots.txt file, however, scraping the Services without the prior consent of Twitter is expressly prohibited"

Google similarly forbids the scraping of content from their web properties:

    Google's Terms of Service do not allow the sending of automated queries of any sort to our system without express permission in advance from Google.

So be careful, kids.
Content analysis

Mastering the basics of data scraping will open up a whole new world of possibilities for content analysis. I'd advise any content marketer (or at least a member of their team) to get clued up on this.

Before I get started on the specific examples, you'll need to ensure that you have Microsoft Excel on your computer (everyone should have Excel!) and also the SEO Tools plugin for Excel (free download here). I put together a full tutorial on using the SEO tools plugin that you may also be interested in.

Alongside this, you'll want a web crawling tool like Screaming Frog's SEO Spider or Xenu Link Sleuth (both have free options). Once you've got these set up, you'll be able to do everything that I outline below.

So here are some ways in which you can use scraping to analyse content and how this can be applied into your content marketing campaigns:

1. Finding the different authors of a blog

Analysing big publications and blogs to find who the influential authors are can give you some really valuable data. Once you have a list of all the authors on a blog, you can find out which of those have created content that has performed well on social media, had a lot of engagement within the comments and also gather extra stats around their social following, etc.

I use this information on a daily basis to build relationships with influential writers and get my content placed on top tier websites. Here's how you can do it:

Step 1: Gather a list of the URLs from the domain you're analysing using Screaming Frog's SEO Spider. Simply add the root domain into Screaming Frog's interface and hit start (if you haven't used this tool before, you can check out my tutorial here).

Once the tool has finished gathering all the URLs (this can take a little while for big websites), simply export them all to an Excel spreadsheet.

Step 2: Open up Google Chrome and navigate to one of the article pages of the domain you're analysing and find where they mention the author's name (this is usually within an author bio section or underneath the post title). Once you've found this, right-click their name and select inspect element (this will bring up the Chrome developer console).

Within the developer console, the line of code associated to the author's name that you selected will be highlighted (see the below image). All you need to do now is right-click on the highlighted line of code and press Copy XPath.

For the Search Engine Land website, the following code would be copied:

//*[@id="leftCol"]/div[2]/p/span/a

This may not make any sense to you at this stage, but bear with me and you'll see how it works.

Step 3: Go back to your spreadsheet of URLs and get rid of all the extra information that Screaming Frog gives you, leaving just the list of raw URLs – add these to the first column (column A) of your worksheet.

Step 4: In cell B2, add the following formula:

=XPathOnUrl(A2,"//*[@id='leftCol']/div[2]/p/span/a")

Just to break this formula down for you, the function XPathOnUrl allows you to use the XPath code directly within (this is with the SEO Tools plugin installed; it won't work without this). The first element of the function specifies which URL we are going to scrape. In this instance I've selected cell A2, which contains a URL from the crawl I did within Screaming Frog (alternatively, you could just type the URL, making sure that you wrap it within quotation marks).

Finally, the last part of the function is our XPath code that we gathered. One thing to note is that you have to remove the quotation marks from the code and replace them with apostrophes. In this example, I'm referring to the "leftCol" section, which I've changed to ‘leftCol' — if you don't do this, Excel won't read the formula correctly.

Once you press enter, there may be a couple of seconds delay whilst the SEO Tools plugin crawls the page, then it will return a result. It's worth mentioning that within the example I've given above, we're looking for author names on article pages, so if I try to run this on a URL that isn't an article (e.g. the homepage) I will get an error.

For those interested, the XPath code itself works by starting at the top of the code of the URL specified and following the instructions outlined to find on-page elements and return results. So, for the following code:

//*[@id='leftCol']/div[2]/p/span/a

We're telling it to look for any element (//*) that has an id of leftCol (@id='leftCol') and then go down to the second div tag after this (div[2]), followed by a p tag, a span tag and finally, an a tag (/p/span/a). The result returned should be the text within this a tag.

Don't worry if you don't understand this, but if you do, it will help you to create your own XPath. For example, if you wanted to grab the output of an a tag that has rel=author attached to it (another great way of finding page authors), then you could use some XPath that looked a little something like this:

//a[@rel='author']

As a full formula within Excel it would look something like this:

=XPathOnUrl(A2,"//a[@rel='author']")

Once you've created the formula, you can drag it down and apply it to a large number of URLs all at once. This is a huge time-saver as you'd have to manually go through each website and copy/paste each author to get the same results without scraping – I don't need to explain how long this would take.

Now that I've explained the basics, I'll show you some other ways in which scraping can be used…

2. Finding extra details around page authors

So, we've found a list of author names, which is great, but to really get some more insight into the authors we will need more data. Again, this can often be scraped from the website you're analysing.

