In course of a breaking news event, such as natural calamity, political uproar etc., a massive crowd sourced data is generated over social media which makes social media platforms an important source of information in such scenarios. The value of the information being propagated via social media is being increasingly realised by the news organisations and the journalists. Better tools and methodologies are needed to facilitate them in utilising this information for news production. A lot of analysis over social media, by the journalists, is performed via rigorous manual labour. However, the sheer volume of the data produced on social media is overwhelming and acts as a major obstacle for manual inspection of the streaming data for finding, aggregating and contextualising the emerging event in a short time span. This is a day-to-day challenge for journalists and media organisations. This paper addresses the above problem for journalist in handling the voluminous social media data, viewing it from an information retrieval perspective, by proposing an 'event detection and contextualisation' framework that processes an input stream of social media data into the clusters of likely events.
['Prashant Khare', 'Pablo Torres', 'Bahareh Rahmanzadeh Heravi']
What Just Happened? A Framework for Social Event Detection and Contextualisation