research-article

Personalized time-aware tweets summarization

Authors:

Shangsong Liang,

Maarten de RijkeAuthors Info & Claims

SIGIR '13: Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval

Pages 513 - 522

https://doi.org/10.1145/2484028.2484052

Published: 28 July 2013 Publication History

Abstract

We focus on the problem of selecting meaningful tweets given a user's interests; the dynamic nature of user interests, the sheer volume, and the sparseness of individual messages make this an challenging problem. Specifically, we consider the task of time-aware tweets summarization, based on a user's history and collaborative social influences from ``social circles.'' We propose a time-aware user behavior model, the Tweet Propagation Model (TPM), in which we infer dynamic probabilistic distributions over interests and topics. We then explicitly consider novelty, coverage, and diversity to arrive at an iterative optimization algorithm for selecting tweets. Experimental results validate the effectiveness of our personalized time-aware tweets summarization method based on TPM.

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Cited By

Xu EZhao KYu ZZhang YGuo BYao L(2024)Limits of predictability in top-N recommendationInformation Processing & Management10.1016/j.ipm.2024.10373161:4(103731)Online publication date: Jul-2024
https://doi.org/10.1016/j.ipm.2024.103731
Jones SKlein MWeigle MNelson M(2023)Summarizing Web Archive Corpora via Social Media Storytelling by Automatically Selecting and Visualizing ExemplarsACM Transactions on the Web10.1145/360603018:1(1-48)Online publication date: 11-Oct-2023
https://dl.acm.org/doi/10.1145/3606030
Dusart APinel-Sauvagnat KHubert G(2023)TSSuBERT: How to Sum Up Multiple Years of Reading in a Few TweetsACM Transactions on Information Systems10.1145/358178641:4(1-33)Online publication date: 10-Apr-2023
https://dl.acm.org/doi/10.1145/3581786
Show More Cited By

Index Terms

Personalized time-aware tweets summarization
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

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cover image ACM Conferences

SIGIR '13: Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval

July 2013

1188 pages

ISBN:9781450320344

DOI:10.1145/2484028

General Chairs:
Gareth J.F. Jones
Dublin City University, Ireland
,
Páraic Sheridan
Dublin City University, Ireland
,
Program Chairs:
Diane Kelly
University of North Carolina, Chapel Hill, USA
,
Maarten de Rijke
University of Amsterdam, The Netherlands
,
Tetsuya Sakai
Microsoft Research Asia, China

Copyright © 2013 ACM.

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SIGIR: ACM Special Interest Group on Information Retrieval

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New York, NY, United States

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Published: 28 July 2013

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SIGIR '13

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SIGIR

SIGIR '13: The 36th International ACM SIGIR conference on research and development in Information Retrieval

July 28 - August 1, 2013

Dublin, Ireland

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SIGIR '13 Paper Acceptance Rate 73 of 366 submissions, 20%;

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

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Cited By

Xu EZhao KYu ZZhang YGuo BYao L(2024)Limits of predictability in top-N recommendationInformation Processing & Management10.1016/j.ipm.2024.10373161:4(103731)Online publication date: Jul-2024
https://doi.org/10.1016/j.ipm.2024.103731
Jones SKlein MWeigle MNelson M(2023)Summarizing Web Archive Corpora via Social Media Storytelling by Automatically Selecting and Visualizing ExemplarsACM Transactions on the Web10.1145/360603018:1(1-48)Online publication date: 11-Oct-2023
https://dl.acm.org/doi/10.1145/3606030
Dusart APinel-Sauvagnat KHubert G(2023)TSSuBERT: How to Sum Up Multiple Years of Reading in a Few TweetsACM Transactions on Information Systems10.1145/358178641:4(1-33)Online publication date: 10-Apr-2023
https://dl.acm.org/doi/10.1145/3581786
Chen XLi MGao SChan ZZhao DGao XZhang XYan R(2023)Follow the Timeline! Generating an Abstractive and Extractive Timeline Summary in Chronological OrderACM Transactions on Information Systems10.1145/351722141:1(1-30)Online publication date: 9-Jan-2023
https://dl.acm.org/doi/10.1145/3517221
Singh TKumari MGupta DSiniak N(2023)Axiomatic Analysis of Pre‐Processing Methodologies Using Machine Learning in Text MiningConvergence of Cloud with AI for Big Data Analytics10.1002/9781119905233.ch11(229-256)Online publication date: 10-Feb-2023
https://doi.org/10.1002/9781119905233.ch11
Fariha ABrucato MHaas PMeliou A(2020)SuDocuProceedings of the VLDB Endowment10.14778/3415478.341549413:12(2861-2864)Online publication date: 14-Sep-2020
https://dl.acm.org/doi/10.14778/3415478.3415494
Zhang PSoergel D(2020)Cognitive mechanisms in sensemakingJournal of the Association for Information Science and Technology10.1002/asi.2422171:2(158-171)Online publication date: 1-Jan-2020
https://dl.acm.org/doi/10.1002/asi.24221
Pang JTao FHuang QTian QYin B(2019)Two Birds With One Stone: A Coupled Poisson Deconvolution for Detecting and Describing Topics From Multimodal Web DataIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2018.287299730:8(2397-2409)Online publication date: Aug-2019
https://doi.org/10.1109/TNNLS.2018.2872997
Chin JBhowmick SJatowt A(2019)On‐demand recent personal tweets summarization on mobile devicesJournal of the Association for Information Science and Technology10.1002/asi.2413770:6(547-562)Online publication date: 22-Apr-2019
https://dl.acm.org/doi/10.1002/asi.24137
Wu YLuo XXu XGuo SShi YAizawa KLew MSatoh S(2018)Dictionary Learning based Supervised Discrete Hashing for Cross-Media RetrievalProceedings of the 2018 ACM on International Conference on Multimedia Retrieval10.1145/3206025.3206045(222-230)Online publication date: 5-Jun-2018
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