The propagation of fake news on online social networks represents one of the main causes of the spread of misinformation in modern society. In this paper, a bipartite graph model is proposed to analyze the relationships between different fake news spreaders regarding different topics represented by keywords derived from the content of the articles. The projections of the weighted graph allow us to identify the spreaders of fakeness that play a central role and the interconnections between topics most affected by fakeness. The effectiveness of the model is demonstrated by applying it to real datasets derived from scraping on the Politifact.com website and considering the weights obtained from fact-checking of the news content.