A Review of Event Detection Techniques for Online Public Opinion Monitoring

CHENG Gang; XIA Jingjing; DAI Lili; WU Yongfei; YANG Liu

doi:10.11959/j.issn.1000-0801.DXKX260556

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A Review of Event Detection Techniques for Online Public Opinion Monitoring

更新时间：2026-09-16

- A Review of Event Detection Techniques for Online Public Opinion Monitoring
- Telecommunications Science (2026)
- 作者机构：
  
  1.应急管理大学计算机与信息安全学院，河北廊坊 065201
  2.应急管理大学应急文化传播与法学院，河北廊坊 065201
- 作者简介：
- 基金信息：
  
  Social Science Development Research Project of Hebei Province: “Research on Urban Language Planning in Hebei Province for Emergency Language Services”(202403150);The National Natural Science Foundation of China(42377200);The Natural Science Foundation of Hebei Province, China(D2025508013)
- DOI：10.11959/j.issn.1000-0801.DXKX260556
  CLC： TP391.1
- Received：09 August 2026，
  
  Revised：2026-08-30，
  
  Accepted：08 September 2026，
- 稿件说明：
移动端阅览
CHENG Gang, XIA Jingjing, DAI Lili, et al. A Review of Event Detection Techniques for Online Public Opinion Monitoring[J/OL]. Telecommunications Science, 2026.
DOI：

CHENG Gang, XIA Jingjing, DAI Lili, et al. A Review of Event Detection Techniques for Online Public Opinion Monitoring[J/OL]. Telecommunications Science, 2026. DOI： 10.11959/j.issn.1000-0801.DXKX260556.

摘要

随着社交媒体技术的快速发展，社交媒体已成为社会信息传播与公众意见表达的重要场所。由于该类信息呈现短文本、多噪声等区别于传统文本的特征，传统舆情分析方法在事件发现、演化追踪与真实性判别方面的局限日益凸显，事件检测技术因此被日渐应用于网络舆情感知领域。本文系统性地综述了网络舆情事件检测技术的发展脉络与研究进展，重点阐述了网络舆情与社交媒体数据基础、事件检测任务分类与生命周期理论、统计与传统机器学习方法、深度语义与时序建模方法、多源信息与关系建模方法以及大模型与开放学习方法等核心内容，并系统梳理了主流评估指标与常用数据集。通过对比分析与文献研究，论证了不同技术路线在事件发现能力、语义表达能力与开放域适应能力等方面的演进优势。最后，本文深入探讨了当前研究面临的关键挑战，包括动态开放环境下未知事件发现能力不足、多源异构信息融合与跨模态语义对齐困难、大语言模型可靠性与高效部署瓶颈以及评价体系标准化等问题，并从开放世界持续感知、多模态统一理解、知识增强因果推理、高效可信大模型检测以及动态评测体系构建等方面对未来研究方向提出了建设性展望，研究结果可为舆情感知提供一种新的研究思路。

Abstract

With the rapid development of social media technology

social media has become an important platform for the dissemination of social information and the expression of public opinion. Because this type of information often comes in short texts with a lot of noise

which is different from traditional text

the limitations of traditional public opinion analysis methods in event detection

evolution tracking

and authenticity judgment have become increasingly apparent. As a result

event detection technology is increasingly being applied in the field of online public opinion perception. This article systematically reviews the development and research progress of online public opinion event detection technology

focusing on key aspects such as the basics of online public opinion and social media data

classification of event detection tasks and lifecycle theory

statistical and traditional machine learning methods

deep semantic and temporal modeling methods

multi-source information and relationship modeling methods

as well as large models and open learning methods. It also systematically outlines mainstream evaluation metrics and common datasets. Through comparative analysis and literature review

this paper demonstrates the advantages of different technical approaches in event discovery

semantic representation

and open-domain adaptability. Finally

The article delves into current key challenges

including insufficient ability to detect unknown events in dynamic open environments

difficulties in fusing multi-source heterogeneous information and cross-modal semantic alignment

reliability and efficient deployment bottlenecks of large language models

and standardization of evaluation systems. It also offers constructive prospects for future research in areas like continuous perception in an open world

unified multimodal understanding

knowledge-enhanced causal reasoning

efficient and trustworthy large model detection

and dynamic evaluation system construction. The research results can provide a new perspective on public opinion.

关键词

Keywords

references

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