Social Network Rumor Detection: A Survey

doi:10.3969/j.issn.0372-2112.2020.07.023

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Social Network Rumor Detection: A Survey

更新时间：2025-07-08

- Social Network Rumor Detection: A Survey
- Acta Electronica Sinica Vol. 48, Issue 7, Pages: 1421-1435(2020)
- 作者机构：
  
  1. 四川大学网络空间安全学院,四川,成都,610065
  2. 四川大学信息管理中心,四川,成都,610065
  3. 成都信息工程大学软件工程学院,四川,成都,610225
  4. 四川大学网络空间安全学院,四川,成都,610065
  5. 四川大学信息管理中心,四川,成都,610065
  6. 成都信息工程大学软件工程学院,四川,成都,610225
- 作者简介：
- 基金信息：
  
  Applied Basic Research Project of Science and Technology Department of Sichuan Province (No.2018JY0193);Key Program of Department of Education of Sichuan Province (No.17ZA0238, No.18ZA0305, No.18ZA0301);National Natural Science Foundation of China (No.61872254)
- DOI：10.3969/j.issn.0372-2112.2020.07.023
  CLC： TP393
- Published Online：25 July 2020，
  
  Published：2020
- 稿件说明：
移动端阅览
Social Network Rumor Detection: A Survey[J]. Acta Electronica Sinica, 2020, 48(7): 1421-1435.
DOI：

Social Network Rumor Detection: A Survey[J]. Acta Electronica Sinica, 2020, 48(7): 1421-1435. DOI： 10.3969/j.issn.0372-2112.2020.07.023.

摘要

当前社会网络已取代传统媒体成为信息交流的重要平台，社会网络中的信息具有传播速度快，范围广，即时性强等优点.然而，由于发布信息时缺乏有效的监管手段，导致社会网络平台同时也成为谣言传播的温床.因此，快速有效地检测出社会网络谣言，对净化网络环境，维护公共安全至关重要.本文首先对谣言定义进行阐述，并描述当前谣言检测的问题及检测过程；其次，介绍不同数据获取方式并分析其利弊，同时对比谣言检测中不同的数据标注方法；第三，根据谣言检测技术的发展对现有的人工、机器学习和深度学习的谣言检测方法进行分析对比；第四，通过实验在相同公开数据集下对当前主流算法进行实证评估；最后，对社会网络谣言检测技术面临的挑战进行归纳并总结全文.

Abstract

The current social network has replaced traditional media as an important platform for information exchange. The information in social networks has the advantages of fast dissemination

wide range

and strong immediacy. However

due to the lack of effective supervision means when publishing information

the social network platform has also become a hotbed of rumors. Therefore

the rapid and effective detection of social network rumors is essential for purifying the network environment and maintaining public safety. Firstly

this article explains the definition of rumors

and the problems of current rumors detection and detection process are described. Secondly

different data acquisition methods are introduced and their advantages and disadvantages are analyzed. At the same time

different data annotation methods in rumor detection are compared. Thirdly

according to the development of rumor detection technology

analyze and compare the existing rumors detection methods of artificial

machine learning and deep learning. Fourthly

current mainstream algorithms are empirically evaluated under the same open data set through experiments. Finally

analyze and summarize the challenges faced by current social network rumor detection technology.

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