Robust Social Event Detection via Deep Clustering

Jiaofu Zhang; Lianzhong Liu; Zihang Huang; Lihua Han; Shuhai Wang; Tongge Xu; Jingyi Zhang; Yangyang Li; Yifeng Liu; Md Zakirul Alam Bhuiyan

doi:10.1109/ISPA-BDCloud-SocialCom-SustainCom52081.2021.00116

Robust Social Event Detection via Deep Clustering

Jiaofu Zhang
, Lianzhong Liu
, Zihang Huang
, Lihua Han
, Shuhai Wang
, Tongge Xu
, Jingyi Zhang
, Yangyang Li
, Yifeng Liu
, Md Zakirul Alam Bhuiyan

School of Cyber Science and Technology

Beihang University
Shijiazhuang Tiedao University
China Academy of Electronics and Information Technology
Fordham University

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

3 Scopus citations

Abstract

Social networks are quickly becoming the primary medium for discussing what is happening around real-world events. However, it is still a challenge to detect events on social media due to its real-time nature, scale and amount of unstructured data generated. In this paper, we present a novel real-time system for detecting surrounding real-world events. Our proposed framework consists of four main components, including text filtering, text representation, deep clustering, and event merging. After filtering non-event messages, we use entities and words to represent messages. Based on text representation, we propose a novel density clustering algorithm for online event detection. The resulted sub-events are further merged based on time information and keyword similarity. Experiments on standard and real-world datasets demonstrated the effectiveness of our proposed method.

Original language	English
Title of host publication	19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	814-819
Number of pages	6
ISBN (Electronic)	9781665435741
DOIs	https://doi.org/10.1109/ISPA-BDCloud-SocialCom-SustainCom52081.2021.00116
State	Published - 2021
Event	19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021 - New York, United States Duration: 30 Sep 2021 → 3 Oct 2021

Publication series

Name	19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021

Conference

Conference	19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021
Country/Territory	United States
City	New York
Period	30/09/21 → 3/10/21

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Cluster analysis
Event detection
Social media
Temporal information

Access to Document

10.1109/ISPA-BDCloud-SocialCom-SustainCom52081.2021.00116

Cite this

Zhang, J., Liu, L., Huang, Z., Han, L., Wang, S., Xu, T., Zhang, J., Li, Y., Liu, Y., & Zakirul Alam Bhuiyan, M. (2021). Robust Social Event Detection via Deep Clustering. In 19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021 (pp. 814-819). (19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISPA-BDCloud-SocialCom-SustainCom52081.2021.00116

Zhang, Jiaofu ; Liu, Lianzhong ; Huang, Zihang et al. / Robust Social Event Detection via Deep Clustering. 19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021. Institute of Electrical and Electronics Engineers Inc., 2021. pp. 814-819 (19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021).

@inproceedings{8c9f9837830147d7b6401b77070e10a9,

title = "Robust Social Event Detection via Deep Clustering",

abstract = "Social networks are quickly becoming the primary medium for discussing what is happening around real-world events. However, it is still a challenge to detect events on social media due to its real-time nature, scale and amount of unstructured data generated. In this paper, we present a novel real-time system for detecting surrounding real-world events. Our proposed framework consists of four main components, including text filtering, text representation, deep clustering, and event merging. After filtering non-event messages, we use entities and words to represent messages. Based on text representation, we propose a novel density clustering algorithm for online event detection. The resulted sub-events are further merged based on time information and keyword similarity. Experiments on standard and real-world datasets demonstrated the effectiveness of our proposed method.",

keywords = "Cluster analysis, Event detection, Social media, Temporal information",

author = "Jiaofu Zhang and Lianzhong Liu and Zihang Huang and Lihua Han and Shuhai Wang and Tongge Xu and Jingyi Zhang and Yangyang Li and Yifeng Liu and \{Zakirul Alam Bhuiyan\}, Md",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021 ; Conference date: 30-09-2021 Through 03-10-2021",

year = "2021",

doi = "10.1109/ISPA-BDCloud-SocialCom-SustainCom52081.2021.00116",

language = "英语",

series = "19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "814--819",

booktitle = "19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021",

address = "美国",

}

Zhang, J, Liu, L, Huang, Z, Han, L, Wang, S, Xu, T, Zhang, J, Li, Y, Liu, Y & Zakirul Alam Bhuiyan, M 2021, Robust Social Event Detection via Deep Clustering. in 19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021. 19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021, Institute of Electrical and Electronics Engineers Inc., pp. 814-819, 19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021, New York, United States, 30/09/21. https://doi.org/10.1109/ISPA-BDCloud-SocialCom-SustainCom52081.2021.00116

Robust Social Event Detection via Deep Clustering. / Zhang, Jiaofu; Liu, Lianzhong; Huang, Zihang et al.
19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021. Institute of Electrical and Electronics Engineers Inc., 2021. p. 814-819 (19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021).

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

TY - GEN

T1 - Robust Social Event Detection via Deep Clustering

AU - Zhang, Jiaofu

AU - Liu, Lianzhong

AU - Huang, Zihang

AU - Han, Lihua

AU - Wang, Shuhai

AU - Xu, Tongge

AU - Zhang, Jingyi

AU - Li, Yangyang

AU - Liu, Yifeng

AU - Zakirul Alam Bhuiyan, Md

PY - 2021

Y1 - 2021

N2 - Social networks are quickly becoming the primary medium for discussing what is happening around real-world events. However, it is still a challenge to detect events on social media due to its real-time nature, scale and amount of unstructured data generated. In this paper, we present a novel real-time system for detecting surrounding real-world events. Our proposed framework consists of four main components, including text filtering, text representation, deep clustering, and event merging. After filtering non-event messages, we use entities and words to represent messages. Based on text representation, we propose a novel density clustering algorithm for online event detection. The resulted sub-events are further merged based on time information and keyword similarity. Experiments on standard and real-world datasets demonstrated the effectiveness of our proposed method.

AB - Social networks are quickly becoming the primary medium for discussing what is happening around real-world events. However, it is still a challenge to detect events on social media due to its real-time nature, scale and amount of unstructured data generated. In this paper, we present a novel real-time system for detecting surrounding real-world events. Our proposed framework consists of four main components, including text filtering, text representation, deep clustering, and event merging. After filtering non-event messages, we use entities and words to represent messages. Based on text representation, we propose a novel density clustering algorithm for online event detection. The resulted sub-events are further merged based on time information and keyword similarity. Experiments on standard and real-world datasets demonstrated the effectiveness of our proposed method.

KW - Cluster analysis

KW - Event detection

KW - Social media

KW - Temporal information

UR - https://www.scopus.com/pages/publications/85124161776

U2 - 10.1109/ISPA-BDCloud-SocialCom-SustainCom52081.2021.00116

DO - 10.1109/ISPA-BDCloud-SocialCom-SustainCom52081.2021.00116

M3 - 会议稿件

AN - SCOPUS:85124161776

T3 - 19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021

SP - 814

EP - 819

BT - 19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021

Y2 - 30 September 2021 through 3 October 2021

ER -

Zhang J, Liu L, Huang Z, Han L, Wang S, Xu T et al. Robust Social Event Detection via Deep Clustering. In 19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021. Institute of Electrical and Electronics Engineers Inc. 2021. p. 814-819. (19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021). doi: 10.1109/ISPA-BDCloud-SocialCom-SustainCom52081.2021.00116

Robust Social Event Detection via Deep Clustering

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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Cite this