Trust-based Misinformation Containment in Directed Online Social Networks
2023 15th International Conference on COMmunication Systems & NETworkS (COMSNETS)
In today's world, Online Social Networks (OSNs) play a crucial role in our everyday life. But, its abuse to disseminate misinformation has turned out to be a major concern to us. Hence, the misinformation containment (MC) problem has attracted a lot of attention in recent times. For a given OSN with a fixed budget, this paper proposes a trust-based static technique independent of the distribution of misinformed nodes to select a set of trusted seed nodes leveraging the topologies of the network, to contain and decimate the misinformation faster. We follow a modified form of Competitive Linear Threshold Model with One Direction state Transition (LT1DT) to study the propagation dynamics of both the correct information and misinformation. Simulation studies on three real-world OSNs show that proposed method outperforms earlier work  significantly in terms of maximum number of misinformed nodes, infected time, point of inflection and number of misinformed nodes in steady state re-spectively. Moreover, its parallel implementation achieves almost 32 x speedup, making the procedure scalable for large scale OSNs to contain and decimate misinformation in real-time.
Competitive Linear Threshold Model, Disjoint and Overlapping Community Structures, General-Purpose Graphic-Processor Unit (GP-GPU), Misinformation Containment (MC), Online Social Networks (OSNs), Parallel Algorithms
Information Systems and Technology
Arnab Kumar Ghoshal, Nabanita Das, Soham Das, and Subhankar Dhar. "Trust-based Misinformation Containment in Directed Online Social Networks" 2023 15th International Conference on COMmunication Systems & NETworkS (COMSNETS) (2023): 594-602. https://doi.org/10.1109/COMSNETS56262.2023.10041359