Integrating Human-in-the-loop into Swarm Learning for Decentralized Fake News Detection
This addresses privacy and user involvement issues in fake news detection for social media users, though it is incremental as it builds on existing decentralized approaches.
The paper tackles fake news detection on social media by proposing a decentralized method that integrates user feedback while preserving privacy, and it outperforms the state-of-the-art decentralized method on a benchmark dataset.
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.