研究论文

危机情境下网络意见分歧消解路径研究

  • 安璐 ,
  • 李吐芬
展开
  • 1 武汉大学信息资源研究中心 武汉 430072;
    2 武汉大学信息管理学院 武汉 430072;
    3 武汉大学数据智能研究院 武汉 430072
安璐,教授,博士,博士生导师,E-mail:anlu97@163.com;李吐芬,硕士研究生。

收稿日期: 2024-04-11

  修回日期: 2024-08-12

  网络出版日期: 2025-03-18

基金资助

本文系国家自然科学基金创新研究群体项目“信息资源管理”(项目编号:71921002)和国家自然科学基金面上项目“危机情境下网络信息传播失序识别与干预方法研究”(项目编号:72174153)研究成果之一。

Research on the Resolution Path of Online Opinion Disagreements in Crisis Situations

  • An Lu ,
  • Li Tufen
Expand
  • 1 Center for Studies of Information Resources, Wuhan University, Wuhan 430072;
    2 School of Information Management, Wuhan University, Wuhan 430072;
    3 Institute of Data Intelligence, Wuhan University, Wuhan 430072
An Lu,professor,PhD,E-mail:anlu97@163.com;Li Tufen,master candidate.

Received date: 2024-04-11

  Revised date: 2024-08-12

  Online published: 2025-03-18

Supported by

This work is supported by the innovation research group project funded by the National Natural Science Foundation of China titled “Information Resource Management” (Grant No. 71921002) and the general project funded by the National Natural Science Foundation of China titled “Identification and Intervention Methods for Online Information Dissemination Disorder in Crisis Situations” (Grant No. 72174153).

摘要

[目的/意义] 探索在用户可接受范围内推荐相异观点,生成网络意见分歧消解路径,减小极化风险。[方法/过程] 通过文本聚类和情感分析识别博文观点,综合评价对象、主题和情感判断评论文本是否表示接受,测量用户极性值和接受相异观点的程度,生成网络意见分歧消解路径。[结果/结论] 选取微博作为数据来源,提出判断评论文本是否表示接受的规则,F1值达到0.891 1。生成的网络意见分歧消解路径能够实现被推荐用户向不同观点的延伸。所提出的网络意见分歧消解策略能够为危机情境下的舆情治理提供参考。

本文引用格式

安璐 , 李吐芬 . 危机情境下网络意见分歧消解路径研究[J]. 图书情报工作, 2025 , 69(6) : 85 -95 . DOI: 10.13266/j.issn.0252-3116.2025.06.007

Abstract

[Purpose/Significance] This paper recommends different viewpoints within user’s acceptable range to generate resolution path of online opinion disagreement and reduce the risk of polarization. [Method/Process] Firstly, this study identified views through text clustering and sentiment analysis. Secondly, it determined whether a comment represented acceptance based on recognizing the object of a comment, calculating topic similarity between the post and the comment, and sentiment analysis. Then, it calculated users’ polarity and the degree to which users could accept different viewpoints, based on which a resolution path of online opinion disagreement was generated. [Result/Conclusion] The Sina Weibo platform is chosen as the data source. It proposes a set of rules to determine whether a comment represents acceptance, with a F1 score of 0.8911. This study provides a possible path for resolving controversy, which can achieve the extension of recommended users to different viewpoints. The resolution path of online opinion disagreements proposed in this study may be able to provide some reference for public opinion governance in crisis situations.

