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At first the focus is to transform the given textual data from dataset in form of vectors. These vectors represent the linguistic features of the article, and can be utilized to represent an article in Euclidean Space. Global Vector (GloVe) embedding is used to transform the articles into vectors. Each article then can be represented as the mean of vectors of the words it contains.
The result of this embedding is used to construct a similarity graph between articles in the dataset. Each node represents a articles and most similar nodes are connected by an edge in the graph. To calculate the similarity, i.e. distance between two articles, Word Mover's Distance is used. It uses semantically meaningful relations between words to find the similarity between two articles.
The next step uses the constructed graph, as an input to the Graph Convolutional Network (GCN) which classifies a given article into binary outputs, viz Fake or Real.
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