Most blogs/publications that list the names of the article author will actually have individual author pages. Again, using Search Engine Land as an example, if you click my name at the top of this post you will be taken to a page that has more details on me, including my Twitter profile, Google+ profile and LinkedIn profile. This is the kind of data that I'd want to gather because it gives me a point of contact for the author I'm looking to get in touch with.

Here's how you can do it.

Step 1: First we need to get the author profile URLs so that we can scrape the extra details off of them. To do this, you can use the same approach to find the author's name, with just a little addition to the formula:

=XPathOnUrl(A2,"//a[@rel='author']", <strong>"href"</strong>)

The addition of the "href" part of the formula will extract the output of the href attribute of the atag. In Lehman terms, it will find the hyperlink attached to the author name and return that URL as a result.

Step 2: Now that we have the author profile page URLs, you can go on and gather the social media profiles. Instead of scraping the article URLs, we'll be using the profile URLs.

So, like last time, we need to find the XPath code to gather the Twitter, Google+ and LinkedIn links. To do this, open up Google Chrome and navigate to one of the author profile pages, right-click on the Twitter link and select Inspect Element.

Once you've done this, hover over the highlighted line of code within Chrome's developer tools, right-click and select Copy XPath.

Step 3: Finally, open up your Excel spreadsheet and add in the following formula (using the XPath that you've copied over):

=XPathOnUrl(C2,"//*[@id='leftCol']/div[2]/p/a[2]", "href")

Remember that this is the code for scraping Search Engine Land, so if you're doing this on a different website, it will almost certainly be different. One important thing to highlight here is that I've selected cell C2 here, which contains the URL of the author profile page and not just the article page. As well as this, you'll notice that I've included "href" at the end because we want the actual Twitter profile URL and not just the words ‘Twitter'.

You can now repeat this same process to get the Google+ and LinkedIn profile URLs and add it to your spreadsheet. Hopefully you're starting to see the value in this, and how it can be used to gather a lot of intelligence that can be used for all kinds of online activity, not least your SEO and social media campaigns.

3. Gathering the follower counts across social networks

Now that we have the author's social media accounts, it makes sense to get their follower counts so that they can be ranked based on influence within the spreadsheet.

Here are the final XPath formulae that you can plug straight into Excel for each network to get their follower counts. All you'll need to do is replace the text INSERT SOCIAL PROFILE URL with the cell reference to the Google+/LinkedIn URL:

Google+:

=XPathOnUrl(<strong>INSERTGOOGLEPROFILEURL</strong>,"//span[@class='BOfSxb']")

LinkedIn:

=XPathOnUrl(<strong>INSERTLINKEDINURL</strong>,"//dd[@class='overview-connections']/p/strong")

4. Scraping page titles

Once you've got a list of URLs, you're going to want to get an idea of what the content is actually about. Using this quick bit of XPath against any URL will display the title of the page:

=XPathOnUrl(A2,"//title")

To be fair, if you're using the SEO Tools plugin for Excel then you can just use the built-in feature to scrape page titles, but it's always handy to know how to do it manually!

A nice extra touch for analysis is to look at the number of words used within the page titles. To do this, use the following formula:

=CountWords(A2)

From this you can get an understanding of what the optimum title length of a post within a website is. This is really handy if you're pitching an article to a specific publication. If you make the post the best possible fit for the site and back up your decisions with historical data, you stand a much better chance of success.

Taking this a step further, you can gather the social shares for each URL using the following functions:

Twitter:

=TwitterCount(<strong>INSERTURLHERE</strong>)

Facebook:

=FacebookLikes(<strong>INSERTURLHERE</strong>)

Google+:

=GooglePlusCount(<strong>INSERTURLHERE</strong>)

Note: You can also use a tool like URL Profiler to pull in this data, which is much better for large data sets. The tool also helps you to gather large chunks of data from other social networks, link data sources like Ahrefs, Majestic SEO and Moz, which is awesome.

If you want to get even more social stats then you can use the SharedCount API, and this is how you go about doing it…

Firstly, create a new column in your Excel spreadsheet and add the following formula (where A2 is the URL of the webpage you want to gather social stats for):

=CONCATENATE("http://api.sharedcount.com/?url=",A2)

You should now have a cell that contains your webpage URL prefixed with the SharedCount API URL. This is what we will use to gather social stats. Now here's the Excel formula to use for each network (where B2 is the cell that contaiins the formula above):

StumbleUpon:

=JsonPathOnUrl(B2,"StumbleUpon")

Reddit:

=JsonPathOnUrl(B2,"Reddit")

Delicious:

=JsonPathOnUrl(B2,"Delicious")

Digg:

=JsonPathOnUrl(B2,"Diggs")

Pinterest:

=JsonPathOnUrl(B2,"Pinterest")

LinkedIn:

=JsonPathOnUrl(B2,"Linkedin")

Facebook Shares:

=JsonPathOnUrl(B2,"Facebook.share_count")

Facebook Comments:

=JsonPathOnUrl(B2,"Facebook.comment_count")

Once you have this data, you can start looking much deeper into the elements of a successful post. Here's an example of a chart that I created around a large sample of articles that I analysed within Upworthy.com.

