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Chapter: Internet & World Wide Web HOW TO PROGRAM - Introduction - Dive Into Web 2.0

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Content Networks and User-Generated Content

Content networks are websites or collections of websites that provide information in var-ious forms (such as articles, wikis, blogs, etc.).

Content Networks

 

Content networks are websites or collections of websites that provide information in var-ious forms (such as articles, wikis, blogs, etc.). These provide another way of filtering the vast amounts of information on the Internet, by allowing users to go to a trusted site that  has already sorted through many sources to find the best content or has provided its own content. Figure 3.2 shows some examples of content networks.


User-Generated Content

 

User-generated content has been the key to success for many of today’s leading Web 2.0 companies, such as Amazon, eBay and Monster. The community adds value to these sites, which, in many cases, are almost entirely built on user-generated content. For example, eBay (an online auction site) relies on the community to buy and sell auction items, and Monster (a job search engine) connects job seekers with employers and recruiters.

 

User-generated content includes explicitly generated content such as articles, home videos and photos. It can also include implicitly generated content—information that is gathered from the users’ actions online. For example, every product you buy from Amazon and every video you watch on YouTube provides these sites with valuable information about your interests. Companies like Amazon have developed massive databases of anon-ymous user data to understand how users interact with their site. For example, Amazon uses your purchase history and compares it to purchases made by other users with similar interests to make personalized recommendations (e.g., “customers who bought this item also bought...”). Implicitly generated content is often considered hidden content. For example, web links and tags are hidden content; every site you link to from your own site or bookmark on a social bookmarking site could be considered a vote for that site’s impor-tance. Search engines such as Google (which uses the PageRank algorithm) use the number and quality of these links to a site to determine the importance of a site in search results.

 

Collective Intelligence

 

Collective intelligence is the concept that collaboration can result in smart ideas. Working together, users combine their knowledge for everyone’s benefit.

The first chapter of Wikinomics, by Don Tapscott and Anthony D. Williams, tells the Goldcorp story. Inspired by the community efforts in Linux, the CEO of Goldcorp released to the public proprietary geological information about the company’s land. Gold-corp offered cash rewards to people who could use this information to help the company locate gold on the land. The community helped his company find 8 million ounces of gold, catapulting Goldcorp from $100 million in stock equity to $9 billion.39 Goldcorp reaped amazing benefits by sharing information and encouraging community participation. User-generated content is significant to Web 2.0 companies because of the innovative ways companies are harnessing collective intelligence. We’ve already discussed Google’s PageRank (Section 3.3), which is a product of collective intelligence. Amazon’s and Last.fm’s personalized recommendations also result from collective intelligence, as algo-rithms evaluate user preferences to provide you with a better experience by helping you discover new products or music preferred by other people with similar interests. Wesabe is a web community where members share their decisions about money and savings—the site uses the collective financial experiences of the community to create recommenda-tions. Reputation systems (used by companies like eBay) also use collective intelligence to build trust between buyers and sellers by sharing user feedback with the community. Social bookmarking sites (Section 3.10), and social media sites (like Digg and Flickr) use collective intelligence to promote popular material, making it easier for others to find.

 

Wikis

 

Wikis, websites that allow users to edit existing content and add new information, are prime examples of user-generated content and collective intelligence. The most popular wiki is Wikipedia, a community-generated encyclopedia with articles available in over 200 languages. Wikipedia trusts its users to follow certain rules, such as not deleting accurate information and not adding biased information, while allowing community members to enforce the rules. The result has been a wealth of information growing much faster than could otherwise be produced. In 2005, an experiment comparing 42 entries from Wiki-pedia and Britannica (a popular printed traditional encyclopedia) showed only slightly more inaccuracies in the Wikipedia articles. The Wikipedia entries were promptly cor-rected, though, whereas errors in Britannica entries cannot be corrected until the book’s next printing and will remain in already printed copies.

 

Wikipedia, Wikia (a site for specialized wiki communities about popular television shows, games, literature, shopping and more) and many other wikis use MediaWiki open source software (originally developed for Wikipedia). The software can be downloaded from MediaWiki’s website (www.mediawiki.org), where you can also find descriptions, tutorials, suggestions and more to help navigate the software. Wikis are also used by many companies to provide product information, support and community resources. Social-Text, the first wiki company, provides corporate wiki services. Many companies have found that using wikis for project collaboration reduces e-mails and phone calls between employees, while allowing the ability to closely track a project’s changes.

 

Collaborative Filtering

Though collaboration can result in a wealth of knowledge, some users might submit false or faulty information. For example, Wikipedia has experienced instances of people delib-erately adding false information to entries. While moderation (monitoring of content by staff) is sometimes necessary, it is time consuming and costly. Many Web 2.0 companies rely on the community to help police their sites. This collaborative filtering lets users pro-mote valuable material and flag offensive or inappropriate material. Users have the power to choose for themselves what is important. Examples of sites using collaborative filtering include Digg, a news site where users rate the stories (see Section 3.8), and social book-marking sites such as del.icio.us, where users can easily find popular sites (see Section 3.10). Customer reviews on Amazon products also employ collaborative filter-ing—readers vote on the usefulness of each review (helping other readers to find the best reviews).

 

Craigslist

Craigslist, founded by Craig Newmark, is a popular classified ads website that has radically changed the classified advertising market. Newspapers have experienced a decline in classified ad sales,as revenues from help-wanted ads on Craigslist climbed to $50 million in 2006. Most ad postings on Craigslist are free, and it’s easy for anyone to post ads. The site has gained popularity because of its job and housing postings. In 2005, a documentary, “24 Hours on Craigslist,” showed the diverse postings that occur on the site in a single day.45 Craigslist is built on user content, leveraging the Long Tail by connecting the unique (often unusual) needs of its users. The site also uses collaborative filtering—users are encouraged to flag inappropriate postings.

Wisdom of Crowds

 

Wisdom of crowds (from the book of the same title written by James Surowiecki) is sim-ilar to collective intelligence—it suggests that a large diverse group of people (that does not necessarily include experts) can be smarter than a small group of specialists. The key dif-ference between collective intelligence and the wisdom of crowds is that the latter is not

 

meant to be a collaborative process—part of forming a reliable crowd is making sure peo-ple don’t influence each other. For example, Surowiecki describes how calculating the average of all submissions in a guessing contest (e.g., guessing the number of jelly beans in a jar) often results in nearly the correct answer, even though most individual estimates are incorrect and vary considerably. When the U.S. submarine Scorpion sank in 1968, the Navy asked various experts to work individually assessing what might have happened; their collective answers were then analyzed to determine the accurate location of the subma-rine. Practical everyday applications of the wisdom of crowds can be seen in sites employing collaborative filtering.


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