research-article

Automatical Storyline Generation with Help from Twitter

Authors:

Naren RamakrishnanAuthors Info & Claims

CIKM '16: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management

Pages 2383 - 2388

https://doi.org/10.1145/2983323.2983698

Published: 24 October 2016 Publication History

Abstract

Storyline detection aims to connect seemly irrelevant single documents into meaningful chains, which provides opportunities for understanding how events evolve over time and what triggers such evolutions. Most previous work generated the storylines through unsupervised methods that can hardly reveal underlying factors driving the evolution process. This paper introduces a Bayesian model to generate storylines from massive documents and infer the corresponding hidden relations and topics. In addition, our model is the first attempt that utilizes Twitter data as human input to ``supervise'' the generation of storylines. Through extensive experiments, we demonstrate our proposed model can achieve significant improvement over baseline methods and can be used to discover interesting patterns for real world cases.

References

[1]

David M Blei and John D Lafferty. Dynamic topic models. In Proceedings of the 23rd international conference on Machine learning, pages 113--120. ACM, 2006.

Digital Library

[2]

David M Blei, Andrew Y Ng, and Michael I Jordan. Latent dirichlet allocation. In The Journal of Machine Learning Research, volume 3, pages 993--1022. MIT Press, 2003.

Digital Library

[3]

Ting Hua, Ning Yue, Feng Chen, Chang-Tien Lu, and Naren Ramakrishnan. Topical analysis of interactions between news and social media. In Proceedings of the 30th AAAI Conference on Artificial Intelligence, pages 2964--2971, 2016.

[4]

Lifu Huang and Lian'en Huang. Optimized event storyline generation based on mixture-event-aspect model. In Proceedings of the 12th Conference on Empirical Methods in Natural Language Processing, pages 726--735, 2013.

[5]

Ou Jin, Nathan N Liu, Kai Zhao, Yong Yu, and Qiang Yang. Transferring topical knowledge from auxiliary long texts for short text clustering. In Proceedings of the 20th ACM international conference on Information and knowledge management, pages 775--784. ACM, 2011.

Digital Library

[6]

Noriaki Kawamae. Trend analysis model: trend consists of temporal words, topics, and timestamps. In Proceedings of the 4th ACM international conference on Web search and data mining, pages 317--326. ACM, 2011.

Digital Library

[7]

Haewoon Kwak, Changhyun Lee, Hosung Park, and Sue Moon. What is twitter, a social network or a news media? In Proceedings of the 19th international conference on World wide web, pages 591--600. ACM, 2010.

Digital Library

[8]

Theodoros Lappas, Benjamin Arai, Manolis Platakis, Dimitrios Kotsakos, and Dimitrios Gunopulos. On burstiness-aware search for document sequences. In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 477--486. ACM, 2009.

Digital Library

[9]

Jiwei Li and Sujian Li. Evolutionary hierarchical dirichlet process for timeline summarization. In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, pages 556--560, 2013.

[10]

Chen Lin, Chun Lin, Jingxuan Li, Dingding Wang, Yang Chen, and Tao Li. Generating event storylines from microblogs. In Proceedings of the 21st ACM international conference on Information and knowledge management, pages 175--184. ACM, 2012.

Digital Library

[11]

Craig Macdonald, Richard McCreadie, Miles Osborne, Iadh Ounis, Sa\vsa Petrovic, and Luke Shrimpton. Can twitter replace newswire for breaking news. In Proceedings of the 7th International AAAI Conference on Weblogs and Social Media, 2013.

[12]

Qiaozhu Mei and ChengXiang Zhai. Discovering evolutionary theme patterns from text: An exploration of temporal text mining. In Proceedings of the 11th ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, pages 198--207. ACM, 2005.

Digital Library

[13]

Daniel Ramage, Susan T Dumais, and Daniel J Liebling. Characterizing microblogs with topic models. In Proceedings of the 4th International AAAI Conference on Weblogs and Social Media, pages 130--137, 2010.

[14]

Dafna Shahaf, Jaewon Yang, Caroline Suen, Jeff Jacobs, Heidi Wang, and Jure Leskovec. Information cartography: creating zoomable, large-scale maps of information. In Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 1097--1105. ACM, 2013.

Digital Library

[15]

Lidan Shou, Zhenhua Wang, Ke Chen, and Gang Chen. Sumblr: continuous summarization of evolving tweet streams. In Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval, pages 533--542. ACM, 2013.

Digital Library

[16]

Mark Steyvers, Padhraic Smyth, Michal Rosen-Zvi, and Thomas Griffiths. Probabilistic author-topic models for information discovery. In Proceedings of the 10th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 306--315. ACM, 2004.

Digital Library

[17]

Xuerui Wang and Andrew McCallum. Topics over time: a non-markov continuous-time model of topical trends. In Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 424--433. ACM, 2006.

Digital Library

[18]

Rui Yan, Xiaojun Wan, Jahna Otterbacher, Liang Kong, Xiaoming Li, and Yan Zhang. Evolutionary timeline summarization: a balanced optimization framework via iterative substitution. In Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval, pages 745--754. ACM, 2011.

Digital Library

[19]

Shuang-Hong Yang, Alek Kolcz, Andy Schlaikjer, and Pankaj Gupta. Large-scale high-precision topic modeling on twitter. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 1907--1916. ACM, 2014.

Digital Library

[20]

Deyu Zhou, Haiyang Xu, and Yulan He. An unsupervised bayesian modelling approach to storyline detection from news articles. In Proceedings of the 14th Conference on Empirical Methods in Natural Language Processing, pages 1943--1948, 2015.

