Identification of Influential Users to Curb the Spread of Fake News on Social Networks

In order to reduce the harm that fake news brings to people in SNs and provide an effective idea for social platform operators and the government, this project will conduct an in-depth analysis of this issue. The research questions of this project are:

(1) How can we identify influential users in the SN by considering multi-source structural information rather than limited information?
(2) How can we build a model that can simulate the impact of personal beliefs and decisions on others?
(3) How is the information propagation process in the SN affected by personal attitude changes, and how can we quantify the parameters in the information propagation model through real-world datasets to enable the model to perform well?

To answer these questions, this project will mainly study the role of individuals in information propagation and fake news propagation pattern in SNs, thereby providing recommendations for curbing the spread of fake news on online social platforms. The aims of this project are:

(1) To avoid the biased results caused by considering limited information, the evidential reasoning algorithm (Yang & Xu 2002) that is a typical multiple-criteria decision-making (MCDM) method to aggregate several pieces of information in a multilevel structure with uncertainty and ignorance will be applied in this
project to consider multi-source topology information in SNs to identify influential nodes, including the characteristics of users and statistical properties of the SN. The specific steps include but are not limited to the selection of information sources (Lu et al. 2016), the determination of weights (Zhou et al. 2020), and
the construction of the information fusion model. This project will analyze the distribution of these important users in the SN and observe whether they obey certain laws. Quantitative large-scale experiments and qualitative analyses will be undertaken simultaneously to verify the validity.
(2) After obtaining these important users in the SN, this project will explore how they can influence other users in the network based on the evidence theory (Ni et al. 2021), such as changing the decisions and behaviours of others. Evidence theory can express and combine the probability of various cases simultaneously
instead of simple yes or no through the mass function, which is suitable for describing users' responses to fake news. Through this part, we will examine how some influential users manipulate or promote the fake news propagation process in SNs, thereby creating infodemic and misleading large numbers of uninformed users. This will be experimentally evaluated in real-world and synthetic networks to observe the impact of social influence on fake news propagation caused by these important users.
(3) The fake news propagation pattern will be studied through the existing dynamical models by combining some reasonable factors (Zhang et al. 2016, Zhu & Zhao 2017), such as psychological factors and behavioural delay of users, as well as social recommendations. The performance of this model will be
improved by comparison and analysis with real-world propagation datasets. This project aims to propose a mechanism-driven and data-driven fake news propagation model.