TY - GEN
T1 - Towards Effective, Efficient and Unsupervised Social Event Detection in the Hyperbolic Space
AU - Yu, Xiaoyan
AU - Wei, Yifan
AU - Zhou, Shuaishuai
AU - Yang, Zhiwei
AU - Sun, Li
AU - Peng, Hao
AU - Zhu, Liehuang
AU - Yu, Philip S.
N1 - Publisher Copyright:
Copyright © 2025, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
PY - 2025/4/11
Y1 - 2025/4/11
N2 - The vast, complex, and dynamic nature of social message data has posed challenges to social event detection (SED). Despite considerable effort, these challenges persist, often resulting in inadequately expressive message representations (ineffective) and prolonged learning durations (inefficient). In response to the challenges, this work introduces an unsupervised framework, HyperSED (Hyperbolic SED). Specifically, the proposed framework first models social messages into semantic-based message anchors, and then leverages the structure of the anchor graph and the expressiveness of the hyperbolic space to acquire structure- and geometry-aware anchor representations. Finally, HyperSED builds the partitioning tree of the anchor message graph by incorporating differentiable structural information as the reflection of the detected events. Extensive experiments on public datasets demonstrate HyperSED's competitive performance, along with a substantial improvement in efficiency compared to the current state-of-the-art unsupervised paradigm. Statistically, HyperSED boosts incremental SED by an average of 2%, 2%, and 25% in NMI, AMI, and ARI, respectively; enhancing efficiency by up to 37.41 times and at least 12.10 times, illustrating the advancement of the proposed framework.
AB - The vast, complex, and dynamic nature of social message data has posed challenges to social event detection (SED). Despite considerable effort, these challenges persist, often resulting in inadequately expressive message representations (ineffective) and prolonged learning durations (inefficient). In response to the challenges, this work introduces an unsupervised framework, HyperSED (Hyperbolic SED). Specifically, the proposed framework first models social messages into semantic-based message anchors, and then leverages the structure of the anchor graph and the expressiveness of the hyperbolic space to acquire structure- and geometry-aware anchor representations. Finally, HyperSED builds the partitioning tree of the anchor message graph by incorporating differentiable structural information as the reflection of the detected events. Extensive experiments on public datasets demonstrate HyperSED's competitive performance, along with a substantial improvement in efficiency compared to the current state-of-the-art unsupervised paradigm. Statistically, HyperSED boosts incremental SED by an average of 2%, 2%, and 25% in NMI, AMI, and ARI, respectively; enhancing efficiency by up to 37.41 times and at least 12.10 times, illustrating the advancement of the proposed framework.
UR - http://www.scopus.com/inward/record.url?scp=105003907290&partnerID=8YFLogxK
U2 - 10.1609/aaai.v39i12.33430
DO - 10.1609/aaai.v39i12.33430
M3 - Conference contribution
AN - SCOPUS:105003907290
T3 - Proceedings of the AAAI Conference on Artificial Intelligence
SP - 13106
EP - 13114
BT - Special Track on AI Alignment
A2 - Walsh, Toby
A2 - Shah, Julie
A2 - Kolter, Zico
PB - Association for the Advancement of Artificial Intelligence
T2 - 39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025
Y2 - 25 February 2025 through 4 March 2025
ER -