Identifying Event-related Bursts via Social Media Activities

Activities on social media increase at a dramatic rate. When an external event happens, there is a surge in the degree of activities related to the event. These activities may be temporally correlated with one another, but they may also capture different aspects of an event and therefore exhibit dif...

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Main Authors: ZHAO, Xin, Shu, Baihan, JIANG, Jing, SONG, YANG, YAN, Hongfei, LI, Xiaoming
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Language:English
Published: Institutional Knowledge at Singapore Management University 2012
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Online Access:https://ink.library.smu.edu.sg/sis_research/1621
https://ink.library.smu.edu.sg/context/sis_research/article/2620/viewcontent/jingjiang2.pdf
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spelling sg-smu-ink.sis_research-26202018-07-13T03:09:54Z Identifying Event-related Bursts via Social Media Activities ZHAO, Xin Shu, Baihan JIANG, Jing SONG, YANG YAN, Hongfei LI, Xiaoming Activities on social media increase at a dramatic rate. When an external event happens, there is a surge in the degree of activities related to the event. These activities may be temporally correlated with one another, but they may also capture different aspects of an event and therefore exhibit different bursty patterns. In this paper, we propose to identify event-related bursts via social media activities. We study how to correlate multiple types of activities to derive a global bursty pattern. To model smoothness of one state sequence, we propose a novel function which can capture the state context. The experiments on a large Twitter dataset shows our methods are very effective. 2012-07-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/1621 https://ink.library.smu.edu.sg/context/sis_research/article/2620/viewcontent/jingjiang2.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Databases and Information Systems Numerical Analysis and Scientific Computing
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Databases and Information Systems
Numerical Analysis and Scientific Computing
spellingShingle Databases and Information Systems
Numerical Analysis and Scientific Computing
ZHAO, Xin
Shu, Baihan
JIANG, Jing
SONG, YANG
YAN, Hongfei
LI, Xiaoming
Identifying Event-related Bursts via Social Media Activities
description Activities on social media increase at a dramatic rate. When an external event happens, there is a surge in the degree of activities related to the event. These activities may be temporally correlated with one another, but they may also capture different aspects of an event and therefore exhibit different bursty patterns. In this paper, we propose to identify event-related bursts via social media activities. We study how to correlate multiple types of activities to derive a global bursty pattern. To model smoothness of one state sequence, we propose a novel function which can capture the state context. The experiments on a large Twitter dataset shows our methods are very effective.
format text
author ZHAO, Xin
Shu, Baihan
JIANG, Jing
SONG, YANG
YAN, Hongfei
LI, Xiaoming
author_facet ZHAO, Xin
Shu, Baihan
JIANG, Jing
SONG, YANG
YAN, Hongfei
LI, Xiaoming
author_sort ZHAO, Xin
title Identifying Event-related Bursts via Social Media Activities
title_short Identifying Event-related Bursts via Social Media Activities
title_full Identifying Event-related Bursts via Social Media Activities
title_fullStr Identifying Event-related Bursts via Social Media Activities
title_full_unstemmed Identifying Event-related Bursts via Social Media Activities
title_sort identifying event-related bursts via social media activities
publisher Institutional Knowledge at Singapore Management University
publishDate 2012
url https://ink.library.smu.edu.sg/sis_research/1621
https://ink.library.smu.edu.sg/context/sis_research/article/2620/viewcontent/jingjiang2.pdf
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