论文标题
将人类融合到分散的假新闻检测中的群中学习
Integrating Human-in-the-loop into Swarm Learning for Decentralized Fake News Detection
论文作者
论文摘要
社交媒体已成为产生和传播假新闻的有效平台，这些新闻可能会误导人们甚至扭曲公众舆论。但是，在培训模型的集中数据收集过程中，用于假新闻检测的集中方法无法有效保护用户隐私。此外，它不能完全涉及学习检测模型循环中的用户反馈，以进一步增强假新闻检测。为了克服这些挑战，本文提出了一种新颖的分散方法，即基于人类的群体学习（HBSL），以将用户反馈整合到学习的循环中，并推断出识别假新闻而不以分散方式侵犯用户隐私的识别。它由分布式节点组成，这些节点能够独立学习和检测到本地数据的假新闻。此外，可以通过分散的模型合并来增强对这些节点训练的检测模型。实验结果表明，所提出的方法在基准数据集上检测假新闻方面的最先进方法优于最先进的分散方法。
Social media has become an effective platform to generate and spread fake news that can mislead people and even distort public opinion. Centralized methods for fake news detection, however, cannot effectively protect user privacy during the process of centralized data collection for training models. Moreover, it cannot fully involve user feedback in the loop of learning detection models for further enhancing fake news detection. To overcome these challenges, this paper proposed a novel decentralized method, Human-in-the-loop Based Swarm Learning (HBSL), to integrate user feedback into the loop of learning and inference for recognizing fake news without violating user privacy in a decentralized manner. It consists of distributed nodes that are able to independently learn and detect fake news on local data. Furthermore, detection models trained on these nodes can be enhanced through decentralized model merging. Experimental results demonstrate that the proposed method outperforms the state-of-the-art decentralized method in regard of detecting fake news on a benchmark dataset.
