Mining the Web for Multimedia-Based Enriching
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As the amount of social media shared on the Internet grows increasingly, it becomes possible to explore a topic with a novel, people based viewpoint. We aim at performing topic enriching using media items mined from social media sharing platforms. Nevertheless, such data collected from the Web is likely to contain noise, hence the need to further process collected documents to ensure relevance. To this end, we designed an approach to automatically propose a cleaned set of media items related to events mined from search trends. Events are described using word tags and a pool of videos is linked to each event in order to propose relevant content. This pool has previously been filtered out from non-relevant data using information retrieval techniques. We report the results of our approach by automatically illustrating the popular moments of four celebrities.
KeywordsOutlier Detection Query Expansion Relevant Content Media Item Outlier Score
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