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CAST: A Context-Aware Story-Teller for Streaming Social Content

Published:03 November 2014Publication History

ABSTRACT

Online social streams such as Twitter timelines, forum discussions and email threads have emerged as important channels for information propagation. Mining transient stories and their correlations implicit in social streams is a challenging task, since these streams are noisy and surge quickly. In this paper, we propose CAST, which is a context-aware story-teller that discovers new stories from social streams and tracks their structural context on the fly to build a vein of stories. More precisely, we model the social stream as a capillary network, and define stories by a new cohesive subgraph type called (k,d)-Core in the capillary network. We propose deterministic and randomized context search to support the iceberg query, which builds the story vein as social streams flow. We perform detailed experimental study on real Twitter streams and the results demonstrate the creativity and value of our approach.

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      • Published in

        cover image ACM Conferences
        CIKM '14: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management
        November 2014
        2152 pages
        ISBN:9781450325981
        DOI:10.1145/2661829

        Copyright © 2014 ACM

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        Publication History

        • Published: 3 November 2014

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        CIKM '14 Paper Acceptance Rate175of838submissions,21%Overall Acceptance Rate1,861of8,427submissions,22%

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