TwiBiNG: A Bipartite News Generator Using Twitter

Online Journalism is being seen as future of Journalism. News Professionals are vying to capture newsworthy stories that emerge from crowd. Live Social Media especially Twitter is generating enormous volumes of data every minute. It becomes difficult to select credible and relevant tweets that may form quality news among others. The problem intensifies due to the freedom of Twitter being an informal language. Generating headlines by solving this problem may still not be relevant and may face the question of authenticity. Given a set of keywords and a time period this problem becomes manageable and can be solved efficiently. We propose a bipartite algorithm that clusters authentic tweets based on key phrases and ranks the clusters based on trends in each timeslot. Finally, we present an approach to select those topics which have sufficient content to form a story

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