Abstract
<h2>Abstract</h2> In the study of fake news spreading, it is essential to know how different types of spreaders differ in terms of their characteristics, interconnections, and cascading flow. The fake news graph analyzer (FNGA) is an open-source software that provides the required computations for such extended analyses on large graphs. Moreover, FNGA generates data for graph visualizations. Also, FNGA is designed to consider the spreading of both fake and true news simultaneously in the graph, leading to a variety of confrontational patterns. FNGA facilitates future research on fake news and the diffusion of any contagion within a graph of entities.
Highlights
Following the 2016 US Presidential Election, fake news has gained attention in academic scholarship [1]
The analysis based on user characteristics has roots in a variety of research contexts, from sports [2] to social media [3]
When it comes to spreading fake news or viral contagions, the underlying graph of users and the characteristics driven from it would play a major role in the analysis
Summary
Following the 2016 US Presidential Election, fake news has gained attention in academic scholarship [1]. The analysis based on user characteristics has roots in a variety of research contexts, from sports [2] to social media [3] When it comes to spreading fake news or viral contagions, the underlying graph of users and the characteristics driven from it would play a major role in the analysis. When it comes to modeling the spreading process of fake news on social media, considering the graph-based characteristics of users appear to be a necessity [4,5].
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