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DEAP-FAKED: Knowledge Graph based Approach for Fake News Detection
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Detailed description Fake News on social media platforms has received a lot of attention in recent years, notably for incidents relating to politics (the 2016 US Presidential election) and healthcare (the COVID-19 infodemic, to name a few). Several approaches for identifying fake news have been presented in the literature. The methodologies range from network analysis techniques to Natural Language Processing (NLP) and the use of Graph Neural Networks (GNNs). In this work, we developed a framework called DEAP-FAKED (a knowleDgE grAPh FAKe nEws Detection) for detecting Fake News. We tested our methodology against two publicly available datasets that comprise articles from a variety of fields, including politics, business, technology, and healthcare. We also eliminate bias, such as the source of the articles, as part of the dataset pre-processing, which might affect the models' performance. DEAP-FAKED achieves an F1-score of 88% and 78% for the two datasets, respectively, an improvement of ∼21% and ∼3%, demonstrating the efficiency of DEAP-FAKED.
Group Societal Debates and Misinformation
Involved People Sharma, Rajesh, [email protected], orcid.org/0000-0003-3581-1332
State Complete
Thematic Cluster Visual Analytics [VA]
Thematic Cluster Text and Social Media Mining [TSMM]
Thematic Cluster Social Network Analysis [SNA]
Thematic Cluster Social Data [SD]
system:type Experiment
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Author Sharma Rajesh
Maintainer Sharma Rajesh
Version 1
Last Updated 16 September 2023, 10:05 (CEST)
Created 14 October 2022, 10:37 (CEST)