用深度学习检测俄乌战争中可疑Telegram频道的虚假信息
Russo-Ukrainian war disinformation detection in suspicious Telegram channels
- 基于大模型微调，结合自建真假内容数据集
- 相比传统方法，识别准确率显著提升
- 适合关注网络战与信息对抗的研究者
本文提出一种先进方法，用于识别与俄乌冲突相关的可疑Telegram频道中的虚假信息。传统手段依赖人工验证或规则系统，难以应对快速演变的宣传策略和海量每日生成的数据。为此，该系统采用前沿深度学习技术，包括大语言模型（LLM），并在自建数据集上进行微调，该数据集包含经验证的虚假信息和真实内容。实验结果表明，该方法显著优于传统机器学习技术，在上下文理解与应对新兴传播策略方面具备更强适应性。
原文摘要 · Abstract (English)
The paper proposes an advanced approach for identifying disinformation on Telegram channels related to the Russo-Ukrainian conflict, utilizing state-of-the-art (SOTA) deep learning techniques and transfer learning. Traditional methods of disinformation detection, often relying on manual verification or rule-based systems, are increasingly inadequate in the face of rapidly evolving propaganda tactics and the massive volume of data generated daily. To address these challenges, the proposed system employs deep learning algorithms, including LLM models, which are fine-tuned on a custom dataset encompassing verified disinformation and legitimate content. The paper's findings indicate that this approach significantly outperforms traditional machine learning techniques, offering enhanced contextual understanding and adaptability to emerging disinformation strategies.
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