图书情报工作

图书情报工作 >

2024 , Vol. 68 >Issue 24: 77 - 91

DOI: https://doi.org/10.13266/j.issn.0252-3116.2024.24.007

情报研究

突发自然灾害事件短视频用户情感时空演化分析方法研究

  • 岳丽欣 ,
  • 刘自强 ,
  • 朱承宁
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  • 南京师范大学新闻与传播学院, 南京 210023
岳丽欣，讲师，博士；刘自强，副教授，博士，通信作者，E-mail:lzq@njnu.edu.cn；朱承宁，硕士研究生。

收稿日期: 2024-03-15

  修回日期: 2024-06-15

  网络出版日期: 2024-12-23

基金资助

本文系国家社会科学基金青年项目“多模态数据赋能的失真健康信息识别及传播干预研究”（项目编号：23CTQ013）研究成果之一。

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Research on the Analysis Method of User Emotional Spatiotemporal Evolution in Short Video Related to Sudden Natural Disaster Events

  • Yue Lixin ,
  • Liu Ziqiang ,
  • Zhu Chengning
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  • School of Journalism and Communication, Nanjing Normal University, Nanjing 210023

Received date: 2024-03-15

  Revised date: 2024-06-15

  Online published: 2024-12-23

Supported by

This work is supported by the National Social Science Fund of China project titled “Research on intervention of distorted health information recognition and dissemination with multimodal data empowerment” (Grant No. 23CTQ013).

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摘要

[目的/意义] 准确、有效地揭示突发自然灾害事件短视频用户情感演变特征、规律，对于网络舆情监测、短视频治理等相关研究与实践具有重要的意义。[方法/过程] 首先利用LSTM模型进行短视频用户情感计算；然后设计突发自然灾害短视频用户情感交互指标，分析短视频用户情感交互程度；最后综合利用时间序列和可视化方法揭示短视频用户情感的时间和地理空间演化特征、规律。[结果/结论] 通过对15个突发自然灾害事件的实证，发现我国突发自然灾害短视频用户情感时空演化特征主要有：不同突发自然灾害事件中短视频用户起始情感受事件严重程度影响、不同突发自然灾害事件中短视频用户情感时序演化受到作息影响和不同突发自然灾害事件中短视频用户情感和地理空间呈正相关关系等，验证情感时空演化分析方法的可行性和有效性。

关键词： 突发事件; 自然灾害; 短视频; 情感计算; 深度学习

本文引用格式

岳丽欣 , 刘自强 , 朱承宁 . 突发自然灾害事件短视频用户情感时空演化分析方法研究[J]. 图书情报工作, 2024 , 68(24) : 77 -91 . DOI: 10.13266/j.issn.0252-3116.2024.24.007

Abstract

[Purpose/Significance] Accurately and effectively revealing the emotional evolution characteristics and patterns of short video users in sudden natural disaster events is of great significance for related research and practice, such as online public opinion monitoring and short video governance. [Method/Process] First, the LSTM model was used for emotional calculation of short video users. Then, an emotional interaction index for short video users in sudden natural disasters was designed to analyze the degree of emotional interaction among short video users. Finally, time series and visualization methods were comprehensively adopted to reveal the temporal and geographic spatial evolution characteristics and patterns of short video user emotions. [Result/Conclusion] Empirical analysis of 15 sudden natural disaster events in China reveals the spatiotemporal evolution characteristics of short video user emotions. The initial emotions of short video users in different sudden natural disaster events are affected by the severity of the event. The emotional temporal evolution of short video users in different sudden natural disaster events is affected by their daily routines, and a positive correlation exists between short video user emotions and geographic space in different sudden natural disaster events. These findings verify the feasibility and effectiveness of the proposed method in this paper.

Key words： sudden events; natural disasters; short videos; emotional computing; deep learning

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