Towards Effective, Efficient and Unsupervised Social Event Detection in the Hyperbolic Space
This work addresses inefficiencies and ineffectiveness in social event detection for applications like social media analysis, offering incremental improvements in performance and speed.
The paper tackles social event detection by proposing HyperSED, an unsupervised framework that uses hyperbolic space to improve representation and efficiency, achieving average gains of 2% in NMI and AMI, 25% in ARI, and efficiency improvements of 12.10 to 37.41 times compared to state-of-the-art methods.
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. Our code is publicly available at https://github.com/XiaoyanWork/HyperSED.