Story line: Unsupervised geo-event demultiplexing in social spaces without location information

Shiguang Wang, Prasanna Giridhar, Hongwei Wang, Lance Kaplan, Tien Pham, Aylin Yener, Tarek Abdelzaher

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Some of the most widely deployed IoT devices in urban areas are smartphones in the possession of urban individuals. Their proliferation has led to the emergence of crowdsensing/crowdsourcing services, where humans collect data about their environment (using phones), and servers aggregate the data for various application purposes of interest. With the emergence of social media, a common alternative form of human data entry has become media posts (e.g., on Twitter). This leads to the prospect of building crowdsensing services on top of social media content, exploiting humans as "sensors". In this paper, we develop one such service, called StoryLine. the service detects and tracks physical urban events of interest to the user, such as car accidents, infrastructure damage (in the aftermath of a natural disaster), or instances of civil unrest. It others an interface to client-side software that allows browsing such events in real time, as well as an interface for software applications to a structured representation of the events and their related statistics. the service embodies novel algorithms for real-time detection, demultiplexing, and tracking of physical events using social media data. In our evaluation with Twitter feeds, we show that our service outperforms two state-of-the-art baselines in event detection and demultiplexing. We also conduct two case-studies to show the effectiveness of the real-time event detection capability and event tracking performance of our system.

Original languageEnglish (US)
Title of host publicationProceedings - 2017 IEEE/ACM 2nd International Conference on Internet-of-Things Design and Implementation, IoTDI 2017 (part of CPS Week)
PublisherAssociation for Computing Machinery, Inc
Pages83-93
Number of pages11
ISBN (Electronic)9781450349666
DOIs
StatePublished - Apr 18 2017
Event2nd IEEE/ACM International Conference on Internet-of-Things Design and Implementation, IoTDI 2017 - Pittsburgh, United States
Duration: Apr 18 2017Apr 20 2017

Publication series

NameProceedings - 2017 IEEE/ACM 2nd International Conference on Internet-of-Things Design and Implementation, IoTDI 2017 (part of CPS Week)

Other

Other2nd IEEE/ACM International Conference on Internet-of-Things Design and Implementation, IoTDI 2017
CountryUnited States
CityPittsburgh
Period4/18/174/20/17

Keywords

  • Event tracking
  • Social sensing

ASJC Scopus subject areas

  • Hardware and Architecture
  • Control and Systems Engineering
  • Computer Networks and Communications

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  • Cite this

    Wang, S., Giridhar, P., Wang, H., Kaplan, L., Pham, T., Yener, A., & Abdelzaher, T. (2017). Story line: Unsupervised geo-event demultiplexing in social spaces without location information. In Proceedings - 2017 IEEE/ACM 2nd International Conference on Internet-of-Things Design and Implementation, IoTDI 2017 (part of CPS Week) (pp. 83-93). (Proceedings - 2017 IEEE/ACM 2nd International Conference on Internet-of-Things Design and Implementation, IoTDI 2017 (part of CPS Week)). Association for Computing Machinery, Inc. https://doi.org/10.1145/3054977.3054992