Web 2.0 and Collective Intelligence Mark Levene (Follow the links to learn more!)

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Web 2.0 and Collective Intelligence Mark Levene (Follow the links to learn more!)

Web 2.0 Technologies Empowering users! Users can create, edit, modify, share, link to and tag content Examples: Delicious, Flickr, Facebook, Digg, Twitter, Wikipedia, Ebay and YouTube Technologies: Ajax, RSS, Open ApIs, Widgets, Mashups, SAAS, The Machine is Us/ing Us

Social Tagging and Bookmarking Tag your photos (Flickr) Tag your videos (YouTube) Tag web pages (Delicious) Folksonomy vs Taxonomy Tag clouds – WordleWordle Search with tags - MyTaggyMyTaggy

Opinion Mining Online shoppers are influenced by product reviews and are willing to pay more for products highly rated by other consumers. OM and sentiment analysis mines for options in user generated content. OM goes beyond Comparison Shopping. OM can be treated as a classification problem.

Collective Intelligence The emergence of new ideas as a result of a group effort. Collaborative filtering and opinion mining are examples of machine technologies. Wikis and Community Question Answering as example of human-based technologies. Crowdsourcing is the activity of outscourcing a problem to a large group of people; e.g. Amazon’s Mechnical Turk

Wikipedia – The World’s Largest Encyclopaedia Has over 14 million articles in 272 languages; the English version has over 3 million articles and could grow to 10 million by Wikipedia is very popular, and its elaborate mechanisms ensure quality despite articles not necessarily written by experts.

Ebay – The World’s Largest Online Trading Community Handles an enormous volume of content, with over 80 million active users, trading $60 billion per year, 120 million items for sale in 50,000 categories and over 2 billion pageviews per day. Has a simple auction mechanism, based on a second price auction, where the winner pays the second highest price bid plus a minimum bid increment. Susceptible to sniping, i.e. last minute bidding (allowed) and shilling, i.e. introducing fake bids to increase the final price (fraudulent).