用于假新闻检测的人工智能模型的比较

Ștefan Emil Repede, R. Brad
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引用次数: 0

摘要

本文旨在比较当前最先进的自然语言处理模型(NLP),该模型基于一组指标对假新闻检测进行微调,并评估其作为虚假信息管理结构一部分的有效性。发展这一领域的必要性是对假新闻泛滥和不受监管的传播的回应,假新闻是当今时代的主要困难之一。由于技术可能具有多种用途,人工智能技术的发展对错误信息和虚假信息的产生和传播产生了直接影响。目前,机器学习技术被用于开发大型语言模型(LLM)。科学上的这些发展也被用于造谣活动。与此相关的是,虚假信息管理的概念已经成为当前网络威胁环境中不可或缺的网络安全问题
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A comparison of artificial intelligence models used for fake news detection
This article aims to compare current state-of-the-art natural language processing models (NLP) fine-tuned for fake news detection based on a set of metrics and asses their effectiveness as a part of a disinformation management structure. The need for a development of this area comes as a response to the overwhelming and unregulated spread of fake news that represents one of the current major difficulties in today`s era. The development of AI technologies has a direct impact over the creation and spreading of misinformation and disinformation as a result of the multiple uses that technology may have. Currently, machine learning techniques are used for the development of large language models (LLM). These developments in science are also used in disinformation campaigns. Related to this matter the concept of disinformation management has arisen as a cybersecurity issue integral in the current cyber threat landscape
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