MCWDST: a Minimum-Cost Weighted Directed Spanning Tree Algorithm for Real-Time Fake News Mitigation in Social Media

TL;DR

This paper tackles real-time fake news on social media by integrating two stack deep learning detectors (CNN-3BiLSTM and 3BiLSTM) with a network-aware mitigation pipeline. The mitigation constructs a minimum-cost weighted directed spanning tree (MCWDST) rooted at a detected source and ranks nodes for immunization using a tri-component score that includes diffusion-path length (H), subtree reach (A), and diffusion speed (f_t). Benchmarking on multiple real-world datasets demonstrates strong fake news detection performance and effective, scalable mitigation that outperforms several baseline methods. The work offers a practical, real-time approach to curb misinformation and lays groundwork for future enhancements with transformer embeddings and graph representations.

Abstract

The widespread availability of internet access and handheld devices confers to social media a power similar to the one newspapers used to have. People seek affordable information on social media and can reach it within seconds. Yet this convenience comes with dangers; any user may freely post whatever they please and the content can stay online for a long period, regardless of its truthfulness. A need to detect untruthful information, also known as fake news, arises. In this paper, we present an end-to-end solution that accurately detects fake news and immunizes network nodes that spread them in real-time. To detect fake news, we propose two new stack deep learning architectures that utilize convolutional and bidirectional LSTM layers. To mitigate the spread of fake news, we propose a real-time network-aware strategy that (1) constructs a minimum-cost weighted directed spanning tree for a detected node, and (2) immunizes nodes in that tree by scoring their harmfulness using a novel ranking function. We demonstrate the effectiveness of our solution on five real-world datasets.

Figures (8)

• Figure 1: Detection and Mitigation Pipeline
• Figure 2: Stacked CNN-3BiLSTM architecture
• Figure 3: Stacked 3BiLSTM architecture
• Figure 4: From graph to the harmful nodes ranking
• Figure 5: Fake news detection page