The chart looks at the average number of social shares that an article on Upworthy receives vs the number of words within its title. This is invaluable data that can be used across a whole host of different on-page elements to get the perfect article template for the site you're pitching to.

See, big data is useful!

5. Date/time the post was published

Along with analysing the details of headlines that are working within a site, you may want to look at the optimal posting times for best results. This is something that I regularly do within my blogs to ensure that I'm getting the best possible return from the time I spend writing.

Every site is different, which makes it very difficult for an automated, one-size-fits-all tool to gather this information. Some sites will have this data within the <head> section of their webpages, but others will display it directly under the article headline. Again, Search Engine Land is a perfect example of a website doing this…

So here's how you can scrape this information from the articles on Search Engine Land:

=XPathOnUrl(<strong>INSERTARTICLEURL</strong>,"//*[@class='dateline']/text()")

Now you've got the date and time of the post. You may want to trim this down and reformat it for your data analysis, but you've got it all in Excel so that should be pretty easy.

Extra reading

Data scraping is seriously powerful, and once you've had a bit of a play around with it you'll also realise that it's not that complicated. The examples that I've given are just a starting point but once you get your creative head on, you'll soon start to see the opportunities that arise from this intelligence.

Here's some extra reading that you might find useful:

    http://findmyblogway.com/scraping-communities-with-xpath/

    http://builtvisible.com/data-entry-is-a-waste-of-time/

    http://www.seotakeaways.com/data-scraping-guide-for-seo/

    http://okdork.com/2014/04/30/the-step-by-step-guide-to-10x-growth-for-any-blog/

TL;DR

    Start using actual data to inform your content campaigns instead of going on your gut feeling.

    Gather intelligence around specific domains you want to target for content placement and create the perfect post for their audience.

    Get clued up on XPath and JSON through using the SEO Tools plugin for Excel.

    Spend more time analysing what content will get you results as opposed to what sites will give you links!

    Check the website's ToS before scraping.

Source:http://moz.com/blog/a-content-marketers-guide-to-data-scraping

Data Scraping Guide for SEO & Analytics

Data scraping can help you a lot in competitive analysis as well as pulling out data from your client’s website like extracting the titles, keywords and content categories.

You can quickly get an idea of which keywords are driving traffic to a website, which content categories are attracting links and user engagement, what kind of resources will it take to rank your site…………and the list goes on…

 Scraping Organic Search Results

By scraping organic search results you can quickly find out your SEO competitors for a particular search term. You can determine the title tags and the keywords they are targeting.

    The easiest way to scrape organic search results is by using the SERPs Redux bookmarklet.

For e.g if you scrape organic listings for the search term ‘seo tools’ using this bookmarklet, you may see the following results:

You can copy paste the websites URLs and title tags easily into your spreadsheet from the text boxes.

    Pro Tip by Tahir Fayyaz:

    Just wanted to add a tip for people using the SERPs Redux bookmarklet.

    If you have a data separated over multiple pages that you want to scrape you can use AutoPager for Firefox or Chrome to loads x amount of pages all on one page and then scrape it all using the bookmarklet.

Scraping on page elements from a web document

Through this Excel Plugin by Niels Bosma you can fetch several on-page elements from a URL or list of URLs like:

    Title tag
    Meta description tag
    Meta keywords tag
    Meta robots tag
    H1 tag
    H2 tag
    HTTP Header
    Backlinks
    Facebook likes etc.

Scraping data through Google Docs

Google docs provide a function known as importXML through which you can import data from web documents directly into Google Docs spreadsheet. However to use this function you must be familiar with X-path expressions.

    Syntax: =importXML(URL,X-path-query)

    url=> URL of the web page from which you want to import the data.

    x-path-query => A query language used to extract data from web pages.

You need to understand following things about X-path in order to use importXML function:

1. Xpath terminology- What are nodes and kind of nodes like element nodes, attribute nodes etc.

2. Relationship between nodes- How different nodes are related to each other. Like parent node, child node, siblings etc.

