research-article

Automatic online news issue construction in web environment

Authors:

Liyun RuAuthors Info & Claims

WWW '08: Proceedings of the 17th international conference on World Wide Web

Pages 457 - 466

https://doi.org/10.1145/1367497.1367560

Published: 21 April 2008 Publication History

Abstract

In many cases, rather than a keyword search, people intend to see what is going on through the Internet. Then the integrated comprehensive information on news topics is necessary, which we called news issues, including the background, history, current progress, different opinions and discussions, etc. Traditionally, news issues are manually generated by website editors. It is quite a time-consuming hard work, and hence real-time update is difficult to perform. In this paper, a three-step automatic online algorithm for news issue construction is proposed. The first step is a topic detection process, in which newly appearing stories are clustered into new topic candidates. The second step is a topic tracking process, where those candidates are compared with previous topics, either merged into old ones or generating a new one. In the final step, news issues are constructed by the combination of related topics and updated by the insertion of new topics. An automatic online news issue construction process under practical Web circumstances is simulated to perform news issue construction experiments. F-measure of the best results is either above (topic detection) or close to (topic detection and tracking) 90%. Four news issue construction results are successfully generated in different time granularities: one meets the needs like "what's new", and the other three will answer questions like "what's hot" or "what's going on". Through the proposed algorithm, news issues can be effectively and automatically constructed with real-time update, and lots of human efforts will be released from tedious manual work.

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Index Terms

Automatic online news issue construction in web environment
1. Information systems
  1. Information retrieval
    1. Evaluation of retrieval results
    2. Retrieval tasks and goals
      1. Clustering and classification
  2. Information systems applications
    1. Data mining
      1. Clustering

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Published In

cover image ACM Conferences

WWW '08: Proceedings of the 17th international conference on World Wide Web

April 2008

1326 pages

ISBN:9781605580852

DOI:10.1145/1367497

General Chairs:
Jinpeng Huai
Beihang University, China
,
Robin Chen
AT&T Labs, USA
,
Hsiao-Wuen Hon
Microsoft Research Asia, China
,
Yunhao Liu
HK University of Science and Technology, Hong Kong
,
Program Chairs:
Wei-Ying Ma
Microsoft Research Asia, China
,
Andrew Tomkins
Yahoo! Research, USA
,
Xiaodong Zhang
The Ohio State University, USA

Copyright © 2008 ACM.

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 ACM 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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ACM: Association for Computing Machinery

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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 21 April 2008

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WWW '08

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WWW '08: The 17th International World Wide Web Conference

April 21 - 25, 2008

Beijing, China

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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https://doi.org/10.1109/TNNLS.2023.3274912
Wang SLiu XPan XXu HLiu M(2023)Heterogeneous Graph Transformer for Meta-structure Learning with Application in Text ClassificationACM Transactions on the Web10.1145/358050817:3(1-27)Online publication date: 22-May-2023
https://dl.acm.org/doi/10.1145/3580508
Xu ZShan ZLi YHu BQin BChua TLauw HSi LTerzi ETsaparas P(2023)Hansel: A Chinese Few-Shot and Zero-Shot Entity Linking BenchmarkProceedings of the Sixteenth ACM International Conference on Web Search and Data Mining10.1145/3539597.3570418(832-840)Online publication date: 27-Feb-2023
https://dl.acm.org/doi/10.1145/3539597.3570418
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Xu ZShan ZHu BZhang M(2023)Overview of NLPCC 2023 Shared Task 6: Chinese Few-Shot and Zero-Shot Entity LinkingNatural Language Processing and Chinese Computing10.1007/978-3-031-44699-3_23(257-265)Online publication date: 12-Oct-2023
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Cao GLiang YLin RWang MXu J(2022)A Chinese L2 Learners' Dynamic Vocabulary Growth Network Model Based on Graph Deep Learning2022 4th International Conference on Computer Science and Technologies in Education (CSTE)10.1109/CSTE55932.2022.00035(156-163)Online publication date: May-2022
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