用贝叶斯图神经网络自动筛选社交媒体中与事件相关的帖子。
Interactive Event Sifting using Bayesian Graph Neural Networks
- 基于贝叶斯图神经网络构建多模态分类模型
- 主动学习和伪标签可减少人工标注量
- 跨事件未标注数据能提升模型性能
法医分析人员常通过社交媒体图文信息理解重要事件。主要挑战在于初步筛选无关内容。本文提出一种交互式训练流程，构建以事件为中心的、基于学习的多模态分类模型，实现自动化内容清洗。方法基于贝叶斯图神经网络（BGNNs），评估了主动学习与伪标签策略对降低人工标注需求的效果。结果表明，BGNNs适用于事件类社交媒体数据筛选，主动学习与伪标签的价值取决于具体场景，且引入其他事件的未标注数据可提升模型表现。
原文摘要 · Abstract (English)
Forensic analysts often use social media imagery and texts to understand important events. A primary challenge is the initial sifting of irrelevant posts. This work introduces an interactive process for training an event-centric, learning-based multimodal classification model that automates sanitization. We propose a method based on Bayesian Graph Neural Networks (BGNNs) and evaluate active learning and pseudo-labeling formulations to reduce the number of posts the analyst must manually annotate. Our results indicate that BGNNs are useful for social-media data sifting for forensics investigations of events of interest, the value of active learning and pseudo-labeling varies based on the setting, and incorporating unlabelled data from other events improves performance.
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