3. Selecting nodes- The node is selected by following a path known as the path expression.

4. Predicates – They are used to find a specific node or a node that contains a specific value. They are always embedded in square brackets.

If you follow the x-path tutorial then it should not take you more than an hour to understand how X path expressions works.

Understanding path expressions is easy but building them is not. That’s is why i use a firefbug extension named ‘X-Pather‘ to quickly generate path expressions while browsing HTML and XML documents.

Since X-Pather is a firebug extension, it means you first need to install firebug in order to use it.

 How to scrape data using importXML()

Step-1: Install firebug – Through this add on you can edit & monitor CSS, HTML, and JavaScript while you browse.

Step-2: Install X-pather – Through this tool you can generate path expressions while browsing a web document. You can also evaluate path expressions.

Step-3: Go to the web page whose data you want to scrape. Select the type of element you want to scrape. For e.g. if you want to scrape anchor text, then select one anchor text.

Step-4: Right click on the selected text and then select ‘show in Xpather’ from the drop down menu.

Then you will see the Xpather browser from where you can copy the X-path.

Here i have selected the text ‘Google Analytics’, that is why the xpath browser is showing ‘Google Analytics’ in the content section. This is my xpath:

    /html/body/div[@id='page']/div[@id='page-ext']/div[@id='main']/div[@id='main-ext']/div[@id='mask-3']/div[@id='mask-2']/div[@id='mask-1']/div[@id='primary-content']/div/div/div[@id='post-58']/div/ol[2]/li[1]/a

Pretty scary huh. It can be even more scary if you try to build it manually. I want to scrape the name of all the analytic tools from this page: killer seo tools. For this i need to modify the aforesaid path expression into a formula.

This is possible only if i can determine static and variable nodes between two or more path expressions. So i determined the path expression of another element ‘Google Analytics Help center’ (second in the list) through X-pather:

    /html/body/div[@id='page']/div[@id='page-ext']/div[@id='main']/div[@id='main-ext']/div[@id='mask-3']/div[@id='mask-2']/div[@id='mask-1']/div[@id='primary-content']/div/div/div[@id='post-58']/div/ol[2]/li[2]/a

Now we can see that the node which has changed between the original and new path expression is the final ‘li’ element: li[1] to li[2]. So i can come up with following final path expression:

    /html/body/div[@id='page']/div[@id='page-ext']/div[@id='main']/div[@id='main-ext']/div[@id='mask-3']/div[@id='mask-2']/div[@id='mask-1']/div[@id='primary-content']/div/div/div[@id='post-58']/div/ol[2]//li/a

Now all i have to do is copy-paste this final path expression as an argument to the importXML function in Google Docs spreadsheet. Then the function will extract all the names of Google Analytics tool from my killer SEO tools page.

This is how you can scrape data using importXML.

    Pro Tip by Niels Bosma: “Anything you can do with importXML in Google docs you can do with XPathOnUrl directly in Excel.”

    To use XPathOnUrl function you first need to install the Niels Bosma’s Excel plugin. It is not a built in function in excel.

Note:You can also use a free tool named Scrapy for data scraping. It is an an open source web scraping framework and is used to extract structured data from web pages & APIs. You need to know Python (a programming language) in order to use scrapy.

Scraping on-page elements of an entire website

There are two awesome tools which can help you in scraping on-page elements (title tags, meta descriptions, meta keywords etc) of an entire website. One is the evergreen and free Xenu Link Sleuth and the other is the mighty Screaming Frog SEO Spider.

What make these tools amazing is that you can scrape the data of entire website and download it into excel. So if you want to know the keywords used in the title tag on all the web pages of your competitor’s website then you know what you need to do.

Note: Save the Xenu data as a tab separated text file and then open the file in Excel.

 Scraping organic and paid keywords of an entire website

The tool that i use for scraping keywords is SEMRush. Through this awesome tool i can determine which organic and paid keyword are driving traffic to my competitor’s website and then can download the whole list into excel for keyword research. You can get more details about this tool through this post: Scaling Keyword Research & Competitive Analysis to new heights

Scraping keywords from a webpage

Through this excel macro spreadsheet from seogadget you can fetch keywords from the text of a URL(s). However you need an Alchemy API key to use this macro.

You can get the Alchemy API key from here

Scraping keywords data from Google Adwords API

If you have access to Google Adwords API then you can install this plugin from seogadget website. This plugin creates a series of functions designed to fetch keywords data from the Google Adwords API like:

getAdWordAvg()- returns average search volume from the adwords API.

getAdWordStats() – returns local search volume and previous 12 months separated by commas

getAdWordIdeas() – returns keyword suggestions based on API suggest service.

