基于创新的多目标优化,自动检测假新闻
Cebrail Barut1, Suna Yildirim2, Bilal Alatas3
1Department of Continuing Education Center, Firat (Euphrates) University, Elazig, Turkey.
PeerJ. Computer science
|September 24, 2025
概括
检测假新闻至关重要. 这项研究引入了一种新的元启发方法,用于更快,更准确地检测假新闻,特别是在较小的数据集上更有效.
科学领域:
- 计算机科学 计算机科学
- 信息科学 信息科学 信息科学
- 人工智能的人工智能
背景情况:
- 数字时代通过互联网和社交媒体提供了前所未有的信息获取机会.
- 社交媒体上的快速信息传播缺乏可靠的准确性验证机制,增加了虚假新闻的传播.
- 有效的假新闻检测对于减轻社会错误信息至关重要.
研究的目的:
- 开发一种有效的检测假新闻的方法.
- 解决现有的假新闻检测方法的局限性,特别是他们专注于单个标准优化.
- 提出一种新的方法,用于在假新闻检测中同时优化精度和回忆.
主要方法:
- 这项研究提出了一种新的元启发方法来检测假新闻.
- 它在非主导排序遗传算法2 (NSGA-2) 中引入了拥挤距离水平方法的创新应用.
- 这种方法可以同时优化两个关键标准:精度和回忆.
主要成果:
- 拟议的方法在检测假新闻方面表现出高的成功率.
- 在小型数据集上,性能特别显著,超过了传统的人工智能和机器学习方法.
- 验证使用各种数据集进行,包括Covid-19新闻,叙利亚战争报道和FakeNewsNet (Gossipcop).
结论:
- 开发的metaheuristic方法在假新闻检测方面取得了重大进展.
- 同时优化精度和回忆被证明是有效的,特别是在挑战小型数据集时.
- 这种方法提供了一个更强大的解决方案,用于打击在线虚假信息的传播.
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