Misinformation Containment in Online Social Networks: Nabanita Das, Indian Statistical Institute
Misinformation Containment in Online Social Networks: Role of Community Structure, N. Das, Indian Statistical Institute:
With the emergence of Online Social Networks (OSNs) as an effective medium of information dissemination, its abuse spreading misinformation has become a great concern to its users. Hence recently, the misinformation containment problem has emerged as an important topic of research. In general, given a snapshot of an OSN with a set of misinformed nodes and a
budget, the goal is to determine a set of seed nodes with the correct information, to contain the misinformation at the earliest.
In our work, we have studied the influence of the underlying community structure of the OSN to select the seed nodes statically,
independent of the distribution of misinformed nodes. Experiments on real OSNs reveal that the proposed techniques
outperform state-of-the-art algorithms significantly in terms of maximum infected time, average infected time and point of decline respectively,
manifesting the key role of community structure on misinformation containment in a social network. Moreover, the parallel implementation of the proposed algorithms on General-Purpose Graphics Processor Unit (GP-GPU) achieves around 10 times speedup compared to the conventional CPU algorithms facilitating early containment of misinformation.
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