Check out this video to know how this plug-in works

Scraping Google Adwords Ad copies of any website

I use the tool SEMRush to scrape and download the Google Adwords ad copies of my competitors into excel and then mine keywords or just get ad copy ideas.  Go to semrush, type the competitor website URL and then click on ‘Adwords Ad texts’ link on the left hand side menu. Once you see the report you can download it into excel.

Scraping back links of an entire website

The tool that you can use to scrape and download the back links of an entire website is: open site explorer

Scraping Outbound links from web pages

Garrett French of citation Labs has shared an excellent tool: OBL Scraper+Contact Finder which can scrape outbound links and contact details from a URL or URL list. This tool can help you a lot in link building. Check out this video to know more about this awesome tool:

Scraper – Google chrome extension

This chrome extension can scrape data from web pages and export it to Google docs. This tool is simple to use. Select the web page element/node you want to scrape. Then right click on the selected element and select ‘scrape similar’.

Any element/node that’s similar to what you have selected will be scraped by the tool which you can later export to Google Docs. One big advantage of this tool is that it reduces our dependency on building Xpath expressions and make scraping easier.

See how easy it is to scrape name and URLs of all the Analytics tools without using Xpath expressions.

Source: http://www.optimizesmart.com/data-scraping-guide-for-seo/

Interactive Crawls for Scraping AJAX Pages on the Web

Crawling pages on the web has become an everyday affair for most enterprises. Too often do we come across offline businesses as well who’d like data gathered from the web for internal analyses. All this eventually to serve customers faster and better. At times, when the crawl job is high-end cum high-scale, businesses also consider DaaS providers to supplement their efforts.

However, the web landscape too has evolved with newer technologies that provide fancy experiences to web users. AJAX elements are one such common aid that leave even the DaaS providers perplexed. They come in various forms from a user’s point of view-

1. Load more results on the same page

2. Filter results based on various selection criteria

3. Submit forms, etc.

When crawling a non-AJAX page, simple GET requests do the job. However, AJAX pages work with POST requests that are not easy to trace for a normal bot.

Difference between GET request and POST request- Scraping

GET vs. POST

At PromptCloud, from our experience with a number of AJAX sites on the web, we’ve crossed the tech barrier. Below is a quick review about the challenges that come with AJAX crawling and its indicative solutions-

1. Javascript Emulations- A bot essentially emulates human browsing to fetch pages. When this needs to be done for Javascript components on a page, it gets tricky. Headless browser, which emulates human interaction with a web page without an interface, is the current approach. These browsers click on various elements/ dropdown lists that are embedded within Javascript code and capture responses to be transferred to programs. Which headless browser to pick depends on what fits well into your current stack.

2. Fetch Bandwidths- Unlike GET requests which complete pretty quickly, POST requests take quite a bit of time due to the number of events involved per fetch. Hence a good amount of bandwidth needs to be allocated in order to receive the response. For the same reason, wait times need to be taken care of too else you might end up with incomplete responses.

3. .NET Architectures- This is a more complex scenario related to maintaining the View State. Most of the postbacks come with an event and its validation. The bot needs to track the view state and pass validations for the event to occur so that the code can be executed and results captured. This is achieved by adopting a mechanism to restore states if things break midway.

4. Page Encoding- Request and response headers need to be taken care of on AJAX pages. The request needs to be sent in the exact format as expected by the server (Content-type or media type, accept fields, etc.) and similarly responses need to be parsed based on the content-type.

A Use Case

One of our clients who is into sale of event tickets at discounted rates had us crawl one of the ticketing sites on the web weekly; one of the most complex AJAX crawling we’ve dealt with so far. For the data that was to be extracted, multiple AJAX fetches were needed depending on the selections made. Requests had to be made for a combination of items from the dropdown box. These came with cookies and session IDs. To add to the challenge the site was extremely dynamic and changed its structure every week making it difficult for us to follow what data was where on the page.

We developed an AJAX crawler specific to this site to take care of all the dynamics. Response times were taken care of so that we didn’t miss any relevant information. We included an ML component to improve the crawler which is now pretty stable irrespective of changes on the site.

Overall, AJAX crawling requires more compute power in addition to the tech expertise. And because there’s no uniformity on the web, there’s always a new challenge to overcome in this landscape. It wouldn’t be an overrating if we said we’ve done a good job at that so far and have developed the knack :)

Reach out to us for any kind of web scraping/ crawling- either AJAX or not. We’ll take care of the complexities.

Source: https://www.promptcloud.com/blog/web-scraping-interactive-ajax-crawls/

A Content Marketer's Guide to Data Scraping

As digital marketers, big data should be what we use to inform a lot of the decisions we make. Using intelligence to understand what works within your industry is absolutely crucial within content campaigns, but it blows my mind to know that so many businesses aren't focusing on it.

