Abstract

Social tagging has become an interesting approach to improve search and navigation over the actual Web, since it aggregates the tags added by different users to the same resource in a collaborative way. This way, it results in a list of weighted tags describing its resource. Combined to a classical taxonomic classification system such as that by Wikipedia, social tags can enhance document navigation and search. On the one hand, social tags suggest alternative navigation ways, including pivot-browsing, popularity-driven navigation, and filtering. On the other hand, it provides new metadata, sometimes uncovered by the documents’ content, that can substantially improve document search. In this work, the inclusion of an interface to add user-defined tags describing Wikipedia articles is proposed, as a way to improve article navigation and retrieval.

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Unlike Twitter’s own search engine, where the tweets containing the search term(s) are shown as a result, OneRiot’s Twitter searcher goes further. When you type and submit a search term in this engine, it returns interesting links (that users have shared on Twitter) about that search term. When selecting what links to show you as a result, the system bases on what have users said about them in the corresponding tweets.

Look, for instance, for a search on readwriteweb. Numerous links pointing to this well-known site are returned. For each of these links, you can know how many tweets have talked about it. Moreover, if you click over this info, a summary of these tweets is shown: what has been the latest conversation on that link, and who discovered (first-tweeted) the link. Note that apparently some tweets point to different links, since they are usually tinyurl’d or somewhat. Another interesting way to search over tweets

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