Abstract
Along with the development of Information Technology, Online Social Networks (OSN) are constantly developing and have become popular media in the world. Besides communication enhancement benefits, OSN have such limitations on rapid spread of false information as rumors, fake news, and contradictory news. False information spread is collectively referred to as misinformation which has significant on social communities. The more sources and topics of misinformation are, the greater the number of users are affected. Therefore, it is necessary to prevent the spread of misinformation with multiple topics within a given period of time. In this paper, we propose a Multiple Topics Linear Threshold model for misinformation diffusion, and define a misinformation blocking problem based on this model that takes account of multiple topics and budget constraint. The problem is to find a set of nodes that minimizes the impact of misinformation at an allowed cost when blocking them from the network. We prove that the problem is NP-hard and the time complexity of the objective function calculation is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\#P$ </tex-math></inline-formula> -hard. We also prove that the objective function is monotone and submodular. We propose an approximation algorithm with approximation ratio <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(1-1/\sqrt {e})$ </tex-math></inline-formula> based on these attributes. For large networks, we propose an extended algorithm by using a tree data structure for quickly updating and calculating the objective function. Experiments conducted on real-world datasets show efficiency and effectiveness of our proposed algorithms in comparison with other state-of-the-art algorithms.
Highlights
Online social networks have become one of the most efficient communication channels over the last two decades with very high socio-economic impacts
We model the problem as a combination optimization problem based on the LT model with additional requirements of multi-topic and fixed budget for node selection
We propose the Multiple Topics Linear Threshold (MT-LT) model to describe the process of multi-topic information spreading by extending the LT model
Summary
Online social networks have become one of the most efficient communication channels over the last two decades with very high socio-economic impacts. Being interested in mitigating the misinformation risks, in this paper we study the problem of modelling misinformation diffusion and propose effective methods to detect misinformation sources and limit its spread. Pham et al.: Multi-Topic Misinformation Blocking With Budget Constraint on OSNs problem. In this paper we consider a more realistic scenario where multi-topic misinformation can reach and affect users at the same time We develop a new model of misinformation diffusion blocking with multiple topics and budget constraint. Effective approximation methods for minimizing misinformation spread are proposed from these monotone and submodular properties of the objective function. Based on the monotone and submodular properties of the objective function, we propose efficient and effective algorithms for solving the MMTB problem.
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