基于LSTM的谣言检测系统的设计与实现

基于LSTM的谣言检测系统的设计与实现

[摘 要]随着社交媒体平台的广泛使用，谣言的生成与传播速度日益加快，严重干扰了公共信息环境，甚至造成社会恐慌和资源错配。传统依靠人工审核的方式效率低下、实时性差，难以满足当前信息流通的高速发展需求。针对这一问题，本文设计并实现了一个基于长短期记忆网络（LSTM）的谣言检测系统。系统分为两个阶段：首先基于传统机器学习模型（如SVM、随机森林）进行初步分类训练，随后引入深度学习方法，并结合文本内容、用户响应与传播路径等特征，对社交媒体上的文本进行多维度建模与判别。在数据集PHEME与Ma-Weibo上进行实验，LSTM模型在准确率与F1值上均优于传统方法，尤其在引入用户可信度与传播特征后，整体识别性能进一步提升。本文还探讨了谣言早期检测、多任务学习与跨平台泛化等关键问题，为构建实用化的智能谣言检测系统提供了理论基础与实验验证。
[关键词]自然语言处理；谣言检测；LSTM；深度学习；社交媒体；文本分类；用户响应；传播路径

Design and Implementation of the
LSTM System

Software Engineering Major Xi Zhenhua
Abstract:With the widespread use of social media platforms, the generation and dissemination of rumors are accelerating, seriously disrupting the public information environment and even causing social panic and resource mismatch. The traditional method of relying on manual review is inefficient and lacks real-time performance, making it difficult to meet the high-speed development needs of current information circulation. This article designs and implements a rumor detection system based on Long Short Term Memory (LSTM) network to address this issue. The system is divided into two stages: first, preliminary classification training is conducted based on traditional machine learning models such as SVM and random forest. Then, deep learning methods are introduced and combined with features such as text content, user response, and propagation path to model and discriminate text on social media in multiple dimensions. Experiments were conducted on the PHEME and Ma Weibo datasets, and the LSTM model outperformed traditional methods in terms of accuracy and F1 score. Especially after introducing user credibility and propagation features, the overall recognition performance was further improved. This article also explores key issues such as early rumor detection, multi task learning, and cross platform generalization, providing a theoretical basis and experimental verification for building a practical intelligent rumor detection system.
Key words: Natural language processing; Rumor detection; LSTM； Deep learning; Social media; Text classification; User response; Propagation path

目录
1 绪论 1
1.1 研究背景 1
1.2 研究目的与意义 1
1.3 国内外研究现状 2
1.4 研究内容 2
2 相关技术简介 3
2.1 自然语言处理 3
2.2 LSTM网络 3
2.3 传统分类模型 4
3 算法模型 7
3.1 总体架构 7
3.2 LSTM模型设计 7
4 实验分析 9
4.1 数据处理 9
4.2 模型设置 9
4.3 实验结果 10
5 编码与实现 11
总结与展望 13
参考文献 14

1 绪论

1.1 研究背景

随着社交媒体的迅猛发展，平台如微博、Twitter、Facebook、Reddit 等已成为用户获取新闻、表达观点和参与社会事件的重要渠道。社交媒体信息发布门槛低、传播速度快、范围广，使得每一位用户都可能成为信息源，这极大地提升了信息多样性与传播效率。但与此同时，这种去中心化的信息传播机制也暴露出明显的弊端——缺乏有效的信息审查与验证机制，导致虚假信息、谣言和误导性内容呈爆炸式增长。
近年来，诸如