Social event detection with interaction graph modeling

Yanxiang Wang*, Hari Sundaram, Lexing Xie

*Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    38 Citations (Scopus)

    Abstract

    This paper focuses on detecting social, physical-world events from photos posted on social media sites. The problem is important: cheap media capture devices have significantly increased the number of photos shared on these sites. The main contribution of this paper is to incorporate online social interaction features in the detection of physical events. We believe that online social interaction reflect important signals among the participants on the "social affinity" of two photos, thereby helping event detection. We compute social affinity via a random-walk on a social interaction graph to determine similarity between two photos on the graph. We train a support vector machine classifier to combine the social affinity between photos and photo-centric metadata including time, location, tags and description. Incremental clustering is then used to group photos to event clusters. We have very good results on two large scale real-world datasets: Upcoming and MediaEval. We show an improvement between 0.06-0.10 in F1 on these datasets.

    Original languageEnglish
    Title of host publicationMM 2012 - Proceedings of the 20th ACM International Conference on Multimedia
    PublisherAssociation for Computing Machinery
    Pages865-868
    Number of pages4
    ISBN (Print)9781450310895
    DOIs
    Publication statusPublished - 2012
    Event20th ACM International Conference on Multimedia, MM 2012 - Nara, Japan
    Duration: 29 Oct 20122 Nov 2012

    Publication series

    NameMM 2012 - Proceedings of the 20th ACM International Conference on Multimedia

    Conference

    Conference20th ACM International Conference on Multimedia, MM 2012
    Country/TerritoryJapan
    CityNara
    Period29/10/122/11/12

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