WikiReverse- Visualizing Reverse Links with the Common Crawl Archive

Ross FairbanksThis is a guest blog post by Ross Fairbanks

Ross Fairbanks is a software developer based in Barcelona. He mainly develops in Ruby and is interested in open data and cloud computing. This guest post describes his open data project wikireverse.org and why he built it.



What is WikiReverse?

WikiReverse [1] is an application that highlights web pages and the Wikipedia articles they link to. The project is based on Common Crawl’s July 2014 web crawl, which contains 3.6 billion pages. The results produced 36 million links to 4 million Wikipedia articles. Most of the results are from English Wikipedia (which had 32 million links) followed by Spanish, Indonesian and German. In total there are results for 283 languages.

I first heard about Common Crawl in a blog post by Steve Salevan— MapReduce for the Masses: Zero to Hadoop in Five Minutes with Common Crawl [2]. Running Steve’s code deepened my interest in the project. What I like most is the efficiency savings of a large web scale crawl that anyone can access. Attempting to crawl the same volume of web pages myself would have been vastly more expensive and time consuming.

I found that the data can be processed relatively cheaply, as it cost just $64 to process the metadata for 3.6 billion pages. This was achieved by using spot instances, which is the spare server capacity that Amazon Web Services auctions off when demand is low. This saved $115 compared to using full price instances.

There is great value in the Common Crawl archive; however, it is difficult to see with no interface to the data. It can be hard to visualize the possibilities and what can be done with the data. For this reason, my project runs an analysis over an entire crawl with a resulting site that allows the findings to be viewed and searched.

I chose to look at reverse links because, despite it’s relatively simple approach, it exposes interesting data that is normally deeply hidden. Wikipedia articles are often cited on the web and they appear highly in search results. I was interested in seeing how many links these articles have and what types of sites are linking to them.

A great benefit of working with an open dataset like Common Crawl’s is that WikiReverse results can be released very quickly to the public. Already, Gianluca Demartini from the University of Sheffield has released Who links to Wikipedia? [3] on the Wikimedia blog. This is an analysis of which top-level domains appear in the results. It is encouraging to see the interest in open data projects and hopefully more analyses of these types will be done.

Choosing Wikipedia also means the project can continue to benefit from the wide range of open data they release. The DBpedia [4] project uses raw data dumps released by Wikipedia and creates structured datasets for many aspects of data, including categories, images and geographic locations. I plan on using DBpedia to categorize articles in WikiReverse.

The code developed to analyze the data is available on Github. I’ve written a more detailed post on my blog on the data pipeline [5] that was developed to generate the data. The full dataset can be downloaded using BitTorrent. The data is 1.1 GB when compressed and 5.4 GB when extracted. Hopefully this will help others build their own projects using the Common Crawl data.


[1] https://wikireverse.org/
[2] http://blog.commoncrawl.org/2011/12/mapreduce-for-the-masses/
[3] http://blog.wikimedia.org/2015/02/03/who-links-to-wikipedia/
[4] http://dbpedia.org/About
[5] https://rossfairbanks.com/2015/01/23/wikireverse-data-pipeline.html

5 Good Reads in Big Open Data: Feb 13 2015

  1. What does it mean for the Open Web if users don’t know they’re on the internet? – via QUARTZ:

    This is more than a matter of semantics. The expectations and behaviors of the next billion people to come online will have profound effects on how the internet evolves. If the majority of the world’s online population spends time on Facebook, then policymakers, businesses, startups, developers, nonprofits, publishers, and anyone else interested in communicating with them will also, if they are to be effective, go to Facebook. That means they, too, must then play by the rules of one company. And that has implications for us all.


  2. Hard Drive Data Sets -via Backblaze: Backblaze provides online backup services storing data on over 41,000 hard drives ranging from 1 terabyte to 6 terabytes in size.  They have released an open, downloadable dataset on the reliability of these drives.


  3. The Open Source Question: critically important web infrastructure is woefully underfunded – via Slate: on the strange dichotomy of Silicon Valley: a “hypercapitalist steamship powered by it’s very antithesis”


  4. February 21st is Open Data Day- via Spatial Source: use this interactive map to find an Open Data event near you (or add your own)

    International Open Data Hackathon
    Image Source: opendataday.org/map

  5. Security is at the heart of the web – via O’Reilly Radar:

    …we want to be able to go to sleep without worrying that all of those great conversations on the open web will endanger the rest of what we do.

