TY - GEN
T1 - Hashtag-based sub-event discovery using mutually generative lda in twitter
AU - Xing, Chen
AU - Wang, Yuan
AU - Liu, Jie
AU - Huang, Yalou
AU - Ma, Wei-Ying
N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].
PY - 2016
Y1 - 2016
N2 - Sub-event discovery is an effective method for social event analysis in Twitter. It can discover sub-events from large amount of noisy event-related information in Twitter and semantically represent them. The task is challenging because tweets are short, informal and noisy. To solve this problem, we consider leveraging event-related hashtags that contain many locations, dates and concise sub-event related descriptions to enhance sub-event discovery. To this end, we propose a hashtag-based mutually generative Latent Dirichlet Allocation model(MGe-LDA). In MGe-LDA, hashtags and topics of a tweet are mutually generated by each other. The mutually generative process models the relationship between hashtags and topics of tweets, and highlights the role of hashtags as a semantic representation of the corresponding tweets. Experimental results show that MGe-LDA can significantly outperform state-of-the-art methods for sub-event discovery. © 2016, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
AB - Sub-event discovery is an effective method for social event analysis in Twitter. It can discover sub-events from large amount of noisy event-related information in Twitter and semantically represent them. The task is challenging because tweets are short, informal and noisy. To solve this problem, we consider leveraging event-related hashtags that contain many locations, dates and concise sub-event related descriptions to enhance sub-event discovery. To this end, we propose a hashtag-based mutually generative Latent Dirichlet Allocation model(MGe-LDA). In MGe-LDA, hashtags and topics of a tweet are mutually generated by each other. The mutually generative process models the relationship between hashtags and topics of tweets, and highlights the role of hashtags as a semantic representation of the corresponding tweets. Experimental results show that MGe-LDA can significantly outperform state-of-the-art methods for sub-event discovery. © 2016, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
UR - http://www.scopus.com/inward/record.url?scp=85007233463&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85007233463&origin=recordpage
U2 - 10.1609/aaai.v30i1.10326
DO - 10.1609/aaai.v30i1.10326
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 9781577357605
T3 - 30th AAAI Conference on Artificial Intelligence, AAAI 2016
SP - 2666
EP - 2672
BT - 30th AAAI Conference on Artificial Intelligence, AAAI 2016
PB - AAAI Press
T2 - 30th AAAI Conference on Artificial Intelligence, AAAI 2016
Y2 - 12 February 2016 through 17 February 2016
ER -