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Revealing the Hidden Links in Content Networks: An Application to Event Discovery

Published: 06 November 2017 Publication History

Abstract

Social networks have become the de facto online resource for people to share, comment on and be informed about events pertinent to their interests and livelihood, ranging from road traffic or an illness to concerts and earthquakes, to economics and politics. This has been the driving force behind research endeavors that analyse such data. In this paper, we focus on how Content Networks can help us identify events effectively. Content Networks incorporate both structural and content-related information of a social network in a unified way, at the same time, bringing together two disparate lines of research: graph-based and content-based event discovery in social media. We model interactions of two types of nodes, users and content, and introduce an algorithm that builds heterogeneous, dynamic graphs, in addition to revealing content links in the network's structure. By linking similar content nodes and tracking connected components over time, we can effectively identify different types of events. Our evaluation on social media streaming data suggests that our approach outperforms state-of-the-art techniques, while showcasing the significance of hidden links to the quality of the results.

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Cited By

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  • (2021)News Monitor: A Framework for Exploring News in Real-TimeData10.3390/data70100037:1(3)Online publication date: 27-Dec-2021
  • (2021)News Monitor: A Framework for Querying News in Real TimeAdvances in Information Retrieval10.1007/978-3-030-72240-1_62(543-548)Online publication date: 30-Mar-2021
  • (2018)Detection and Delineation of Events and Sub-Events in Social Networks2018 IEEE 34th International Conference on Data Engineering (ICDE)10.1109/ICDE.2018.00147(1348-1351)Online publication date: Apr-2018

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cover image ACM Conferences
CIKM '17: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management
November 2017
2604 pages
ISBN:9781450349185
DOI:10.1145/3132847
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Publication History

Published: 06 November 2017

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Author Tags

  1. anomaly detection
  2. event discovery
  3. graph mining
  4. social network analysis

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CIKM '17 Paper Acceptance Rate 171 of 855 submissions, 20%;
Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

View all
  • (2021)News Monitor: A Framework for Exploring News in Real-TimeData10.3390/data70100037:1(3)Online publication date: 27-Dec-2021
  • (2021)News Monitor: A Framework for Querying News in Real TimeAdvances in Information Retrieval10.1007/978-3-030-72240-1_62(543-548)Online publication date: 30-Mar-2021
  • (2018)Detection and Delineation of Events and Sub-Events in Social Networks2018 IEEE 34th International Conference on Data Engineering (ICDE)10.1109/ICDE.2018.00147(1348-1351)Online publication date: Apr-2018

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