    Making the web work has always been a balancing act between enabling and forbidding, remembering and forgetting, and public and private. Managing identity, security, and privacy has always been complicated, both because of the challenges in each of those pieces and the tensions among them.

    Complicating things further, the web has succeeded in large part because people — myself included — have been willing to lock their paranoias away so long as nothing too terrible happened.

    Follow us @CommonCrawl on Twitter for the latest in Big Open Data

5 Good Reads in Big Open Data: Feb 6 2015

  1. The Dark Side of Open Data – via Forbes:

    There’s no reason to doubt that opening to the public of data previously unreleased by governments, if well managed, can be a boon for the economy and, ultimately, for the citizens themselves. It wouldn’t hurt, however, to strip out the grandiose rhetoric that sometimes surrounds them, and look, case by case, at the contexts and motivations that lead to their disclosure.


  2.  Bigger Data; Same Laptop -via Frank McSherry: throwing more machines at a problem isn’t necessarily the best approach. A laptop can outperform clusters when used effectively. This post uses the Web Data Commons 128 billion edge Hyperlink Graph, created using Common Crawl data, to showcase that.


  3. Fixing Verizon’s permacookie – via Slate: 9 lines of code could make Verizon’s controversial user-tracking system slightly less invasive and much less creepy.


  4. Interact with Committee to Protect Journalist ‘s Data- via Reuters Graphics: interactive map of journalists killed over time and by location

    Source: Committee to Protect Journalists Graphic by Matthew Weber/Reuters Graphics

    Source: Committee to Protect Journalists
    Graphic by Matthew Weber/Reuters Graphics


  5. The EU wants the rest of the world to forget too – via The New York Times:

    Countries have different standards for acceptable speech and for invasions of privacy. American libel laws, for example, are much more permissive than those in Britain. That’s why authors sometimes find it easier to have some books published in United States than in Britain. There is no doubt that the Internet has made it harder for governments to enforce certain rules and laws because information is not easily contained within borders. But that does not justify restricting the information available to citizens of other countries.

    Follow us @CommonCrawl on Twitter for the latest in Big Open Data

The Promise of Open Government Data & Where We Go Next

One of the biggest boons for the Open Data movement in recent years has been the enthusiastic support from all levels of government for releasing more, and higher quality, datasets to the public. In May 2013, the White House released its Open Data Policy and announced the launch of Project Open Data, a repository of tools and information–which anyone is free to contribute to–that help government agencies release data that is “available, discoverable, and usable.”

Since 2013, many enterprising government leaders across the United States at the federal, state, and local levels have responded to the President’s call to see just how far Open Data can take us in the 21st century. Following the White House’s groundbreaking appointment in 2009 of Aneesh Chopra as the country’s first Chief Technology Officer, many local and state governments across the United States have created similar positions. San Francisco last year named its first Chief Data Officer, Joy Bonaguro, and released a strategic plan to institutionalize Open Data in the city’s government. Los Angeles’ new Chief Data Officer, Abhi Nemani, was formerly at Code for America and hopes to make LA a model city for open government. His office recently launched an Open Data portal along with other programs aimed at fostering a vibrant data community in Los Angeles.1

Open government data is powerful because of its potential to reveal information about major trends and to inform questions pertaining to the economic, demographic, and social makeup of the United States. A second, no less important, reason why open government data is powerful is its potential to help shift the culture of government toward one of greater collaboration, innovation, and transparency.

These gains are encouraging, but there is still room for growth. One pressing issue is for more government leaders to establish Open Data policies that specify the type, format, frequency, and availability of the data  that their offices release. Open Data policy ensures that government entities not only release data to the public, but release it in useful and accessible formats.

Only nine states currently have formal Open Data policies, although at least two dozen have some form of informal policy and/or an Open Data portal.2 Agencies and state and local governments should not wait too long to standardize their policies about releasing Open Data. Doing so will severely limit Open Data’s potential. There is not much that a data analyst can do with a PDF.

One area of great potential is for data whizzes to pair open government data with web crawl data. Government data makes for a natural complement to other big datasets, like Common Crawl’s corpus of web crawl data, that together allow for rich educational and research opportunities. Educators and researchers should find Common Crawl data a valuable complement to government datasets when teaching data science and analysis skills. There is also vast potential to pair web crawl data with government data to create innovative social, business, or civic ventures.