参考文献

[1] 安宁, 安璐. 危机情景下群体情感表达的动力学机制研究[J]. 情报科学, 2022, 40(1): 148-157. (AN N, AN L. The dynamic mechanism of the group emotional expression in the crisis[J]. Information science, 2022, 40(1): 148-157.)
[2] 王晰巍, 王楠阿雪. 数智驱动的重大突发事件应急情报管理:新机遇、新挑战、新趋势[J]. 图书情报工作, 2022, 66(16): 4-12. (WANG X W, WANG N A X. Data intelligence-driven emergency information management of major emergencies: new opportunities, new challenges and new trends[J]. Library and information service, 2022, 66(16): 4-12.)
[3] 彭知辉. 论情报编写中的事实表达[J]. 科技情报研究, 2022, 4(1): 60-70. (PENG Z H. On the expression of fact in intelligence writing[J]. Scientific information research, 2022, 4(1): 60-70.)
[4] 郭明飞, 许科龙波. “后真相时代”的价值共识困境与消解路径[J]. 思想政治教育研究, 2021, 37(1): 54-61. (GUO M F, XU K L B. The value consensus dilemma and resolution path of the “post-truth era”[J]. Ideological and political education research, 2021, 37(1): 54-61.)
[5] 解庆锋. 网民信念沟:媒介接触对网民意见分歧的影响[J]. 新闻与传播研究, 2022, 29(7): 55-74, 127. (XIE Q F. Netizen’s belief gap: the influence of media contact on netizens’ divergent opinions[J]. Journalism & communication, 2022, 29(7): 55-74, 127.)
[6] 吴越, 李发根. 舆论极化研究综述[J]. 情报杂志, 2022, 41(4): 98-103, 110. (WU Y, LI F G. A review of public opinion polarization[J]. Journal of intelligence, 2022, 41(4): 98-103, 110.)
[7] SUNSTEIN C R. Republic.com[M]. Princeton: Princeton University Press, 2002.
[8] BAKSHY E, MESSING S, ADAMIC L A. Exposure to ideologically diverse news and opinion on Facebook[J]. Science, 2015, 348(6239): 1130-1132.
[9] INTERIAN R, MORENO J R, RIBEIRO C C. Polarization reduction by minimum-cardinality edge additions: complexity and integer programming approaches[J]. International transactions in operational research, 2021, 28(3): 1242-1264.
[10] GARIMELLA K, MORALES G D, GIONIS A, et al. Reducing controversy by connecting opposing views[C]//Proceedings of the tenth ACM international conference on Web search and data mining. New York: ACM, 2017: 81-90.
[11] GARIMELLA K, MORALES G D, GIONIS A, et al. Quantifying controversy in social media[J]. ACM transactions on social computing, 2018, 1(1): 1-27.
[12] GARIMELLA K, MORALES G D, GIONIS A, et al. Mary, mary, quite contrary: exposing twitter users to contrarian news[C]//Proceedings of the 26th international conference on World Wide Web, Geneva: International World Wide Web Conferences Steering Committee, 2017: 201-205.
[13] MUSCO C, MUSCO C, TSOURAKAKIS C E. Minimizing polarization and disagreement in social networks[C]//Proceedings of the World Wide Web conference. New York: ACM, 2018: 369-378.
[14] 宋彪, 朱建明, 黄启发. 基于群集动力学和演化博弈论的网络舆情疏导模型[J]. 系统工程理论与实践, 2014, 34(11): 2984-2994. (SONG B, ZHU J M, HUANG Q F. The internet public opinion grooming model based on cluster dynamics and evolutionary game theory[J]. Systems engineering-theory & practice, 2014, 34(11): 2984-2994.)
[15] 李洋, 李思佳, 叶琼元, 等. 面向突发事件网络舆情的社会情绪唤醒综合评价与疏导策略研究[J]. 情报资料工作, 2020, 41(6): 17-25. (LI Y, LI S J, YE Q Y, et al. Research on comprehensive evaluation and guidance strategies of emergency events network public opinion oriented social emotional arousal[J]. Information and documentation services, 2020, 41(6): 17-25.)
[16] HEITZ L, LISCHKA J A, BIRRER A, et al. Benefits of diverse news recommendations for democracy: a user study[J]. Digital journalism, 2022, 10(10): 1710-1730.
[17] 王田. 从群体特征看网络群体极化的形成与消解——以新浪微博“东莞挺住”事件为例[J]. 电子政务, 2017(5): 61-74. (WANG T. Formation and dissolution of network group polarization from the perspective of group characteristics: a case study of the “Dongguan Anti-pornography” event on Sina weibo[J]. E-government, 2017(5): 61-74.)
[18] TOMMASEL A, RODRIGUEZ J M, GODOY D. I want to break free! Recommending friends from outside the echo chamber[C]//15th ACM conference on recommender systems(RECSYS). New York: ACM, 2021: 23-33.
[19] MATAKOS A, ASLAY C, GALBRUN E, et al. Maximizing the diversity of exposure in a social network[J]. IEEE transactions on knowledge and data engineering, 2022, 34(9): 4357-4370.
