Special Issue: Propagation Dynamics and Control of Disinformation in Online Social Platforms
Guest Editors
Dr. Lu-Xing Yang
School of Information Technology, Deakin University, Burwood, VIC, Australia
Email: y.luxing@deakin.edu.au
Dr. Qingyi Zhu
School of Cyber Security and Information Law, Chongqing University of Posts and Telecommunications, Chongqing, China
Email: zhuqy@cqupt.edu.cn
Prof. Ángel Martín del Rey
Department of Applied Mathematics, Universidad de Salamanca, Salamanca, Spain
Email: delrey@usal.es
Manuscript Topics
Disinformation is wholly or partly false and/or manipulated information that is intended to deceive and mislead audiences and/or obscure the truth for the purposes of causing strategic, political, economic, social, or personal harm or financial/commercial gain. Ubiquitous online social platforms, due to the inherent features of admitting anonymity and “freedom of speech”, have provided a fertile ground for the ceaseless emergence and rapid dissemination of disinformation. In addition, malicious use of critical technologies (e.g., “bots” that drown out truth, machine learning-enabled “deep fakes” that spread disinformation, etc.,) is increasingly occurring to amplify the prevalence of disinformation and its negative impacts. Consequently, the widespread propagation of disinformation has been a significant issue that requires the joint efforts by governments, organizations, policymakers, to monitor, govern and control.
In this regard, the two identified bottleneck challenges include the development of (1) mathematical models that achieve accurate prediction of disinformation propagation, and (2) efficient and flexible containment policies which are less intrusive. The Special Issue aims to collect latest advancements relevant to dealing with these two challenges, and to bring together computer scientists, mathematicians, physicists, and sociologists to enrich this research area.
Potential topics include but are not limited to the following:
• Population/individual-level propagation modeling of disinformation spreading
• Data-driven modeling for disinformation propagation
• Optimization/ optimal control/ game-based methods for containing disinformation propagation
• Artificial intelligence and machine learning-based detection methods for disinformation
Key words: disinformation, online social platform, propagation dynamics, propagation modeling, control of propagation
Instructions for authors
https://www.aimspress.com/mbe/news/solo-detail/instructionsforauthors
Please submit your manuscript to online submission system
https://aimspress.jams.pub/
Abstract
HTML
PDF