SIFG: an ensemble model for sieving fake news from genuine without metadata by combining syntactic and semantic features

ABSTRACT

With the emergence of the Internet, social media, and smartphones, almost everyone can now claim that they have news in their pockets. This phenomenon makes people aware of their surroundings and knowledgeable of current events. But, it also presents new challenges, such as automatic detection, machine/bot-generated fake news, limited or no metadata, etc. In this paper, we mitigate some of these challenges by taking a step toward SIeving Fake News from Genuine (SIFG) and presenting a system named SIFG. In SIFG we have used basic and advanced Natural Language Processing techniques to detect online fake news automatically. This makes SIFG independent of the metadata, such as the source, network structure and behavior, temporal propagation, and responses, about the news. We also introduce specific Parts of Speech patterns, utilize Generalized Relevance Learning Vector Quantization for feature selection, and employ ensemble learning to improve the performance of SIFG. When tested with a publicly available online fake news dataset of 10,000 fake and 10,000 genuine news, SIFG shows promising results and achieves an accuracy in the range of 89.85% − 95%.

KEYWORDS:

• Ensemble learning
• latent dirichlet allocation
• natural language processing
• part of speech patterns
• syntactic and semantic features

Disclosure statement

No potential conflict of interest was reported by the author(s).