Emerging Topic Detection from Microblog Streams Based on Emerging Pattern Mining
Paper
Paper/Presentation Title | Emerging Topic Detection from Microblog Streams Based on Emerging Pattern Mining |
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Presentation Type | Paper |
Authors | Peng, Min (Author), Ouyang, Shuang (Author), Zhu, Jiahui (Author), Huang, Jiajia (Author), Wang, Hua (Author) and Yong, Jianming (Author) |
Journal or Proceedings Title | Proceedings of the 2018 IEEE 22nd International Conference on Computer Supported Cooperative Work in Design |
ERA Conference ID | 43280 |
Number of Pages | 6 |
Year | 2018 |
Place of Publication | United States |
ISBN | 9781538614822 |
Digital Object Identifier (DOI) | https://doi.org/10.1109/CSCWD.2018.8465166 |
Web Address (URL) of Paper | https://ieeexplore.ieee.org/document/8465166 |
Conference/Event | 2018 IEEE 22nd International Conference on Computer Supported Cooperative Work in Design |
International Conference on Computer Supported Cooperative Work in Design | |
Event Details | International Conference on Computer Supported Cooperative Work in Design CSCWD Rank B B B B B B B B B B |
Event Details | 2018 IEEE 22nd International Conference on Computer Supported Cooperative Work in Design Event Date 09 to end of 11 May 2018 Event Location Nanjing, China |
Abstract | Emerging topic detection from microblogs has developed into an attractive task because events usually break on social channels. However, due to the features of high noise, short length, fast arriving rate and irregular writing style of microblogs, it has been proven to be a challenge to detect emerging topics from microblog streams early and accurately in a scalable way. Several approaches have been proposed to tackle this problem and have achieved sound performance in some aspects. However, from the point of novelty and scalability, there is still considerable space for improvement. Inspired by the consideration, we propose an emerging topic detection framework based on emerging pattern mining. Via encoding the term novelty into an efficient high utility itemset mining (HUIM) algorithm, a group of emerging patterns which are concise and interpretive representations of topics can be first detected, decreasing the computational cost of the clustering part. |
Keywords | Emerging Topic Detection, High Utility Itemset Mining, Emerging Pattern Clustering |
ANZSRC Field of Research 2020 | 460999. Information systems not elsewhere classified |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | Wuhan University, China |
Nanjing Audit University, China | |
Victoria University | |
School of Management and Enterprise | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q50w7/emerging-topic-detection-from-microblog-streams-based-on-emerging-pattern-mining
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