The fake news graph analyzer : An open-source software for characterizing spreaders in large diffusion graphs
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
| Original language | English |
|---|---|
| Article number | 100182 |
| Journal / Publication | Software Impacts |
| Volume | 10 |
| Online published | 26 Nov 2021 |
| Publication status | Published - Nov 2021 |
Link(s)
| DOI | DOI |
|---|---|
| Attachment(s) | Documents
Publisher's Copyright Statement
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| Link to Scopus | https://www.scopus.com/record/display.uri?eid=2-s2.0-85120875444&origin=recordpage |
| Permanent Link | https://scholars.cityu.edu.hk/en/publications/publication(eb0c9a1f-7e32-4723-86dd-56e55fc02efa).html |
Abstract
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.
Research Area(s)
- fake news, social network, Spreading process, Graph analysis, Twitter
Citation Format(s)
The fake news graph analyzer: An open-source software for characterizing spreaders in large diffusion graphs. / Bodaghi, Amirhosein; Oliveira, Jonice; Zhu, Jonathan J.H.
In: Software Impacts, Vol. 10, 100182, 11.2021.
In: Software Impacts, Vol. 10, 100182, 11.2021.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
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