Detecting rumor patterns in streaming social media

Shihan Wang; Takao Terano

doi:10.1109/BigData.2015.7364071

Detecting rumor patterns in streaming social media

Shihan Wang, Takao Terano

Tokyo Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

Abstract

Rumor detection in streaming social media is a significant but challenging problem. In this paper, we present a method to identify rumor patterns in the streaming social media environment. Patterns which combine both structural and behavioral properties of rumor are firstly proposed to distinguish false rumors from valid news. A novel graph-based pattern matching algorithm is also described to detect rumor patterns from streaming social media data. Compared within Twitter data of rumors and non-rumors, our selected rumor patterns contain distinct properties of rumors in short-term series.

Original language	English
Title of host publication	Proceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015
Editors	Feng Luo, Kemafor Ogan, Mohammed J. Zaki, Laura Haas, Beng Chin Ooi, Vipin Kumar, Sudarsan Rachuri, Saumyadipta Pyne, Howard Ho, Xiaohua Hu, Shipeng Yu, Morris Hui-I Hsiao, Jian Li
Publisher	IEEE
Pages	2709-2715
Number of pages	7
ISBN (Electronic)	9781479999255
DOIs	https://doi.org/10.1109/BigData.2015.7364071
Publication status	Published - 22 Dec 2015
Event	3rd IEEE International Conference on Big Data, IEEE Big Data 2015 - Santa Clara, United States Duration: 29 Oct 2015 → 1 Nov 2015

Conference

Conference	3rd IEEE International Conference on Big Data, IEEE Big Data 2015
Country/Territory	United States
City	Santa Clara
Period	29/10/15 → 1/11/15

Keywords

rumor detection
social media
socioeconomic sustainability
streaming pattern matching

Access to Document

10.1109/BigData.2015.7364071

Cite this

Wang, S., & Terano, T. (2015). Detecting rumor patterns in streaming social media. In F. Luo, K. Ogan, M. J. Zaki, L. Haas, B. C. Ooi, V. Kumar, S. Rachuri, S. Pyne, H. Ho, X. Hu, S. Yu, M. H.-I. Hsiao, & J. Li (Eds.), Proceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015 (pp. 2709-2715). Article 7364071 IEEE. https://doi.org/10.1109/BigData.2015.7364071

Wang, Shihan ; Terano, Takao. / Detecting rumor patterns in streaming social media. Proceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015. editor / Feng Luo ; Kemafor Ogan ; Mohammed J. Zaki ; Laura Haas ; Beng Chin Ooi ; Vipin Kumar ; Sudarsan Rachuri ; Saumyadipta Pyne ; Howard Ho ; Xiaohua Hu ; Shipeng Yu ; Morris Hui-I Hsiao ; Jian Li. IEEE, 2015. pp. 2709-2715

@inproceedings{eadc79b5fc2b4f1bbd96d37742e9e604,

title = "Detecting rumor patterns in streaming social media",

abstract = "Rumor detection in streaming social media is a significant but challenging problem. In this paper, we present a method to identify rumor patterns in the streaming social media environment. Patterns which combine both structural and behavioral properties of rumor are firstly proposed to distinguish false rumors from valid news. A novel graph-based pattern matching algorithm is also described to detect rumor patterns from streaming social media data. Compared within Twitter data of rumors and non-rumors, our selected rumor patterns contain distinct properties of rumors in short-term series.",

keywords = "rumor detection, social media, socioeconomic sustainability, streaming pattern matching",

author = "Shihan Wang and Takao Terano",

year = "2015",

month = dec,

day = "22",

doi = "10.1109/BigData.2015.7364071",

language = "English",

pages = "2709--2715",

editor = "Feng Luo and Kemafor Ogan and Zaki, {Mohammed J.} and Laura Haas and Ooi, {Beng Chin} and Vipin Kumar and Sudarsan Rachuri and Saumyadipta Pyne and Howard Ho and Xiaohua Hu and Shipeng Yu and Hsiao, {Morris Hui-I} and Jian Li",

booktitle = "Proceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015",

publisher = "IEEE",

address = "United States",

note = "3rd IEEE International Conference on Big Data, IEEE Big Data 2015 ; Conference date: 29-10-2015 Through 01-11-2015",

}

Wang, S & Terano, T 2015, Detecting rumor patterns in streaming social media. in F Luo, K Ogan, MJ Zaki, L Haas, BC Ooi, V Kumar, S Rachuri, S Pyne, H Ho, X Hu, S Yu, MH-I Hsiao & J Li (eds), Proceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015., 7364071, IEEE, pp. 2709-2715, 3rd IEEE International Conference on Big Data, IEEE Big Data 2015, Santa Clara, United States, 29/10/15. https://doi.org/10.1109/BigData.2015.7364071

Detecting rumor patterns in streaming social media. / Wang, Shihan; Terano, Takao.
Proceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015. ed. / Feng Luo; Kemafor Ogan; Mohammed J. Zaki; Laura Haas; Beng Chin Ooi; Vipin Kumar; Sudarsan Rachuri; Saumyadipta Pyne; Howard Ho; Xiaohua Hu; Shipeng Yu; Morris Hui-I Hsiao; Jian Li. IEEE, 2015. p. 2709-2715 7364071.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Detecting rumor patterns in streaming social media

AU - Wang, Shihan

AU - Terano, Takao

PY - 2015/12/22

Y1 - 2015/12/22

N2 - Rumor detection in streaming social media is a significant but challenging problem. In this paper, we present a method to identify rumor patterns in the streaming social media environment. Patterns which combine both structural and behavioral properties of rumor are firstly proposed to distinguish false rumors from valid news. A novel graph-based pattern matching algorithm is also described to detect rumor patterns from streaming social media data. Compared within Twitter data of rumors and non-rumors, our selected rumor patterns contain distinct properties of rumors in short-term series.

AB - Rumor detection in streaming social media is a significant but challenging problem. In this paper, we present a method to identify rumor patterns in the streaming social media environment. Patterns which combine both structural and behavioral properties of rumor are firstly proposed to distinguish false rumors from valid news. A novel graph-based pattern matching algorithm is also described to detect rumor patterns from streaming social media data. Compared within Twitter data of rumors and non-rumors, our selected rumor patterns contain distinct properties of rumors in short-term series.

KW - rumor detection

KW - social media

KW - socioeconomic sustainability

KW - streaming pattern matching

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U2 - 10.1109/BigData.2015.7364071

DO - 10.1109/BigData.2015.7364071

M3 - Conference contribution

AN - SCOPUS:84963761842

SP - 2709

EP - 2715

BT - Proceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015

A2 - Luo, Feng

A2 - Ogan, Kemafor

A2 - Zaki, Mohammed J.

A2 - Haas, Laura

A2 - Ooi, Beng Chin

A2 - Kumar, Vipin

A2 - Rachuri, Sudarsan

A2 - Pyne, Saumyadipta

A2 - Ho, Howard

A2 - Hu, Xiaohua

A2 - Yu, Shipeng

A2 - Hsiao, Morris Hui-I

A2 - Li, Jian

PB - IEEE

T2 - 3rd IEEE International Conference on Big Data, IEEE Big Data 2015

Y2 - 29 October 2015 through 1 November 2015

ER -

Detecting rumor patterns in streaming social media

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