poster

Real-time Filtering on Interest Profiles in Twitter Stream

Authors:

Yue Fei,

Chao Lv,

Yansong Feng,

Dongyan ZhaoAuthors Info & Claims

JCDL '16: Proceedings of the 16th ACM/IEEE-CS on Joint Conference on Digital Libraries

Pages 263 - 264

https://doi.org/10.1145/2910896.2925462

Published: 19 June 2016 Publication History

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Abstract

The advent of Twitter has led to the ubiquitous information overload problem with a dramatic increase in the amount of tweets a user is exposed to. In this paper, we consider real-time tweet filtering with respect to users' interest profiles in public Twitter stream. While traditional filtering methods mainly focus on judging relevance of a document, we aim to retrieve relevant and novel documents to address the high redundancy of tweets. An unsupervised approach is proposed to model relevance between tweets and different profiles adaptively and a neural network language model is employed to learn semantic representation for tweets. Experiments on TREC 2015 dataset demonstrate the effectiveness of the proposed approach.

References

[1]

M. Albakour, C. Macdonald, I. Ounis, et al. On sparsity and drift for effective real-time filtering in microblogs. In Proceedings of the 22nd ACM international conference on Conference on information & knowledge management, pages 419--428. ACM, 2013.

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[2]

Y. Fei, Y. Hong, and J. Yang. Handling topic drift for topic tracking in microblogs. In Advances in Information Retrieval, pages 477--488. Springer, 2015.

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[3]

Q. V. Le and T. Mikolov. Distributed representations of sentences and documents. arXiv preprint arXiv:1405.4053, 2014.

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[4]

J. Lin, M. Efron, Y. Wang, G. Sherman, and E. Voorhees. Overview of the trec-2015 microblog track. In Proceedings of TREC, volume 2015, 2015.

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T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean. Distributed representations of words and phrases and their compositionality. In Advances in neural information processing systems, pages 3111--3119, 2013.

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Cited By

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Misiorek PCiesielczyk MRzycki B(2023)Digital Content Profiling Based on User Engagement FeaturesInformation Systems10.1007/978-3-031-30694-5_8(91-104)Online publication date: 20-Apr-2023
https://doi.org/10.1007/978-3-031-30694-5_8
Misiorek PWarmuz JKaczmarek DCiesielczyk M(2022)Modeling User Engagement Profiles for Detection of Digital Subscription PropensityInformation Systems10.1007/978-3-030-95947-0_5(55-68)Online publication date: 16-Feb-2022
https://doi.org/10.1007/978-3-030-95947-0_5

Index Terms

Real-time Filtering on Interest Profiles in Twitter Stream
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
  2. Information systems applications
    1. Data mining
      1. Data stream mining

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Information

Published In

JCDL '16: Proceedings of the 16th ACM/IEEE-CS on Joint Conference on Digital Libraries

June 2016

316 pages

ISBN:9781450342292

DOI:10.1145/2910896

General Chairs:
Nabil R. Adam
Rutgers University
,
Boots Cassel
Villanova University
,
Yelena Yesha
University of Maryland, Baltimore County
,
Program Chairs:
Richard Furuta
Texas A&M University
,
Michele C. Weigle
Old Dominion University

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

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Published: 19 June 2016

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National Nature Science Foundation of China

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JCDL '16

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JCDL '16: The 16th ACM/IEEE-CS Joint Conference on Digital Libraries

June 19 - 23, 2016

New Jersey, Newark, USA

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JCDL '16 Paper Acceptance Rate 15 of 52 submissions, 29%;

Overall Acceptance Rate 415 of 1,482 submissions, 28%

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Misiorek PCiesielczyk MRzycki B(2023)Digital Content Profiling Based on User Engagement FeaturesInformation Systems10.1007/978-3-031-30694-5_8(91-104)Online publication date: 20-Apr-2023
https://doi.org/10.1007/978-3-031-30694-5_8
Misiorek PWarmuz JKaczmarek DCiesielczyk M(2022)Modeling User Engagement Profiles for Detection of Digital Subscription PropensityInformation Systems10.1007/978-3-030-95947-0_5(55-68)Online publication date: 16-Feb-2022
https://doi.org/10.1007/978-3-030-95947-0_5

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Twitter message recommendation based on user interest profiles

Adaptive Evolutionary Filtering in Real-Time Twitter Stream

On the impact of entity linking in microblog real-time filtering

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