One reason I often hear from businesses is that they don't have the budget to invest in complex and expensive tools that can feed in reams of data to them. That said, you don't always need to invest in expensive tools to gather valuable intelligence — this is where data scraping comes in.

Just so you understand, here's a very brief overview of what data scraping is from Wikipedia:

    "Data scraping is a technique in which a computer program extracts data from human-readable output coming from another program."

Essentially, it involves crawling through a web page and gathering nuggets of information that you can use for your analysis. For example, you could search through a site like Search Engine Land and scrape the author names of each of the posts that have been published, and then you could correlate this to social share data to find who the top performing authors are on that website.

Hopefully, you can start to see how this data can be valuable. What's more, it doesn't require any coding knowledge — if you're able to follow my simple instructions, you can start gathering information that will inform your content campaigns. I've recently used this research to help me get a post published on the front page of BuzzFeed, getting viewed over 100,000 times and channeling a huge amount of traffic through to my blog.

Disclaimer: One thing that I really need to stress before you read on is the fact that scraping a website may breach its terms of service. You should ensure that this isn't the case before carrying out any scraping activities. For example, Twitter completely prohibits the scraping of information on their site. This is from their Terms of Service:

    "crawling the Services is permissible if done in accordance with the provisions of the robots.txt file, however, scraping the Services without the prior consent of Twitter is expressly prohibited"

Google similarly forbids the scraping of content from their web properties:

    Google's Terms of Service do not allow the sending of automated queries of any sort to our system without express permission in advance from Google.

So be careful, kids.

Content analysis

Mastering the basics of data scraping will open up a whole new world of possibilities for content analysis. I'd advise any content marketer (or at least a member of their team) to get clued up on this.

Before I get started on the specific examples, you'll need to ensure that you have Microsoft Excel on your computer (everyone should have Excel!) and also the SEO Tools plugin for Excel (free download here). I put together a full tutorial on using the SEO tools plugin that you may also be interested in.

Alongside this, you'll want a web crawling tool like Screaming Frog's SEO Spider or Xenu Link Sleuth (both have free options). Once you've got these set up, you'll be able to do everything that I outline below.

So here are some ways in which you can use scraping to analyse content and how this can be applied into your content marketing campaigns:

1. Finding the different authors of a blog

Analysing big publications and blogs to find who the influential authors are can give you some really valuable data. Once you have a list of all the authors on a blog, you can find out which of those have created content that has performed well on social media, had a lot of engagement within the comments and also gather extra stats around their social following, etc.

I use this information on a daily basis to build relationships with influential writers and get my content placed on top tier websites. Here's how you can do it:

Step 1: Gather a list of the URLs from the domain you're analysing using Screaming Frog's SEO Spider. Simply add the root domain into Screaming Frog's interface and hit start (if you haven't used this tool before, you can check out my tutorial here).

Once the tool has finished gathering all the URLs (this can take a little while for big websites), simply export them all to an Excel spreadsheet.

Step 2: Open up Google Chrome and navigate to one of the article pages of the domain you're analysing and find where they mention the author's name (this is usually within an author bio section or underneath the post title). Once you've found this, right-click their name and select inspect element (this will bring up the Chrome developer console).

Within the developer console, the line of code associated to the author's name that you selected will be highlighted (see the below image). All you need to do now is right-click on the highlighted line of code and press Copy XPath.

For the Search Engine Land website, the following code would be copied:

//*[@id="leftCol"]/div[2]/p/span/a

This may not make any sense to you at this stage, but bear with me and you'll see how it works.

Step 3: Go back to your spreadsheet of URLs and get rid of all the extra information that Screaming Frog gives you, leaving just the list of raw URLs – add these to the first column (column A) of your worksheet.

Step 4: In cell B2, add the following formula:

=XPathOnUrl(A2,"//*[@id='leftCol']/div[2]/p/span/a")

Just to break this formula down for you, the function XPathOnUrl allows you to use the XPath code directly within (this is with the SEO Tools plugin installed; it won't work without this). The first element of the function specifies which URL we are going to scrape. In this instance I've selected cell A2, which contains a URL from the crawl I did within Screaming Frog (alternatively, you could just type the URL, making sure that you wrap it within quotation marks).

Finally, the last part of the function is our XPath code that we gathered. One thing to note is that you have to remove the quotation marks from the code and replace them with apostrophes. In this example, I'm referring to the "leftCol" section, which I've changed to ‘leftCol' — if you don't do this, Excel won't read the formula correctly.

