Multimodal Emergent Fake News Detection via Meta Neural Process Networks
Multimodal Emergent Fake News Detection via Meta Neural Process Networks
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DOI：
10.1145/3447548.3467153
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发表时间：
2021-06
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影响因子：
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通讯作者：
Yaqing Wang;Fenglong Ma;Haoyu Wang;Kishlay Jha;Jing Gao
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作者：
Yaqing Wang;Fenglong Ma;Haoyu Wang;Kishlay Jha;Jing Gao
虚假新闻以前所未有的速度传播，到达全球受众，并通过社交媒体平台将用户和社区置于巨大风险之中。基于深度学习的模型在对感兴趣事件的大量标记数据进行训练时表现出良好的性能，而由于域转移，模型的性能往往会在其他事件上下降。因此，现有的检测方法对突发事件的假新闻检测提出了重大挑战，而大规模标记数据集很难获得。此外，添加新出现事件的知识需要从头开始构建新模型或继续微调模型，这对于现实世界的环境来说可能具有挑战性、昂贵且不切实际。为了应对这些挑战，我们提出了一个名为 MetaFEND 的端到端假新闻检测框架，该框架能够快速学习，通过一些经过验证的帖子来检测紧急事件的假新闻。具体来说，所提出的模型将元学习和神经过程方法集成在一起，以享受这些方法的好处。特别是，提出了标签嵌入模块和硬注意力机制，通过处理分类信息和修剪不相关的帖子来提高有效性。对从 Twitter 和微博收集的多媒体数据集进行了广泛的实验。实验结果表明，我们提出的 MetaFEND 模型可以有效地检测从未见过的事件的假新闻，并且优于最先进的方法。
Fake news travels at unprecedented speeds, reaches global audiences and puts users and communities at great risk via social media platforms. Deep learning based models show good performance when trained on large amounts of labeled data on events of interest, whereas the performance of models tends to degrade on other events due to domain shift. Therefore, significant challenges are posed for existing detection approaches to detect fake news on emergent events, where large-scale labeled datasets are difficult to obtain. Moreover, adding the knowledge from newly emergent events requires to build a new model from scratch or continue to fine-tune the model, which can be challenging, expensive, and unrealistic for real-world settings. In order to address those challenges, we propose an end-to-end fake news detection framework named MetaFEND, which is able to learn quickly to detect fake news on emergent events with a few verified posts. Specifically, the proposed model integrates meta-learning and neural process methods together to enjoy the benefits of these approaches. In particular, a label embedding module and a hard attention mechanism are proposed to enhance the effectiveness by handling categorical information and trimming irrelevant posts. Extensive experiments are conducted on multimedia datasets collected from Twitter and Weibo. The experimental results show our proposed MetaFEND model can detect fake news on never-seen events effectively and outperform the state-of-the-art methods.