Recurrent Chinese Restaurant Process with a Duration-based Discount for Event Identification from Twitter

Due to the fast development of social media on the Web, Twitter has become one of the major platforms for people to express themselves. Because of the wide adoption of Twitter, events like breaking news and release of popular videos can easily catch people’s attention and spread rapidly on Twitter,...

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Main Authors: DIAO, Qiming, JIANG, Jing
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2014
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Online Access:https://ink.library.smu.edu.sg/sis_research/2412
https://ink.library.smu.edu.sg/context/sis_research/article/3412/viewcontent/RecurrentChineseRestaurantProcessDuration_basedDiscountTwitter.pdf
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spelling sg-smu-ink.sis_research-34122018-07-13T04:05:26Z Recurrent Chinese Restaurant Process with a Duration-based Discount for Event Identification from Twitter DIAO, Qiming JIANG, Jing Due to the fast development of social media on the Web, Twitter has become one of the major platforms for people to express themselves. Because of the wide adoption of Twitter, events like breaking news and release of popular videos can easily catch people’s attention and spread rapidly on Twitter, and the number of relevant tweets approximately reflects the impact of an event. Event identification and analysis on Twitter has thus become an important task. Recently the Recurrent Chinese Restaurant Process (RCRP) has been successfully used for event identification from news streams and news-centric social media streams. However, these models cannot be directly applied to Twitter based on our preliminary experiments mainly for two reasons: (1) Events emerge and die out fast on Twitter, while existing models ignore this burstiness property. (2) Most Twitter posts are personal interest oriented while only a small fraction is event related. Motivated by these challenges, we propose a new nonparametric model which considers burstiness. We further combine this model with traditional topic models to identify both events and topics simultaneously. Our quantitative evaluation provides sufficient evidence that our model can accurately detect meaningful events. Our qualitative evaluation also shows interesting analysis for events on Twitter. 2014-04-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/2412 info:doi/10.1137/1.9781611973440.45 https://ink.library.smu.edu.sg/context/sis_research/article/3412/viewcontent/RecurrentChineseRestaurantProcessDuration_basedDiscountTwitter.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 Computer Sciences Databases and Information Systems Social Media
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Computer Sciences
Databases and Information Systems
Social Media
spellingShingle Computer Sciences
Databases and Information Systems
Social Media
DIAO, Qiming
JIANG, Jing
Recurrent Chinese Restaurant Process with a Duration-based Discount for Event Identification from Twitter
description Due to the fast development of social media on the Web, Twitter has become one of the major platforms for people to express themselves. Because of the wide adoption of Twitter, events like breaking news and release of popular videos can easily catch people’s attention and spread rapidly on Twitter, and the number of relevant tweets approximately reflects the impact of an event. Event identification and analysis on Twitter has thus become an important task. Recently the Recurrent Chinese Restaurant Process (RCRP) has been successfully used for event identification from news streams and news-centric social media streams. However, these models cannot be directly applied to Twitter based on our preliminary experiments mainly for two reasons: (1) Events emerge and die out fast on Twitter, while existing models ignore this burstiness property. (2) Most Twitter posts are personal interest oriented while only a small fraction is event related. Motivated by these challenges, we propose a new nonparametric model which considers burstiness. We further combine this model with traditional topic models to identify both events and topics simultaneously. Our quantitative evaluation provides sufficient evidence that our model can accurately detect meaningful events. Our qualitative evaluation also shows interesting analysis for events on Twitter.
format text
author DIAO, Qiming
JIANG, Jing
author_facet DIAO, Qiming
JIANG, Jing
author_sort DIAO, Qiming
title Recurrent Chinese Restaurant Process with a Duration-based Discount for Event Identification from Twitter
title_short Recurrent Chinese Restaurant Process with a Duration-based Discount for Event Identification from Twitter
title_full Recurrent Chinese Restaurant Process with a Duration-based Discount for Event Identification from Twitter
title_fullStr Recurrent Chinese Restaurant Process with a Duration-based Discount for Event Identification from Twitter
title_full_unstemmed Recurrent Chinese Restaurant Process with a Duration-based Discount for Event Identification from Twitter
title_sort recurrent chinese restaurant process with a duration-based discount for event identification from twitter
publisher Institutional Knowledge at Singapore Management University
publishDate 2014
url https://ink.library.smu.edu.sg/sis_research/2412
https://ink.library.smu.edu.sg/context/sis_research/article/3412/viewcontent/RecurrentChineseRestaurantProcessDuration_basedDiscountTwitter.pdf
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