基于集成学习的微博谣言早期检测

尹鹏博; 彭成; 潘伟民

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基于集成学习的微博谣言早期检测

更新时间：2026-09-04

- 基于集成学习的微博谣言早期检测
- Early detection of microblog rumors based on ensemble learning
- 微电子学与计算机 2021年38卷第1期页码：83-88
- 作者机构：
  
  新疆师范大学机构计算机科学技术学院,新疆,乌鲁木齐,830054
- 作者简介：
- 基金信息：
  
  国家自然科学基金-新疆联合基金项目 U1703261
- DOI：
  中图分类号：
- 纸质出版：2021
- 稿件说明：
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尹鹏博, 彭成, 潘伟民. 基于集成学习的微博谣言早期检测[J]. 微电子学与计算机, 2021, 38(1): 83-88.

YIN Peng-bo, PENG Cheng, PAN Wei-min. Early detection of microblog rumors based on ensemble learning[J]. 2021, 38(1): 83-88.
尹鹏博, 彭成, 潘伟民. 基于集成学习的微博谣言早期检测[J]. 微电子学与计算机, 2021, 38(1): 83-88. DOI：

YIN Peng-bo, PENG Cheng, PAN Wei-min. Early detection of microblog rumors based on ensemble learning[J]. 2021, 38(1): 83-88. DOI：

摘要

微博谣言早期检测对于谣言防治有重要作用，而在谣言发生的早期缺乏相关信息，检测难度大.该文通过构建检测特征和组合多种检测算法实现微博谣言的早期检测.在检测特征选取方面，不直接使用微博的评论转发信息，而是通过对待检测微博文本和用户历史微博进行情感分析，构建刻画出用户和微博的情感特征.在检测算法方面，采用集成学习方法作为谣言检测算法，算法的基模型由多个异构深度学习模型组成，元模型采取随机森林算法，以元模型在基模型的预测输出上进行二次训练的方式组合不同模型以提高检测准确率.实验表明，该方法在谣言早期检测方面具有较好的检测效果.

Abstract

The early detection of microblog rumors plays an important role in the prevention and control of rumors. However

it is difficult to detect rumors due to the lack of relevant information in the early stage of rumor occurrence. In this paper

the early detection of microblog rumors is realized by selecting effective detection characteristics and combining multiple detection algorithms. In the term of selecting detection characteristics

this paper constructs the emotional characteristics of users and microblog through the emotional analysis of microblog text and users historical text instead of using the information extracted from comments and forwards. In the term of detection algorithm

the ensemble learning method is adopted as the rumor detection algorithm. The base model is composed of multiple heterogeneous deep learning models. The random forest algorithm is used in the meta model to combine different models in the way of secondary training on the prediction output of the base model to improve the detection accuracy. Experiments show that this method has a good detection effect in the early detection of rumors.

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references

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