TY - JOUR

T1 - MCWDST

T2 - A Minimum-Cost Weighted Directed Spanning Tree Algorithm for Real-Time Fake News Mitigation in Social Media

AU - Truica, Ciprian Octavian

AU - Apostol, Elena Simona

AU - Nicolescu, Radu Catalin

AU - Karras, Panagiotis

N1 - Publisher Copyright: © 2013 IEEE.

PY - 2023

Y1 - 2023

N2 - 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 arises to detect untruthful information, also known as fake news. 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.

AB - 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 arises to detect untruthful information, also known as fake news. 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.

KW - Fake news detection

KW - fake news propagation

KW - network-aware fake news mitigation

KW - real-time network immunization

U2 - 10.1109/ACCESS.2023.3331220

DO - 10.1109/ACCESS.2023.3331220

M3 - Journal article

AN - SCOPUS:85177056475

VL - 11

SP - 125861

EP - 125873

JO - IEEE Access

JF - IEEE Access

SN - 2169-3536

ER -