Author: News Room
本文详细介绍了Liar、FakeNewsNet和Weibo三类数据集的特征提取和分类设计，结合Bert、RoBERTa、XLNet、ERNIE和GPT-3.5等多种模型进行了集成测试，最终构建了一个多模态特征融合、多任务模型，对外部输入数据进行激活实验，构建了一个对比、综合的实验设计，最终构建了采用Resnet和SlowFast网络构建的多模态特征融合优化的大型机器学习模型，为真假新闻检测构建了一个综合性和全面性的评估框架。该模型构建过程中，首先进行了机器学习模型的共生优化设计，构建了一个有效的多任务融合模型，构建了一个特征融合模型，构建了一个多模态特征融合模型，构建了一个深度学习模型，构建了一个深度学习网络，构建了一个系统的多任务多网络模型，构建了一个优化机制，构建了一个分类模型，并构建了一个综合评估模型，构建了一个对比分析模型，构建了一个综合评估模型，构建了一个对比分析模型，构建了一个综合评估模型，构建了一个对比分析模型，构建了一个对比分析模型。
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