Peng, H, Li, J, Song, Y et al. (4 more authors) (2021) Streaming Social Event Detection and Evolution Discovery in Heterogeneous Information Networks. ACM Transactions on Knowledge Discovery from Data (TKDD), 15 (5). pp. 1-18. ISSN 1556-4681
Abstract
Events are happening in real world and real time, which can be planned and organized for occasions, such as social gatherings, festival celebrations, influential meetings, or sports activities. Social media platforms generate a lot of real-time text information regarding public events with different topics. However, mining social events is challenging because events typically exhibit heterogeneous texture and metadata are often ambiguous. In this article, we first design a novel event-based meta-schema to characterize the semantic relatedness of social events and then build an event-based heterogeneous information network (HIN) integrating information from external knowledge base. Second, we propose a novel Pairwise Popularity Graph Convolutional Network, named as PP-GCN, based on weighted meta-path instance similarity and textual semantic representation as inputs, to perform fine-grained social event categorization and learn the optimal weights of meta-paths in different tasks. Third, we propose a streaming social event detection and evolution discovery framework for HINs based on meta-path similarity search, historical information about meta-paths, and heterogeneous DBSCAN clustering method. Comprehensive experiments on real-world streaming social text data are conducted to compare various social event detection and evolution discovery algorithms. Experimental results demonstrate that our proposed framework outperforms other alternative social event detection and evolution discovery techniques.
Metadata
Item Type: | Article |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © 2021 ACM. This is an author produced version of an article published in ACM Transactions on Knowledge Discovery from Data. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | event evolution; Social event detection; streaming data; fine-grained categorization; graph convolutional network; pairwise learning; heterogeneous information network; DBSCAN |
Dates: |
|
Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 25 Jan 2021 15:29 |
Last Modified: | 22 May 2021 21:59 |
Status: | Published |
Publisher: | Association for Computing Machinery (ACM) |
Identification Number: | 10.1145/3461317 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:170202 |