CrowdStory: Multi-layered event storyline generation with mobile crowdsourced data

Jiafan Zhang, Yi Ouyang, Bin Guo, Zhiwen Yu, Qi Han

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

4 Scopus citations

Abstract

Online social media, like Twitter, can be viewed as a kind of source of mobile crowdsourced data. It can be utilized to understand real-world social events. Recently, many researchers have worked on event summarization from this kind of data. However, those works fail to present the dynamic evolution of the event and characterize the rich correlations among posts. To this end, we propose a multilayered model for social event characterization. Based on this model, an evolutionary and multi-clue-based approach is adopted for fine-grained event summary, and a progressively multi-layer fusion approach is utilized to generate the storyline to summarize the event. Experiments show the effectiveness of our approach.

Original languageEnglish
Title of host publicationUbiComp 2016 Adjunct - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing
PublisherAssociation for Computing Machinery, Inc
Pages237-240
Number of pages4
ISBN (Electronic)9781450344623
DOIs
StatePublished - 12 Sep 2016
Event2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2016 - Heidelberg, Germany
Duration: 12 Sep 201616 Sep 2016

Publication series

NameUbiComp 2016 Adjunct - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing

Conference

Conference2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2016
Country/TerritoryGermany
CityHeidelberg
Period12/09/1616/09/16

Keywords

  • Event summary
  • Mobile crowdsourced data
  • Multi-layer

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