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2010
ACM

Learning Similarity Metrics for Event Identification in Social Media

11 years 1 months ago
Learning Similarity Metrics for Event Identification in Social Media
Social media sites (e.g., Flickr, YouTube, and Facebook) are a popular distribution outlet for users looking to share their experiences and interests on the Web. These sites host substantial amounts of user-contributed materials (e.g., photographs, videos, and textual content) for a wide variety of real-world events of different type and scale. By automatically identifying these events and their associated user-contributed social media documents, which is the focus of this paper, we can enable event browsing and search in state-of-the-art search engines. To address this problem, we exploit the rich "context" associated with social media content, including user-provided annotations (e.g., title, tags) and automatically generated information (e.g., content creation time). Using this rich context, which includes both textual and non-textual features, we can define appropriate document similarity metrics to enable online clustering of media to events. As a key contribution of th...
Hila Becker, Mor Naaman, Luis Gravano
Added 01 Mar 2010
Updated 02 Mar 2010
Type Conference
Year 2010
Where WSDM
Authors Hila Becker, Mor Naaman, Luis Gravano
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