social world, people are more concern about the social media such as Twitter
updates, because of the regular are frequent update in the Twitter tweets about
the event or news occurred the impact is huge. This update floods the complete
media world from all sorts of people commenting or complementing the stories.
Some social up- dates can support professionally written news articles. In this
work we consider the problem of annotating news stories with supporting social
updates (tweets), and the high volume of news stories. The rate of news and
tweets are very high - pageviews (millions to billions a day) and tweets (more
than 100 millions a day). This makes the real-time indexing of tweets very
difficult, as this requires an indexing of frequently updated social content.
So we propose an architecture where each story is treated as a subscription for
tweets relevant to the stories content and algorithms that effectively matches
tweets to stories. Also it’s vital to maintain top-k tweets to stories, which
can be fulfilled by publisher and subscriber model. We demonstrate the
effectiveness of our approach on real world data.
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