Innovative government leaders across the United States (and the world!) and enterprising organizations like Code for America have laid an impressive foundation that others can continue to build upon as more and more government data is released to the public in increasingly usable formats. Common Crawl is encouraged by the rapid growth of a relatively new movement and we are excited to see the collaborations to come as Open Government and Open Data grow together.

 

Allison Domicone was formerly a Program and Policy Consultant to Common Crawl and previously worked for Creative Commons. She is currently pursuing a master’s degree in public policy from the Goldman School of Public Policy at the University of California, Berkeley.

December 2014 Crawl Archive Available

The crawl archive for December 2014 is now available! This crawl archive is over 160TB in size and contains 2.08 billion webpages. The files are located in the commoncrawl bucket at /crawl-data/CC-MAIN-2014-52/.

To assist with exploring and using the dataset, we’ve provided gzipped files that list:

By simply adding either s3://commoncrawl/ or https://commoncrawl.s3.amazonaws.com/ to each line, you end up with the S3 and HTTP paths respectively.

Thanks again to blekko for their ongoing donation of URLs for our crawl!

November 2014 Crawl Archive Available

The crawl archive for November 2014 is now available! This crawl archive is over 135TB in size and contains 1.95 billion webpages. The files are located in the commoncrawl bucket at /crawl-data/CC-MAIN-2014-49/.

To assist with exploring and using the dataset, we’ve provided gzipped files that list:

By simply adding either s3://commoncrawl/ or https://commoncrawl.s3.amazonaws.com/ to each line, you end up with the S3 and HTTP paths respectively.

Thanks again to blekko for their ongoing donation of URLs for our crawl!

Please Donate To Common Crawl!

Big data has the potential to change the world. The talent exists and the tools are already there. What’s lacking is access to data. Imagine the questions we could answer and the problems we could solve if talented, creative technologists could freely access more big data.

At Common Crawl, we are passionate about getting big open data into the hands of talented and creative people. Increasing access to data enables everything from business innovation to groundbreaking research.

Common Crawl is proud of what we have accomplished in 2014 thanks to our dedicated team and the support of donors like you.

This year:

  • 19 academic publications using Common Crawl data were published
  • Several Open Educational Resources designed to teach big data tools and methods were created
  • 1.3 petabytes containing 18.5 billion web pages were added to the Common Crawl corpus
  • Numerous presentations and tutorials were given at international conferences, local meetup groups, and academic workshops in six countries

100% of our funding comes from donors like you — Thank you! We accomplish a great deal with a small, dedicated staff on a limited budget so your investment in us has a big impact.

More resources for Common Crawl means more access to data for everyone. In 2015 we have big plans to scale up crawling to more rapidly increase the Common Crawl corpus and to grow our educational programs and catalogue of tutorials in order to invest in the next generation of data-driven technologists.

Whether it’s $10 or $10,000, Common Crawl needs your donation today.

Donate here!

Thank you very much,
Lisa Green and the Common Crawl team

October 2014 Crawl Archive Available

The crawl archive for October 2014 is now available! This crawl archive is over 254TB in size and contains 3.72 billion webpages. The files are located in the commoncrawl bucket at /crawl-data/CC-MAIN-2014-42/.

To assist with exploring and using the dataset, we’ve provided gzipped files that list:

By simply adding either s3://commoncrawl/ or https://commoncrawl.s3.amazonaws.com/ to each line, you end up with the S3 and HTTP paths respectively.

Thanks again to blekko for their ongoing donation of URLs for our crawl!

September 2014 Crawl Archive Available

The crawl archive for September 2014 is now available! This crawl archive is over 220TB in size and contains 2.98 billion webpages. The files are located in the commoncrawl bucket at /crawl-data/CC-MAIN-2014-41/.

To assist with exploring and using the dataset, we’ve provided gzipped files that list:

By simply adding either s3://commoncrawl/ or https://commoncrawl.s3.amazonaws.com/ to each line, you end up with the S3 and HTTP paths respectively.

Thanks again to blekko for their ongoing donation of URLs for our crawl!

August 2014 Crawl Data Available

The August crawl of 2014 is now available! The new dataset is over 200TB in size containing approximately 2.8 billion webpages. The new data is located in the commoncrawl bucket at /crawl-data/CC-MAIN-2014-35/.

To assist with exploring and using the dataset, we’ve provided gzipped files that list:

By simply adding either s3://commoncrawl/ or https://commoncrawl.s3.amazonaws.com/ to each line, you end up with the S3 and HTTP paths respectively.

Thanks again to blekko for their ongoing donation of URLs for our crawl!