[20] 何杨, 李洪心, 杨毅. 新媒体环境下网络群体极化动力机理与引导策略研究——以内容智能分发平台为例[J]. 情报科学, 2019, 37(3): 146-151, 168. (HE Y, LI H X, YANG Y. Dynamic mechanism and guiding strategy of network group polarization under new media environment: taking content intelligence distribution platform as an example[J]. Information science, 2019, 37(3): 146-151, 168.)
[21] 于孟利, 沈文瀚, 郑博雯. 社交媒体用户信息删除行为的动因研究——以微信朋友圈为例[J]. 信息资源管理学报, 2023, 13(4): 84-95, 121. (YU M L, SHEN W H, ZHENG B W. Research on the motivation of social media information deletion: take wechat moments as an example[J]. Journal of information resources management, 2023, 13(4): 84-95, 121.)
[22] DEFFUANT G, NEAU D, AMBLARD F, et al. Mixing beliefs among interacting agents[J]. Applications of simulation to social sciences, 2000, 3(1): 87-98.
[23] 王世雄, 祝锡永, 潘旭伟, 等. 网络舆情演化中群体极化的形成机理研究[J]. 情报学报, 2014, 33(6): 614-622. (WANG S X, ZHU X Y, PAN X W, et al. Research on the mechanism for group polarization in online public opinion dynamics[J]. Journal of the China Society for Scientific and Technical Information, 2014, 33(6): 614-622.)
[24] YEOMANS M, MINSON J, COLLINS H, et al. Conversational receptiveness: improving engagement with opposing views[J]. Organizational behavior and human decision processes, 2020, 160: 131-148.
[25] MINSON J A, CHEN F S, TINSLEY C H. Why won't you listen to me? measuring receptiveness to opposing views[J]. Management science, 2020, 66(7): 3069-3094.
[26] REVEILHAC M, SCHNEIDER G. Replicable semi-supervised approaches to state-of-the-art stance detection of tweets[J]. Information processing & management, 2023, 60(2): 103199.
[27] HINDMAN D B. Knowledge gaps, belief gaps, and public opinion about health care reform[J]. Journalism & mass communication quarterly, 2012, 89(4): 585-605.
[28] 殷波锐. 双社区网络中舆情演化模型研究[D]. 大连: 大连理工大学, 2013. (YIN B R. Opinion dynamics on network with dual-communities[D]. Dalian: Dalian University of Technology, 2013.)
[29] PIAGET J, TOMLINSON J, TOMLINSON A. The child’s conception of the world[M]. London: Routledge, 1997.
[30] BROOKES B C. The foundations of information science. part I. philosophical aspects[J]. Journal of information science, 1980, 2(3-4): 125-133.
[31] 贺颖, 孟鹏, 宋文胜. 情报用户知识结构的认知视角分析——布鲁克斯方程式的进一步探讨[J]. 情报杂志, 2003(7): 6-8. (HE Y, MENG P, SONG W S. Cognitive analysis of intelligence user’s knowledge structure: further exploration of Brookes equation[J]. Journal of intelligence, 2003(7): 6-8.)
[32] LORENZ J. Heterogeneous bounds of confidence: meet, discuss and find consensus![J]. Complexity, 2010, 15(4): 43-52.
[33] 安璐, 徐曼婷. 突发公共卫生事件情境下网民对政务微博信任度的测量[J]. 数据分析与知识发现, 2022, 6(1): 55-68. (AN L, XU M T. Measuring online trust in government microblogs in public health emergencies[J]. Data analysis and knowledge discovery, 2022, 6(1): 55-68.)
[34] 安璐, 周凡倩. 突发事件情境下冲突观点与重叠共识研究[J]. 情报理论与实践, 2023, 46(9): 69-78. (AN L, ZHOU F Q. Research on conflicting opinions and overlapping consensus in the public emergency situation[J]. Information studies: theory & application, 2023, 46(9): 69-78.)
[35] 卢国强, 黄微, 刘毅洲. 群体极化视域下突发事件网络舆情极端观点识别研究[J]. 情报资料工作, 2023, 44(1): 42-51. (LU G Q, HUANG W, LIU Y Z. Research on recognition of extreme viewpoints of online public opinion in emergency events from the perspective of group polarization[J]. Information and documentation services, 2023, 44(1): 42-51.)
[36] WANG S, PANG M, PAVLOU P A. Seeing is believing? How including a video in fake news influence users’ reporting of fake news to social media platforms[J]. MIS quarterly, 2022, 46(3): 1323-1354.
[37] ASKER D, DINAS E. Thinking fast and furious emotional intensity and opinion polarization in online media[J]. Public opinion quarterly, 2019, 83(3): 487-509.
[38] XING Y, WANG X, QIU C, et al. Research on opinion polarization by big data analytics capabilities in online social networks[J]. Technology in society, 2022, 68: 101902.
[39] KRAUS S J. Attitudes and the prediction of behavior: a meta-analysis of the empirical literature[J]. Personality and social psychology bulletin, 1995, 21(1): 58-75.
文章导航

/