Event grounding from multimodal social network fusion

Hyunsouk Cho; Jinyoung Yeo; Seung Won Hwang

doi:10.1109/ICDM.2016.28

Event grounding from multimodal social network fusion

Hyunsouk Cho, Jinyoung Yeo, Seung Won Hwang

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Citations (Scopus)

Abstract

This paper studies the problem of extracting realworld event information from social media streams. Although existing work focuses on event signals of bursty mentions extracted from a single-source of textual streams, these signals are likely to be noisy due to ambiguous occurrences of individual mentions. To extract accurate event signals, we propose a framework capable of "grounding" mentions to unique event using multiple social networks with complementary strength. We show that our framework jointly using multiple sources outperforms state-ofthe-Arts using publicly available datasets.

Original language	English
Title of host publication	Proceedings - 16th IEEE International Conference on Data Mining, ICDM 2016
Editors	Francesco Bonchi, Xindong Wu, Ricardo Baeza-Yates, Josep Domingo-Ferrer, Zhi-Hua Zhou
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	835-840
Number of pages	6
ISBN (Electronic)	9781509054725
DOIs	https://doi.org/10.1109/ICDM.2016.28
Publication status	Published - 2017 Jan 31
Event	16th IEEE International Conference on Data Mining, ICDM 2016 - Barcelona, Catalonia, Spain Duration: 2016 Dec 12 → 2016 Dec 15

Publication series

Name	Proceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)	1550-4786

Other

Other	16th IEEE International Conference on Data Mining, ICDM 2016
Country/Territory	Spain
City	Barcelona, Catalonia
Period	16/12/12 → 16/12/15

Bibliographical note

Funding Information:
This work was partly supported by Institute for Information & communications Technology Promotion(IITP) grant funded by the Korea government(MSIP) (No.B0101-16-0307, Basic Software Research in Human-level Lifelong Machine Learning (Machine Learning Center)) and this research was supported by the MSIP(Ministry of Science, ICT and Future Planning), Korea, under the ITRC(Information Technology Research Center) support program (IITP-2016-R2720-16-0007) supervised by the IITP(Institute for Information & communications Technology Promotion)

Publisher Copyright:
© 2016 IEEE.

All Science Journal Classification (ASJC) codes

Engineering(all)

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10.1109/ICDM.2016.28

Cite this

Cho, H., Yeo, J., & Hwang, S. W. (2017). Event grounding from multimodal social network fusion. In F. Bonchi, X. Wu, R. Baeza-Yates, J. Domingo-Ferrer, & Z-H. Zhou (Eds.), Proceedings - 16th IEEE International Conference on Data Mining, ICDM 2016 (pp. 835-840). [7837912] (Proceedings - IEEE International Conference on Data Mining, ICDM). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICDM.2016.28

Cho, Hyunsouk ; Yeo, Jinyoung ; Hwang, Seung Won. / Event grounding from multimodal social network fusion. Proceedings - 16th IEEE International Conference on Data Mining, ICDM 2016. editor / Francesco Bonchi ; Xindong Wu ; Ricardo Baeza-Yates ; Josep Domingo-Ferrer ; Zhi-Hua Zhou. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 835-840 (Proceedings - IEEE International Conference on Data Mining, ICDM).

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abstract = "This paper studies the problem of extracting realworld event information from social media streams. Although existing work focuses on event signals of bursty mentions extracted from a single-source of textual streams, these signals are likely to be noisy due to ambiguous occurrences of individual mentions. To extract accurate event signals, we propose a framework capable of {"}grounding{"} mentions to unique event using multiple social networks with complementary strength. We show that our framework jointly using multiple sources outperforms state-ofthe-Arts using publicly available datasets.",

author = "Hyunsouk Cho and Jinyoung Yeo and Hwang, {Seung Won}",

note = "Funding Information: This work was partly supported by Institute for Information & communications Technology Promotion(IITP) grant funded by the Korea government(MSIP) (No.B0101-16-0307, Basic Software Research in Human-level Lifelong Machine Learning (Machine Learning Center)) and this research was supported by the MSIP(Ministry of Science, ICT and Future Planning), Korea, under the ITRC(Information Technology Research Center) support program (IITP-2016-R2720-16-0007) supervised by the IITP(Institute for Information & communications Technology Promotion) Publisher Copyright: {\textcopyright} 2016 IEEE.; 16th IEEE International Conference on Data Mining, ICDM 2016 ; Conference date: 12-12-2016 Through 15-12-2016",