Cited By

Gui FYang JTang YChen HAn N(2024)Structured Life Narratives: Building Life Story Hierarchies with Graph-Enhanced Event Feature RefinementApplied Sciences10.3390/app1402091814:2(918)Online publication date: 22-Jan-2024
https://doi.org/10.3390/app14020918
Gui FYang JWu QLiu YZhou JAn N(2023)Story Mosaic: Visualizing Elder Life Stories for Caregiver Empowerment (Preprint)JMIR Aging10.2196/50037Online publication date: 18-Jun-2023
https://doi.org/10.2196/50037
Zhao XWang JJin PWang CYang CLi BZhang H(2023)Automatically Generating Storylines from Microblogging PlatformsNeural Information Processing10.1007/978-981-99-1648-1_4(38-50)Online publication date: 15-Apr-2023
https://doi.org/10.1007/978-981-99-1648-1_4
Show More Cited By

Index Terms

Automatical Storyline Generation with Help from Twitter
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
        Topic modeling
    2. Machine learning approaches
      1. Learning in probabilistic graphical models
        Bayesian network models
2. Information systems
  1. Information retrieval
    1. Specialized information retrieval
      1. Environment-specific retrieval
        Web and social media search

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Published In

cover image ACM Conferences

CIKM '16: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management

October 2016

2566 pages

ISBN:9781450340731

DOI:10.1145/2983323

General Chairs:
Snehasis Mukhopadhyay
Indiana University Purdue University Indianapolis, USA
,
ChengXiang Zhai
University of Illinois at Urbana-Champaign, USA
,
Program Chairs:
Elisa Bertino
Purdue University
,
Fabio Crestani
University of Lugano
,
Javed Mostafa
University of North Carolina
,
Jie Tang
Tsinghua University
,
Luo Si
Alibaba Group Inc & Purdue University
,
Xiaofang Zhou
University of Queensland
,
Yi Chang
Yahoo Research
,
Yunyao Li
IBM Research - Almaden
,
Parikshit Sondhi
WalmartLabs

Copyright © 2016 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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Publication History

Published: 24 October 2016

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CIKM'16

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CIKM'16: ACM Conference on Information and Knowledge Management

October 24 - 28, 2016

Indiana, Indianapolis, USA

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CIKM '16 Paper Acceptance Rate 160 of 701 submissions, 23%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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CIKM '25

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The 34th ACM International Conference on Information and Knowledge Management

November 10 - 14, 2025

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Cited By

Gui FYang JTang YChen HAn N(2024)Structured Life Narratives: Building Life Story Hierarchies with Graph-Enhanced Event Feature RefinementApplied Sciences10.3390/app1402091814:2(918)Online publication date: 22-Jan-2024
https://doi.org/10.3390/app14020918
Gui FYang JWu QLiu YZhou JAn N(2023)Story Mosaic: Visualizing Elder Life Stories for Caregiver Empowerment (Preprint)JMIR Aging10.2196/50037Online publication date: 18-Jun-2023
https://doi.org/10.2196/50037
Zhao XWang JJin PWang CYang CLi BZhang H(2023)Automatically Generating Storylines from Microblogging PlatformsNeural Information Processing10.1007/978-981-99-1648-1_4(38-50)Online publication date: 15-Apr-2023
https://doi.org/10.1007/978-981-99-1648-1_4
Wang JZhao XJin PYang CLi BZhang H(2023)Storyline Generation from News Articles Based on Approximate Personalized Propagation of Neural PredictionsDatabase Systems for Advanced Applications10.1007/978-3-031-30678-5_3(37-52)Online publication date: 14-Apr-2023
https://doi.org/10.1007/978-3-031-30678-5_3
Zhao XWang CJin PZhang HYang CLi BShen HZhuang YSmith JYang YCesar PMetze FPrabhakaran B(2021)Post2StoryProceedings of the 29th ACM International Conference on Multimedia10.1145/3474085.3478559(2786-2788)Online publication date: 17-Oct-2021
https://dl.acm.org/doi/10.1145/3474085.3478559
Liu BHan FNiu DKong LLai KXu Y(2020)Story ForestACM Transactions on Knowledge Discovery from Data10.1145/337793914:3(1-28)Online publication date: 13-May-2020
https://dl.acm.org/doi/10.1145/3377939
Poghosyan GIfrim GAtzenbeck CRubart JMillard D(2019)SocialTreeProceedings of the 30th ACM Conference on Hypertext and Social Media10.1145/3342220.3343668(153-162)Online publication date: 12-Sep-2019
https://dl.acm.org/doi/10.1145/3342220.3343668
Zhang RGuo JFan YLan YCheng XPiwowarski BChevalier MGaussier EMaarek YNie JScholer F(2019)Outline GenerationProceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3331184.3331208(745-754)Online publication date: 18-Jul-2019
https://dl.acm.org/doi/10.1145/3331184.3331208
李莹(2018)Analysis of Non-Parametric Event Evolution in Social NetworksComputer Science and Application10.12677/CSA.2018.8610908:06(976-985)Online publication date: 2018
https://doi.org/10.12677/CSA.2018.86109
Liu BNiu DLai KKong LXu YLim EWinslett MSanderson MFu ASun JCulpepper SLo EHo JDonato DAgrawal RZheng YCastillo CSun ATseng VLi C(2017)Growing Story Forest Online from Massive Breaking NewsProceedings of the 2017 ACM on Conference on Information and Knowledge Management10.1145/3132847.3132852(777-785)Online publication date: 6-Nov-2017
https://dl.acm.org/doi/10.1145/3132847.3132852
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