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<p>In this paper, we address the problem of fake news detection on online political news by proposing a semi‑supervised model that combines a binary multi‑objective grasshopper optimization algorithm (GOA) for feature selection with a self‑training classifier. The proposed GOA variant simultaneously minimizes the number of selected textual features and the classification error, and is specifically adapted to discrete feature spaces. After feature selection, a semi‑supervised self‑training scheme is applied to exploit both labeled and unlabeled news articles. The method is evaluated on two public political news datasets, BuzzFeed Political News (1627 news articles) and Random Political News (75 news articles). Experimental results show that the proposed scheme achieves up to 96.7% reduction in feature dimensionality for BuzzFeed (from 5500 to 180 features) and 95.7% for RPN (from 2200 to 95 features) while obtaining accuracy values between 97 and 99% and F1‑scores up to 0.99, outperforming several classical machine learning classifiers as well as recent meta‑heuristic feature selection approaches. These findings indicate that the proposed optimization‑based semi‑supervised pipeline can effectively detect fake news using a compact subset of discriminative textual features.</p>

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A semi supervised fake news detection model using the grasshopper optimization algorithm

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月19日

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  • Kouroush rezvani,
  • Ali Ghaffari,
  • Mohammad Reza Ebrahimi Dishabi

摘要

In this paper, we address the problem of fake news detection on online political news by proposing a semi‑supervised model that combines a binary multi‑objective grasshopper optimization algorithm (GOA) for feature selection with a self‑training classifier. The proposed GOA variant simultaneously minimizes the number of selected textual features and the classification error, and is specifically adapted to discrete feature spaces. After feature selection, a semi‑supervised self‑training scheme is applied to exploit both labeled and unlabeled news articles. The method is evaluated on two public political news datasets, BuzzFeed Political News (1627 news articles) and Random Political News (75 news articles). Experimental results show that the proposed scheme achieves up to 96.7% reduction in feature dimensionality for BuzzFeed (from 5500 to 180 features) and 95.7% for RPN (from 2200 to 95 features) while obtaining accuracy values between 97 and 99% and F1‑scores up to 0.99, outperforming several classical machine learning classifiers as well as recent meta‑heuristic feature selection approaches. These findings indicate that the proposed optimization‑based semi‑supervised pipeline can effectively detect fake news using a compact subset of discriminative textual features.