Once you press enter, there may be a couple of seconds delay whilst the SEO Tools plugin crawls the page, then it will return a result. It's worth mentioning that within the example I've given above, we're looking for author names on article pages, so if I try to run this on a URL that isn't an article (e.g. the homepage) I will get an error.

For those interested, the XPath code itself works by starting at the top of the code of the URL specified and following the instructions outlined to find on-page elements and return results. So, for the following code:

//*[@id='leftCol']/div[2]/p/span/a

We're telling it to look for any element (//*) that has an id of leftCol (@id='leftCol') and then go down to the second div tag after this (div[2]), followed by a p tag, a span tag and finally, an a tag (/p/span/a). The result returned should be the text within this a tag.

Don't worry if you don't understand this, but if you do, it will help you to create your own XPath. For example, if you wanted to grab the output of an a tag that has rel=author attached to it (another great way of finding page authors), then you could use some XPath that looked a little something like this:

//a[@rel='author']

As a full formula within Excel it would look something like this:

=XPathOnUrl(A2,"//a[@rel='author']")

Once you've created the formula, you can drag it down and apply it to a large number of URLs all at once. This is a huge time-saver as you'd have to manually go through each website and copy/paste each author to get the same results without scraping – I don't need to explain how long this would take.

Now that I've explained the basics, I'll show you some other ways in which scraping can be used…

2. Finding extra details around page authors

So, we've found a list of author names, which is great, but to really get some more insight into the authors we will need more data. Again, this can often be scraped from the website you're analysing.

Most blogs/publications that list the names of the article author will actually have individual author pages. Again, using Search Engine Land as an example, if you click my name at the top of this post you will be taken to a page that has more details on me, including my Twitter profile, Google+ profile and LinkedIn profile. This is the kind of data that I'd want to gather because it gives me a point of contact for the author I'm looking to get in touch with.

Here's how you can do it.

Step 1: First we need to get the author profile URLs so that we can scrape the extra details off of them. To do this, you can use the same approach to find the author's name, with just a little addition to the formula:

=XPathOnUrl(A2,"//a[@rel='author']", <strong>"href"</strong>)

The addition of the "href" part of the formula will extract the output of the href attribute of the atag. In Lehman terms, it will find the hyperlink attached to the author name and return that URL as a result.

Step 2: Now that we have the author profile page URLs, you can go on and gather the social media profiles. Instead of scraping the article URLs, we'll be using the profile URLs.

So, like last time, we need to find the XPath code to gather the Twitter, Google+ and LinkedIn links. To do this, open up Google Chrome and navigate to one of the author profile pages, right-click on the Twitter link and select Inspect Element.

Once you've done this, hover over the highlighted line of code within Chrome's developer tools, right-click and select Copy XPath.

Step 3: Finally, open up your Excel spreadsheet and add in the following formula (using the XPath that you've copied over):

=XPathOnUrl(C2,"//*[@id='leftCol']/div[2]/p/a[2]", "href")

Remember that this is the code for scraping Search Engine Land, so if you're doing this on a different website, it will almost certainly be different. One important thing to highlight here is that I've selected cell C2 here, which contains the URL of the author profile page and not just the article page. As well as this, you'll notice that I've included "href" at the end because we want the actual Twitter profile URL and not just the words ‘Twitter'.

You can now repeat this same process to get the Google+ and LinkedIn profile URLs and add it to your spreadsheet. Hopefully you're starting to see the value in this, and how it can be used to gather a lot of intelligence that can be used for all kinds of online activity, not least your SEO and social media campaigns.

3. Gathering the follower counts across social networks

Now that we have the author's social media accounts, it makes sense to get their follower counts so that they can be ranked based on influence within the spreadsheet.

Here are the final XPath formulae that you can plug straight into Excel for each network to get their follower counts. All you'll need to do is replace the text INSERT SOCIAL PROFILE URL with the cell reference to the Google+/LinkedIn URL:

Google+:

=XPathOnUrl(<strong>INSERTGOOGLEPROFILEURL</strong>,"//span[@class='BOfSxb']")

LinkedIn:

=XPathOnUrl(<strong>INSERTLINKEDINURL</strong>,"//dd[@class='overview-connections']/p/strong")

4. Scraping page titles

Once you've got a list of URLs, you're going to want to get an idea of what the content is actually about. Using this quick bit of XPath against any URL will display the title of the page:

=XPathOnUrl(A2,"//title")

To be fair, if you're using the SEO Tools plugin for Excel then you can just use the built-in feature to scrape page titles, but it's always handy to know how to do it manually!