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Cho, H, Yeo, J & Hwang, SW 2017, Event grounding from multimodal social network fusion. in F Bonchi, X Wu, R Baeza-Yates, J Domingo-Ferrer & Z-H Zhou (eds), Proceedings - 16th IEEE International Conference on Data Mining, ICDM 2016., 7837912, Proceedings - IEEE International Conference on Data Mining, ICDM, Institute of Electrical and Electronics Engineers Inc., pp. 835-840, 16th IEEE International Conference on Data Mining, ICDM 2016, Barcelona, Catalonia, Spain, 16/12/12. https://doi.org/10.1109/ICDM.2016.28

Event grounding from multimodal social network fusion. / Cho, Hyunsouk; Yeo, Jinyoung ; Hwang, Seung Won.

Proceedings - 16th IEEE International Conference on Data Mining, ICDM 2016. ed. / Francesco Bonchi; Xindong Wu; Ricardo Baeza-Yates; Josep Domingo-Ferrer; Zhi-Hua Zhou. Institute of Electrical and Electronics Engineers Inc., 2017. p. 835-840 7837912 (Proceedings - IEEE International Conference on Data Mining, ICDM).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Cho, Hyunsouk

AU - Yeo, Jinyoung

AU - Hwang, Seung Won

N1 - Funding Information: This work was partly supported by Institute for Information & communications Technology Promotion(IITP) grant funded by the Korea government(MSIP) (No.B0101-16-0307, Basic Software Research in Human-level Lifelong Machine Learning (Machine Learning Center)) and this research was supported by the MSIP(Ministry of Science, ICT and Future Planning), Korea, under the ITRC(Information Technology Research Center) support program (IITP-2016-R2720-16-0007) supervised by the IITP(Institute for Information & communications Technology Promotion) Publisher Copyright: © 2016 IEEE.

PY - 2017/1/31

Y1 - 2017/1/31

N2 - This paper studies the problem of extracting realworld event information from social media streams. Although existing work focuses on event signals of bursty mentions extracted from a single-source of textual streams, these signals are likely to be noisy due to ambiguous occurrences of individual mentions. To extract accurate event signals, we propose a framework capable of "grounding" mentions to unique event using multiple social networks with complementary strength. We show that our framework jointly using multiple sources outperforms state-ofthe-Arts using publicly available datasets.

AB - This paper studies the problem of extracting realworld event information from social media streams. Although existing work focuses on event signals of bursty mentions extracted from a single-source of textual streams, these signals are likely to be noisy due to ambiguous occurrences of individual mentions. To extract accurate event signals, we propose a framework capable of "grounding" mentions to unique event using multiple social networks with complementary strength. We show that our framework jointly using multiple sources outperforms state-ofthe-Arts using publicly available datasets.

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M3 - Conference contribution

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T3 - Proceedings - IEEE International Conference on Data Mining, ICDM

SP - 835

EP - 840

BT - Proceedings - 16th IEEE International Conference on Data Mining, ICDM 2016

A2 - Bonchi, Francesco

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PB - Institute of Electrical and Electronics Engineers Inc.

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Y2 - 12 December 2016 through 15 December 2016

ER -

Cho H, Yeo J , Hwang SW. Event grounding from multimodal social network fusion. In Bonchi F, Wu X, Baeza-Yates R, Domingo-Ferrer J, Zhou Z-H, editors, Proceedings - 16th IEEE International Conference on Data Mining, ICDM 2016. Institute of Electrical and Electronics Engineers Inc. 2017. p. 835-840. 7837912. (Proceedings - IEEE International Conference on Data Mining, ICDM). doi: 10.1109/ICDM.2016.28

Event grounding from multimodal social network fusion

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