图书情报工作

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2025 , Vol. 69 >Issue 18: 131 - 146

DOI: https://doi.org/10.13266/j.issn.0252-3116.2025.18.011

研究论文

社交媒体倦怠行为因果事理演化逻辑研究

  • 赵又霖 ,
  • 刘卓辰 ,
  • 王嘉杰 ,
  • 封丽 ,
  • 石燕青
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  • 1 河海大学商学院 南京 211100;
    2 南京大学信息管理学院 南京 210023;
    3 河海大学图书馆 南京 211100;
    4 南京农业大学信息管理学院 南京 210095
赵又霖，副教授，博士，博士生导师，E-mail: sobzyl@hhu.edu.cn；刘卓辰，硕士研究生；王嘉杰，博士研究生；封丽，馆员，硕士；石燕青，副教授，博士，硕士生导师。

收稿日期: 2024-07-25

  修回日期: 2024-12-09

  网络出版日期: 2025-08-20

基金资助

本文系国家社会科学基金一般项目“社会感知驱动下用户画像时空数据知识组织研究（项目编号：21BTQ055）研究成果之一。

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A Study on the Causal Evolutionary Logic of Social Media Burnout Behaviors

  • Zhao Youlin ,
  • Liu Zhuochen ,
  • Wang Jiajie ,
  • Feng Li ,
  • Shi Yanqing
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  • 1 School of Business, Hohai University, Nanjing 211100;
    2 School of Information Management, Nanjing University, Nanjing 210023;
    3 Library of Hohai University, Nanjing 211100;
    4 School of Information Management, Nanjing Agricultural University, Nanjing 210095
Zhao Youlin, Associate Professor, PhD, Doctoral Supervisor, E-mail: sobzyl@hhu.edu.cn; Liu Zhuochen, Master’s Candidate; Wang Jiajie, Doctoral Candidate; Feng Li, Librarian, Master’s Degree; Shi Yanqing, Associate Professor, PhD, Master’s Supervisor.

Received date: 2024-07-25

  Revised date: 2024-12-09

  Online published: 2025-08-20

Supported by

This work is supported by the project of National Social Science Fund of China titled “Research on the Knowledge Organization of Spatiotemporal Data in User Profiling Driven by Social Perception” (Grant No. 21BTQ055).

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摘要

[目的/意义] 从因果事理演化逻辑的视角出发探究社交媒体倦怠行为的驱动因素和行为特征，有助于梳理用户消极使用社交媒体的原因，对社交媒体运营企业制定有效策略优化用户体验、维持用户活跃度具有重要意义。[方法/过程] 在挖掘显式因果关系的基础上，首先基于相似度计算得到隐式因果关系并提取主谓宾等结构进行事件表示，借助LERT-Large预训练模型将事件映射至向量空间并实现事件合并。其次进行事件泛化，进而通过GraphSAGE模型预测潜在的因果关系。最后采用多元主路径分析法提取各泛化事件下的核心因果事理演化逻辑。[结果/结论] 潜在因果关系预测方面，GraphSAGE模型在AUC指标上分别优于Node2Vec模型和GAT模型7.54%和9.91%，显示其预测效果最优。结果表明，社交媒体倦怠行为的压力要素可从信息生态系统理论出发，归纳为信息环境、信息技术、信息、信息人四个方面，且四个压力要素下的核心因果事理演化逻辑呈现出“刺激源—情感体验—行为反应”的范式。同一压力要素下的刺激源对跨压力要素的社交媒体倦怠行为具有驱动作用，且不同压力要素下的社交媒体倦怠行为之间亦存在“情感体验—行为反应”多层次关联演化特征。经检验所提出的方法能够更好地展现社交媒体倦怠行为的因果动态演化特征。

关键词： 因果事理演化; 社交媒体倦怠行为; 信息生态理论; 显式因果关系; 隐式因果关系; 潜在因果关系

本文引用格式

赵又霖 , 刘卓辰 , 王嘉杰 , 封丽 , 石燕青 . 社交媒体倦怠行为因果事理演化逻辑研究[J]. 图书情报工作, 2025 , 69(18) : 131 -146 . DOI: 10.13266/j.issn.0252-3116.2025.18.011

Abstract

[Purpose/Significance] Exploring the driving factors and characteristics of social media burnout behavior from the perspective of causal evolutionary logic helps understand users’ negative engagement with social media. It is crucial for social media operating companies in formulating effective strategies to enhance user experience and maintain user engagement. [Method/Process] Based on the exploration of explicit causal relationships, implicit causal relationships were first derived through similarity calculations and event representation is constructed by extracting subject-verb-object structures. Then the LERT-Large pre-trained model was utilized to map the events into a vector space enabling event merging. This was followed by event generalization, during which the GraphSAGE model was employed to predict potential causal relationships. Finally, a multi-pathway analysis was used to extract the core causal evolutionary logic under each generalized event. [Result/Conclusion] In terms of predicting potential causal relationships, the GraphSAGE model outperforms both the Node2Vec model and the GAT model by 7.54% and 9.91%, respectively, on the AUC metric, indicating its superior predictive performance. The findings suggest that the pressure factors contributing to social media burnout can be categorized into four dimensions based on the information ecosystem theory: information environment, information technology, information content, and information individuals. The core causal evolutionary logic under these four pressure factors follows the “stimulus-emotional experience-behavioral response” paradigm. Stimuli within the same pressure factor drive social media burnout behavior across different pressure factors, while a multi-level relational evolution characteristic of “emotional experience-behavioral response” also exists between diverse burnout behaviors across varying pressure factors. The methods proposed in this study effectively reveal the causal dynamic evolution characteristics of social media burnout behavior.

Key words： causal evolutionary logic; social media burnout behavior; information ecosystem theory; explicit causal relationships; implicit causal relationships; potential causal relationships

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