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Recently, one of the most popular applications on the Internet is the social networking, such as, Facebook and Twitter. Finding resources using recent Peer-to-Peer (P2P) schemes in a social network has limits, such as distance problem between peers etc. So, in this paper, we present an efficient social P2P management
present a more informative result compared to conventional search engine. To valid our method, we developed the TCOND system (Twitter Conversation Detector) which offers an alternative, results to keyword search on twitter and Google. We have evaluated our method on collected social network corpus related to specific
which follows a keyword over multiple social media platforms (e.g. Twitter, Facebook), maintaining the aggregated data in a no-SQL database. Afterwards, in order to choose the most suitable system for our application, we will analyze the no_SQL database systems, define their architecture and data model, and depending on
Twitter is a public social service that allows users to share information as short-text messages. Previous researchers have tried to analyze the information available on Twitter to discover topic trending. However, these topics are associated with the whole network, and are not associated to a particular place. In
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.