A nice extra touch for analysis is to look at the number of words used within the page titles. To do this, use the following formula:

=CountWords(A2)

From this you can get an understanding of what the optimum title length of a post within a website is. This is really handy if you're pitching an article to a specific publication. If you make the post the best possible fit for the site and back up your decisions with historical data, you stand a much better chance of success.

Taking this a step further, you can gather the social shares for each URL using the following functions:

Twitter:

=TwitterCount(<strong>INSERTURLHERE</strong>)

Facebook:

=FacebookLikes(<strong>INSERTURLHERE</strong>)

Google+:

=GooglePlusCount(<strong>INSERTURLHERE</strong>)

Note: You can also use a tool like URL Profiler to pull in this data, which is much better for large data sets. The tool also helps you to gather large chunks of data from other social networks, link data sources like Ahrefs, Majestic SEO and Moz, which is awesome.

If you want to get even more social stats then you can use the SharedCount API, and this is how you go about doing it…

Firstly, create a new column in your Excel spreadsheet and add the following formula (where A2 is the URL of the webpage you want to gather social stats for):

=CONCATENATE("http://api.sharedcount.com/?url=",A2)

You should now have a cell that contains your webpage URL prefixed with the SharedCount API URL. This is what we will use to gather social stats. Now here's the Excel formula to use for each network (where B2 is the cell that contaiins the formula above):

StumbleUpon:

=JsonPathOnUrl(B2,"StumbleUpon")

Reddit:

=JsonPathOnUrl(B2,"Reddit")

Delicious:

=JsonPathOnUrl(B2,"Delicious")

Digg:

=JsonPathOnUrl(B2,"Diggs")

Pinterest:

=JsonPathOnUrl(B2,"Pinterest")

LinkedIn:

=JsonPathOnUrl(B2,"Linkedin")

Facebook Shares:

=JsonPathOnUrl(B2,"Facebook.share_count")

Facebook Comments:

=JsonPathOnUrl(B2,"Facebook.comment_count")

Once you have this data, you can start looking much deeper into the elements of a successful post. Here's an example of a chart that I created around a large sample of articles that I analysed within Upworthy.com.

The chart looks at the average number of social shares that an article on Upworthy receives vs the number of words within its title. This is invaluable data that can be used across a whole host of different on-page elements to get the perfect article template for the site you're pitching to.

See, big data is useful!

5. Date/time the post was published

Along with analysing the details of headlines that are working within a site, you may want to look at the optimal posting times for best results. This is something that I regularly do within my blogs to ensure that I'm getting the best possible return from the time I spend writing.

Every site is different, which makes it very difficult for an automated, one-size-fits-all tool to gather this information. Some sites will have this data within the <head> section of their webpages, but others will display it directly under the article headline. Again, Search Engine Land is a perfect example of a website doing this…

So here's how you can scrape this information from the articles on Search Engine Land:

=XPathOnUrl(<strong>INSERTARTICLEURL</strong>,"//*[@class='dateline']/text()")

Now you've got the date and time of the post. You may want to trim this down and reformat it for your data analysis, but you've got it all in Excel so that should be pretty easy.

Extra reading

Data scraping is seriously powerful, and once you've had a bit of a play around with it you'll also realise that it's not that complicated. The examples that I've given are just a starting point but once you get your creative head on, you'll soon start to see the opportunities that arise from this intelligence.

Here's some extra reading that you might find useful:

    http://findmyblogway.com/scraping-communities-with-xpath/

    http://builtvisible.com/data-entry-is-a-waste-of-time/

    http://www.seotakeaways.com/data-scraping-guide-for-seo/

    http://okdork.com/2014/04/30/the-step-by-step-guide-to-10x-growth-for-any-blog/

TL;DR

    Start using actual data to inform your content campaigns instead of going on your gut feeling.

    Gather intelligence around specific domains you want to target for content placement and create the perfect post for their audience.

    Get clued up on XPath and JSON through using the SEO Tools plugin for Excel.

    Spend more time analysing what content will get you results as opposed to what sites will give you links!

    Check the website's ToS before scraping.

Source:http://moz.com/blog/a-content-marketers-guide-to-data-scraping

Web Scraping and Copyright

There are rigorous debates on the topic of copyright issues of a scraped data. Whether it is legal to use web scraping to extract data from a website or it’s just an illegal act that will lead you to a troublesome situation. There are certain implementations when we talk of web scraping. There are certain websites that will provide you with RSS feeds.

Just grab the piece use it and get the credit. However there are sites that do not allow such kind of cooperation (as we will call this!) and thus there is no other way to extract reliable data. Another way is to hold the “ctrl” plus “c” keys and wait for a while for the data to be copied to your computer.

Source:http://www.loginworks.com/blogs/web-scraping-blogs/web-scraping-copyright/