An intelligent spam detection framework using fusion of spammer behavior and linguistic.

ABSTRACT: The diverse types of fake text generation practices by spammer make spam detection challenging. Existing works use manually designed discrete textual or behavior features, which cannot capture complex global semantics of text and reviews. Some studies use limited features while neglecting other significant features. However, in case of a large number of features set, the selection of all features leads to overfitting the model and expensive computation. The problem statement of this research paper revolves around addressing challenges concerning feature selection and evolving spammer behavior and linguistic features, with the goal of devising an efficient model for spam detection. The primary objective of this endeavor was to identify the most efficacious subset of features and patterns fo

SUBMITTER: Iqbal A 

PROVIDER: S-EPMC11801581 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature