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Efficient Tracking of Breaking News in Twitter

Published: 26 June 2019 Publication History

Abstract

We present an efficient graph-based method for filtering tweets relevant to a given breaking news from large tweet streams. Unlike existing models that either require manual effort, strong supervision, and/or not scalable, our method can automatically and effectively filter incoming relevant tweets starting from just a small number of past relevant tweets. Extensive experiments on both synthetic and real datasets show that our proposed method significantly outperforms other methods in filtering the relevant tweets while being as fast as the most efficient state-of-the-art method.

References

[1]
Juan M Cotelo, Fermin L Cruz, and Jose A Troyano. Dynamic topic-related tweet retrieval. JAIST, 65(3):513--523, 2014.
[2]
Tuan-Anh Hoang, Thi-Huyen Nguyen, and Wolfgang Nejdl. Efficient tracking of breaking news in twitter. https://www.dropbox.com/s/uz54vtp8z3ort68/attt.pdf.
[3]
Walid Magdy and Tamer Elsayed. Unsupervised adaptive microblog filtering for broad dynamic topics. IPM, 52(4):513--528, 2016.
[4]
Alexandra Olteanu, Carlos Castillo, Fernando Diaz, and Sarah Vieweg. Crisislex: A lexicon for collecting and filtering microblogged communications in crises. In ICWSM, 2014.
[5]
Wayne Xin Zhao, Jing Jiang, Jianshu Weng, Jing He, Ee-Peng Lim, Hongfei Yan, and Xiaoming Li. Comparing twitter and traditional media using topic models. In ECIR, 2011.

Cited By

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  • (2024)A Trustworthy Approach to Classify and Analyze Epidemic-Related Information From MicroblogsIEEE Transactions on Computational Social Systems10.1109/TCSS.2024.339139511:5(6229-6241)Online publication date: Oct-2024
  • (2023)Learning Faithful Attention for Interpretable Classification of Crisis-Related Microblogs under Constrained Human BudgetProceedings of the ACM Web Conference 202310.1145/3543507.3583861(3959-3967)Online publication date: 30-Apr-2023
  • (2022)A Semantic Preprocessing Framework for Breaking News Detection to Support Future Drone Journalism ServicesFuture Internet10.3390/fi1401002614:1(26)Online publication date: 10-Jan-2022

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Published In

cover image ACM Conferences
WebSci '19: Proceedings of the 10th ACM Conference on Web Science
June 2019
395 pages
ISBN:9781450362023
DOI:10.1145/3292522
Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 June 2019

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Author Tags

  1. realtime filtering
  2. social media
  3. twitter

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WebSci '19
Sponsor:
WebSci '19: 11th ACM Conference on Web Science
June 30 - July 3, 2019
Massachusetts, Boston, USA

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WebSci '19 Paper Acceptance Rate 41 of 130 submissions, 32%;
Overall Acceptance Rate 245 of 933 submissions, 26%

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Cited By

View all
  • (2024)A Trustworthy Approach to Classify and Analyze Epidemic-Related Information From MicroblogsIEEE Transactions on Computational Social Systems10.1109/TCSS.2024.339139511:5(6229-6241)Online publication date: Oct-2024
  • (2023)Learning Faithful Attention for Interpretable Classification of Crisis-Related Microblogs under Constrained Human BudgetProceedings of the ACM Web Conference 202310.1145/3543507.3583861(3959-3967)Online publication date: 30-Apr-2023
  • (2022)A Semantic Preprocessing Framework for Breaking News Detection to Support Future Drone Journalism ServicesFuture Internet10.3390/fi1401002614:1(26)Online publication date: 10-Jan-2022

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