Abstract
TweetMogaz is a news portal platform that generates news reports from social media content. It uses an adaptive information filtering technique for tracking tweets relevant to news topics, such as politics and sports in some regions. Relevant tweets for each topic are used to generate a comprehensive report about public reaction toward events happening. Showing a news report about an entire topic may be suboptimal for some users, since users prefer story-oriented presentation. In this demonstration, we present a technique for identifying stories within a stream of microblogs on a given topic. Detected tweets on a news story are used to generate a dynamic pseudo-article that gets its content updated in real-time based on trends on Twitter. Pseudo-article consists of a title, front-page image, set of tweets on the story, and links to external news articles. The platform is running live and tracks news on hot topics including Egyptian politics, Syrian conflict, and international sports.
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