Abstract：To enhance the governance effectiveness of debunking texts, a quantitative analysis of their response characteristics is conducted. Based on the computational narrative network model, the semantic features of debunking texts are structured, and narrative content nodes are used to depict the audience subject and theme, while narrative logic edges are used to represent response intentions. Design a robust matching filtering algorithm to quantify the degree of counterattack of false health information in debunking texts at the semantic level. By analyzing the narrative networks of four types of debunking texts: beauty and fitness, food safety, nutrition and health, and disease prevention and control, it is found that the cohesive structure of the network is driven by emotional logic and cognitive logic. The calculation results verified the effectiveness of the algorithm and found that the response rate of debunking texts was generally polarized. The characteristics of high response debunking texts are that they match the audience and their information needs, and comprehensively use behavioral guidance, psychological identification, etc. to achieve cognitive logic construction, supplemented by authoritative, emotional and other emotional logic to enhance interactive effects, effectively intervening in the dissemination of pseudo health information.
杨萍, 张军, 李鹏. 计算叙事视角下健康类辟谣文本回应特征研究[J]. 复杂系统与复杂性科学, 2026, 23(3): 37-44.
YANG Ping, ZHANG Jun, LI Peng. The Response Characteristics of Health Related Refutation Texts from the Perspective of Computational Narrative[J]. Complex Systems and Complexity Science, 2026, 23(3): 37-44.
[1] 张帅,王文韬,李晶,等.国外用户在线健康信息行为研究进展[J].图书馆论坛,2018,38(9):138-14 ZHANG S, WANG W T, LI J, et al. Research review on online health information behavior of foreign users[J]. Library Tribune,2018,38(9):138-147. [2] ZHAO Y, DA J, YAN J. Detecting health misinformation in online health communities: incorporating behavioral features into machine learning based approaches[J]. Information Processing & Management, 2021, 58(1): 102390. [3] CHOU W Y S, OH A, KLEIN W M P. Addressing health-related misinformation on s-ocial media[J]. Jama, 2018, 320(23): 2417-2418. [4] 李新月,王莹,韩文婷,等.健康辟谣信息的内容、质量与优化研究[J].情报资料工作,2022,43(3):84-93. LI X Y, WANG Y, HAN W T, et al. Research on the content, quality and optimization of health rumor-refuting Information[J]. Information and Documentation Services,2022,43(3):84-93. [5] 倪珍妮,王淳洋,司湘云.社会网络视角下在线健康社区知识共享与虚假信息传播差异分析[J].情报理论与实践,2023,46(7):67-75. NI Z N, WANG C Y, SI X Y. Analysis of the Differences between knowledge sharing and misinformation spreading in online health communities from a social network perspective[J]. Information Studies:Theory & Application,2023,46(7):67-75. [6] 陈燕方,周晓英,裴俊良.网络健康谣言的传播阻断和治理框架研究[J].情报资料工作,2024,45(01):35-45. CHEN Y F, ZHOU X Y, PEI J L. Research on the spread blocking and governance framework of online health rumors[J].Information and Documentation Services,2024,45(01):35-45. [7] 李建红,董伟,李文兰,等.在线健康信息搜寻行为影响因素的元分析研究[J].情报工程,2020,6(02):78-90. LI J H, DONG W, LI W L, et al. A meta-analysis of influencing factors of online health information searching behavior[J]. Information Engineering, 2019,6(02):78-90. [8] LI Z, ZHANG Q, DU X, et al. Social media rumor refutation effectiveness: evaluation, modelling and enhancement[J]. Information Processing & Management, 2021, 58(1): 102420. [9] 李红霞,郑石明,要蓉蓉.危机情境、危机回应策略与政府回应有效性——基于40个案例的定性比较分析[J].公共行政评论,2024,17(02):45-63+197. LI H X, ZHENG S M, YAO R R. Crisis situation, crisis response strategies, and government response effectiveness: qualitative comparative analysis of 40 cases.[J]. Journal of Public Administration,2024,17(02):45-63+197. [10] SHILLER R J. Narrative Economics: How Stories Go Viral and Drive Major Economic Events[M]. Princeton: Princeton University Press, 2020. [11] SANTANA B, CAMPOS R, AMORIM E, et al. A survey on narrative extraction from textual data[J]. Artificial Intelligence Review, 2023: 1-43. [12] MONTFORT N, PÉREZ R P. Computational models for understanding narrative[J].Revista de Comunicação e Linguagens, 2023(58): 97-117. [13] KEITH NORAMBUENA B F, MITRA T, NORTH C. a survey on event-based news narrative extraction[J].ACM Computing Surveys 55.14s (2023): 1-39. [14] 孔杉杉,张军,冯立超.基于计算叙事模型的虚假健康信息特征研究[J].情报杂志,2024,43(2):152-161. KONG S S, ZHANG J, FENG L C. Research on the characteristics of false health information based on computational narrative model[J]. Journal of Intelligence,2024,43(2):152-161. [15] JIANG X, MA J, JIANG J, GUO X. Robust feature matching using spatial clustering with heavy outliers[J].IEEE Transactions on Image Processing,2020, 29: 736-746. [16] HARRIS R. Evaluating internet research sources[J]. Virtual Salt, 1997, 17(1): 1-17. [17] 丁家友,唐馨雨.数字人文视角下的数据叙事及其应用研究[J].情报理论与实践,2022,45(2):121-128. DING J Y, TANG X Y. Research on data storytelling and its application from the perspective of digital humanities[J]. Information Studies:Theory & Application,2022,45(2):121-128. [18] PÓLYA T, CSERTO″ I. Emotion recognition based on the structure of narratives[J]. Electronics, 2023, 12(4): 919. [19] PHILLIPS S C, UYHENG J, CARLEY K M. A high-dimensional approach to measuring online polarization[J]. Journal of Computational Social Science, 2023, 6(2): 1147-1178.
