Disinformation Propagation Trend Analysis and Identification Based on Social Situation Analytics and Multilevel Attention Network
Datos Bibliográficos
| ID | 22106943 |
|---|---|
| Autores | Junchang Jing (Henan University of Science and Technology), Fei Li (0009-0006-1479-2821, Peng Cheng Laboratory), Bin Song (0000-0002-8882-659X, Henan University of Science and Technology), Zhiyong Zhang (0000-0003-1622-3447, Henan University of Science and Technology), Kim‐Kwang Raymond Choo (0000-0001-9208-5336, The University of Texas at San Antonio) |
| Año | 2023 |
| Volumen | 10 |
| Número | 2 |
| Páginas | 507-522 |
| Fecha de publicación | 2023-04-01 |
| Peer Reviewed | Sí |
| Open Access | Sí |
| Tipo | ARTICLE |
| Revista | IEEE Transactions on Computational Social Systems (JOURNAL) |
| Identificadores de la revista | ISSN: 2329-924X • E-ISSN: 2373-7476 |
| Editorial | Institute of Electrical and Electronics Engineers (IEEE) (PUBLISHER) |
| DOI | 10.1109/tcss.2022.3169132 |
| OpenAlex | W4285285348 |
| Idioma | EN |
| Citas recibidas | 3 |
| Referencias citadas | 48 |
Digital disinformation, such as those occurring on online social networks (OSNs), can influence public opinion, create mistrust and division, and impact decision- and policy-making. In this study, we propose a disinformation diffusion trend analysis and identification method, which uses social situation analytics and a multilevel attention network. First, we present a division and feature representation approach of social user circle based on the content sequence (internal driving factor) and social contextual information (external driving factor) of users associated with disinformation. Second, disinformation content feature, crowd response feature, and time-series feature are represented using embedding layer and bidirectional long short-term memory neural networks (Bi-LSTMs). We also present an attention mechanism model based on multifeature fusion, which can dynamically adjust the weight of each feature. On this foundation, the fused features are fed into the multilayer perceptron to identify the propagation quantity trend. According to the experimental results of real-world OSNs and social situation metadata, we conclude that while disinformation occurs across OSN platforms, the disinformation is more likely to spread widely in the original OSN platform. We also identify four typical disinformation propagation trends based on propagation patterns and propagation peak times. Findings from our experiments demonstrate that our proposed approach accurately identifies and predicts the diffusion trend of disinformation, which can then be used to inform mitigation strategy
Disinformation · Machine learning · Social media · Social network analysis · World Wide Web · Complex Network Analysis Techniques · Computer Science · Misinformation and Its Impacts · Opinion Dynamics and Social Influence · Artificial Intelligence
| Obras citantes distintas | 3 |
|---|---|
| Citas por año | 1,5 |
| Intervalo de citas | 2024 - 2026 (3) |
| Velocidad de citación | current |
| Altamente citado | No |
| Tipos de cita | Neutras: 3 |