Scholarlink

<p>Social media platforms are digital arenas for information dissemination. Consequently, they have become a battlefront for disinformation, propaganda, fake news, and strategic narratives during emerging geopolitical events such as war. Machine learning has increasingly been developed to systematically analyze and classify digital content, shedding light on online propaganda’s underlying patterns and strategies. Here we applied multiple machine learning algorithms to classify pro-Russian communications on Twitter (tweets) following the Russian full-scale invasion of Ukraine. Machine learning models included Logistic Regression, Support Vector Machine, Bi-directional Long Short Term Memory, Naive Bayes, K-Nearest Neighbours, and Extreme Gradient Boosting. Model performances were evaluated based on accuracy, precision, recall, and F1-score metrics. The Support Vector Machine and Extreme Gradient Boosting models consistently outperformed others, achieving higher accuracy and F1 scores. In general, model performance improved with increasing dataset size. The results highlighted the complexities of online informational manipulation, emphasizing the need for a deeper sentiment analysis. This study offers a pioneering contribution to understanding information manipulation in the context of the Russia-Ukraine war and provides valuable insights into the intricacies of digital information warfare.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Investigation of machine learning approaches to classify war-related content during Russian full-scale invasion of Ukraine

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月18日

查看全文
  • Halyna Padalko,
  • Dmytro Chumachenko,
  • Navneet Kaur,
  • Irfhana Zakir Hussain,
  • Jasleen Kaur,
  • Matheus Lotto,
  • Zahid A. Butt,
  • Plinio Pelegrini Morita

摘要

Social media platforms are digital arenas for information dissemination. Consequently, they have become a battlefront for disinformation, propaganda, fake news, and strategic narratives during emerging geopolitical events such as war. Machine learning has increasingly been developed to systematically analyze and classify digital content, shedding light on online propaganda’s underlying patterns and strategies. Here we applied multiple machine learning algorithms to classify pro-Russian communications on Twitter (tweets) following the Russian full-scale invasion of Ukraine. Machine learning models included Logistic Regression, Support Vector Machine, Bi-directional Long Short Term Memory, Naive Bayes, K-Nearest Neighbours, and Extreme Gradient Boosting. Model performances were evaluated based on accuracy, precision, recall, and F1-score metrics. The Support Vector Machine and Extreme Gradient Boosting models consistently outperformed others, achieving higher accuracy and F1 scores. In general, model performance improved with increasing dataset size. The results highlighted the complexities of online informational manipulation, emphasizing the need for a deeper sentiment analysis. This study offers a pioneering contribution to understanding information manipulation in the context of the Russia-Ukraine war and provides valuable insights into the intricacies of digital information warfare.