情报杂志2024,Vol.43Issue(4) :135-145,67.DOI:10.3969/j.issn.1002-1965.2024.04.019

基于语义不一致性的网络暴力舆情预警方法

Early Warning Method for the Cyber-violence Event Based on Semantic Inconsistency

叶瀚 胡凯茜 李欣 孙海春
情报杂志2024,Vol.43Issue(4) :135-145,67.DOI:10.3969/j.issn.1002-1965.2024.04.019

基于语义不一致性的网络暴力舆情预警方法

Early Warning Method for the Cyber-violence Event Based on Semantic Inconsistency

叶瀚 1胡凯茜 1李欣 1孙海春1
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作者信息

  • 1. 中国人民公安大学信息网络安全学院 北京 102623
  • 折叠

摘要

[研究目的]现有网络暴力舆情预警手段对专家知识和先验事件信息存在较大依赖.该文提出一种基于语义不一致性的网络暴力舆情预警方法,以实现对现实情境中网络暴力舆情的有效预警.[研究方法]该文采用网络暴力舆情预警的语义不一致性指数计算方法,并结合时序异常检测方法对语义不一致性异常波动进行监测并预警.收集2022 年7 月至9 月间的微博数据进行模拟预警,验证模型预警能力.[研究结论]实验中平均预警准确率为76.57%,预警覆盖率为60.92%.经验证可实现真实世界条件下至多提前36 小时发出预警.实验揭示了网络暴力舆情事件在发展阶段可对整体内容的主题状态产生显著可监测的影响,基于该特性能够实现或增强舆情预警感知能力.

Abstract

[Research purpose]The existing early warning methods for the cyber-violence rely heavily on expert knowledge and prior e-vent information,so we propose an early warning method for the cyber-violence event based on Semantic Inconsistency(SI)to realize ef-fective early warning in realistic situation.[Research method]We design a calculation method for SI for the early warning of the cyber-violence event,and integrate a semantic inconsistency monitoring scheme based on anomaly detection in different time sequences,and conduct empirical analysis.We collect"Sina Weibo"data from July to September 2022 for simulated early warning to verify the early-warning capability of the model.[Research conclusion]The average accuracy rate of the early warning reaches 76.57%and the early warning coverage reaches60.92%.The experiment realizes the early warning of up to36 hours in real-world application scenarios.The experiment reveals that a cyber-violence event can have a significantly measurable impact on the topic state of the overall content in the de-velopment stage,and we can realize or enhance the early warning perception ability of the public opinion based on this feature.

关键词

网络暴力/语义不一致性/风险预警/时序异常检测/舆情预警方法

Key words

cyber-violence/semantic inconsistency/risk warning/time series anomaly detection/early warning method

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基金项目

中国人民公安大学网络空间安全执法技术双一流创新研究专项(2023SYL07)

出版年

2024
情报杂志
陕西省科学技术信息研究所

情报杂志

CSTPCDCSSCICHSSCD北大核心
影响因子:1.502
ISSN:1002-1965
参考文献量34
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