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Visual exploration of Twitter topics

Some say a picture is worth a thousand words. And when analyzing the massive amounts of data generated by Twitter, this adage could not be more appropriate. FUSE Labs’ SocialGadgets are a set of embeddable widgets that visualize Twitter real-time data. Each gadget focuses on a given keyword and displays its volume of usage over time. Mentioned entities such as people, locations, companies and noun phrases are identified and visually represented. The gadgets are interactive, letting users explore the relationships between topics publically shared on Twitter. By focusing on patterns and trends, the gadgets can extrapolate what people find important, and provide a succinct yet effective way to look at events as they are unfolding.

Twitter, 'the pulse of the world,' is an incredibly rich data source used by millions across the globe. People post their thoughts, ideas and whereabouts via text-based status updates. Visually displaying trending topics can help readers better understand content and its context. By using any one of the four SocialGadgets, data flowing through the Twitter fire hose is indexed and semantically analyzed in real-time. Within minutes, major entities are identified and their relationships are displayed. The gadgets are easily configured, allowing users to choose a keyword and a template type, then copy and paste an embed code to be displayed on a website of their choice.

Tag Cloud

Given a keyword, this gadget displays a visual tag cloud depicting main entities found in the given time period, along with their relative time and volume of appearance.

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Timeline

A timeline centric display of related terms and their volume of appearance during a chosen period of time.

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Buzz

Given a keyword, this gadget highlights the buzz around a specific person, profile or topic. This is an ideal template to embed in a blog or profile page sidebar.

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Comparison

Volume of appearance of 2-3 terms are displayed on the same timeline. This is great way to compare levels of buzz around topics and identify major points in time where some topics spiked.

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