Learning similarity metrics for event identification in social media

Learning similarity metrics for event identification in social media,10.1145/1718487.1718524,Hila Becker,Mor Naaman,Luis Gravano

Learning similarity metrics for event identification in social media   (Citations: 16)
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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 va- riety of real-world events of dierent 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 con- tent, including user-provided annotations (e.g., title, tags) and automatically generated information (e.g., content cre- ation time). Using this rich context, which includes both textual and non-textual features, we can dene appropriate document similarity metrics to enable online clustering of media to events. As a key contribution of this paper, we ex- plore a variety of techniques for learning multi-feature sim- ilarity metrics for social media documents in a principled manner. We evaluate our techniques on large-scale, real- world datasets of event images from Flickr. Our evaluation results suggest that our approach identies events, and their associated social media documents, more eectively
Conference: Web Search and Data Mining - WSDM , pp. 291-300, 2010
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    • ...clustering images according to events [1], [2], and clustering web pages according to persons [3], [4]...
    • ...Therefore, recent approaches used machine learned similarity measures [1], [2], [4]...

    K. Buzaet al. Graph-based clustering based on cutting sets

    • ...Likewise, in [5, 10, 35] the authors use various modalities of photos (i.e...

    Spiros Nikolopouloset al. Combining Multi-modal Features for Social Media Analysis

    • ...Much of what is discussed in social media is inspired by the news (e.g., 85% of Twitter statuses are news-related [22]) and, vice versa, social media provide us with a handle on the impact of news events [5, 25, 27, 46]...
    • ...Which of these is helpful in identifying implicitly linked social media utterances? Alternatively, one can try to identify a selection of “representative” terms from the article [2, 5, 17]...

    Manos Tsagkiaset al. Linking online news and social media

    • ...Event detection: Event detection has been studied in the past [7, 11, 16, 3]. Many of those works focus on the problem of detecting events from documents based on language patterns, with the emphasis on extracting location and time information about the events...

    Anish Das Sarmaet al. Dynamic relationship and event discovery

    • ...Landmark and event detection have been usually dealt with as separate problems; for instance, the works in [3, 5, 6, 9, 14] deal with the problem of landmark recognition, while the works in [1, 2, 4] address the problem of event detection in social media...

    Symeon Papadopouloset al. ClustTour: city exploration by use of hybrid photo clustering

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