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

• URL: http://arxiv.org/abs/2302.12190v2
• Date: Fri, 19 Jan 2024 16:30:14 GMT
• Title: MCWDST: a Minimum-Cost Weighted Directed Spanning Tree Algorithm for Real-Time Fake News Mitigation in Social Media
• Authors: Ciprian-Octavian Truic\u{a} and Elena-Simona Apostol and Radu-C\u{a}t\u{a}lin Nicolescu and Panagiotis Karras
• Abstract summary: We present an end-to-end solution that accurately detects fake news and immunizes network nodes that spread them in real-time. To mitigate the spread of fake news, we propose a real-time network-aware strategy.
• Score: 10.088200477738749
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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