Fact checking by data partitioning

In this paper, we propose a novel fact-checking (truth-finding) strategy based on machine learning data clustering with the k-means method combined with the silhouette index to determine the optimal value of k, in order to detect the optimal partition of the set of attributes. Such an optimal partition maximizes the accuracy of the truth-finding process without having to explore all possible partitions. Results from our intensive experiments on synthetic and real data show that our approach outperforms those in (Lamine Ba et al., 2015), with a more reasonable computational time cost. Finally, we sketch a way to parallelize a given truth-finding process using the MapReduce paradigm with a view to avoiding the explosion of execution time when the size of the input data increases.

Autorentext

Osias Noël N. F. TOSSOU, currently Head of the Data Intelligence Department at MANOBI-Africa, received a Master's degree in Big Data at AIMS-Senegal in 2020 after a Bachelor's degree in Software Engineering at IFRI-UAC in Benin. In this paper, he worked with Dr. Lamine Mouhamadou BA his teacher. Lamine M. BA is a teacher-researcher at the UADB in Senegal.