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Real-Time Multi-Modal Social Event Detection: A New Dataset and a Key Instance-Driven, Quality-Aware Graph Neural Network
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DOI：10.1109/tbdata.2026.3668616.png)
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Abstract
En 中文
Social event detection (SED) involves identifying and analyzing significant real-world events using data generated on social media platforms. With the rapid growth of platforms like Weibo and Twitter, users are sharing not just text but also images. However, most existing SED methods remain text-focused, limiting their ability to fully capture the complexity of real-world social dynamics. Moreover, the lack of multi-modal datasets specifically designed for SED has blocked the development of models that can effectively exploit these rich content types. To address these limitations, we introduced WEIBO2022, an extensive multi-modal SED dataset that includes both text and image data. The dataset is available in two versions: WEIBO2022-Medium, containing 25,435 entries and WEIBO2022-Large, containing 79,825 entries. In addition, we presented a novel network called the Key Instance-driven, Quality-aware Graph Neural Network (KQGNN), which features a key instance-driven library, a quality-aware learning process, and a multi-modal fusion module, enhancing its ability to detect events accurately in both offline and real-time settings. Extensive experiments showcase the exceptional performance and superiority of the proposed model, showing improvements in detection accuracy and effective prevention of catastrophic forgetting during continuous training.
Keywords:
Social event detection
online learning
multi-modal learning
graph neural networks
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