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First Story Detection using Multiple Nearest Neighbors

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SIGIR '16: Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval

Pages 845 - 848

https://doi.org/10.1145/2911451.2914761

Published: 07 July 2016 Publication History

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Abstract

First Story Detection (FSD) systems aim to identify those news articles that discuss an event that was not reported before. Recent work on FSD has focussed almost exclusively on efficiently detecting documents that are dissimilar from their nearest neighbor. We propose a novel FSD approach that is more effective, by adapting a recently proposed method for news summarization based on 3-nearest neighbor clustering. We show that this approach is more effective than a baseline that uses dissimilarity of an individual document from its nearest neighbor.

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Cited By

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Elbedwehy SThron CAlrahmawy MHamza T(2022)Real-Time Detection of First Stories in Twitter Using a FastText ModelArtificial Intelligence for Data Science in Theory and Practice10.1007/978-3-030-92245-0_9(179-218)Online publication date: 2022
https://doi.org/10.1007/978-3-030-92245-0_9
Panagiotou NAkkaya CTsioutsiouliklis KKalogeraki VGunopulos D(2021)A General Framework for First Story Detection Utilizing Entities and Their RelationsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2020.297005133:11(3482-3493)Online publication date: 1-Nov-2021
https://dl.acm.org/doi/10.1109/TKDE.2020.2970051
Bonchi FBordino IGullo FStilo G(2019)The importance of unexpectedness: Discovering buzzing stories in anomalous temporal graphsWeb Intelligence10.3233/WEB-19041217:3(177-198)Online publication date: 16-Aug-2019
https://doi.org/10.3233/WEB-190412
Show More Cited By

Index Terms

First Story Detection using Multiple Nearest Neighbors
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering

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Published In

SIGIR '16: Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval

July 2016

1296 pages

ISBN:9781450340694

DOI:10.1145/2911451

General Chairs:
Raffaele Perego
ISTI-CNR, Italy
,
Fabrizio Sebastiani
Qatar Computing Research Institute, HBKU, Qatar
,
Program Chairs:
Javed Aslam
Northeastern University, US
,
Ian Ruthven
University of Strathclyde, UK
,
Justin Zobel
University of Melbourne, Australia

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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 July 2016

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first story detection

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Short-paper

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SIGIR '16

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SIGIR

SIGIR '16: The 39th International ACM SIGIR conference on research and development in Information Retrieval

July 17 - 21, 2016

Pisa, Italy

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SIGIR '16 Paper Acceptance Rate 62 of 341 submissions, 18%;

Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

View all

Elbedwehy SThron CAlrahmawy MHamza T(2022)Real-Time Detection of First Stories in Twitter Using a FastText ModelArtificial Intelligence for Data Science in Theory and Practice10.1007/978-3-030-92245-0_9(179-218)Online publication date: 2022
https://doi.org/10.1007/978-3-030-92245-0_9
Panagiotou NAkkaya CTsioutsiouliklis KKalogeraki VGunopulos D(2021)A General Framework for First Story Detection Utilizing Entities and Their RelationsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2020.297005133:11(3482-3493)Online publication date: 1-Nov-2021
https://dl.acm.org/doi/10.1109/TKDE.2020.2970051
Bonchi FBordino IGullo FStilo G(2019)The importance of unexpectedness: Discovering buzzing stories in anomalous temporal graphsWeb Intelligence10.3233/WEB-19041217:3(177-198)Online publication date: 16-Aug-2019
https://doi.org/10.3233/WEB-190412
Ma KYu ZJi KYang B(2019)Stream-based live public opinion monitoring approach with adaptive probabilistic topic modelSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-018-3391-723:16(7451-7470)Online publication date: 1-Aug-2019
https://dl.acm.org/doi/10.1007/s00500-018-3391-7

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