关键词: 政府开放数据, 情景叙事, 用户认知联接, 隐私风险, 对照实验

Abstract:

[Purpose/Significance] Open government data (OGD) has increasingly adopted storytelling elements to improve public engagement and enhance user comprehension. Although this narrative approach enhances data accessibility and cognitive resonance, it raises significant privacy concerns. Specifically, storytelling may activate users' cognitive schemas, enabling them to infer sensitive personal information even from anonymized datasets. This dual effect between data usefulness and privacy risk is becoming an increasing challenge for data providers and policymakers. In this study, we aim to explore how storytelling in OGD affects users' cognitive reasoning processes and leads to privacy risks. Our work innovatively combines cognitive psychology, information science, and privacy risk assessment. This interdisciplinary approach offers a new perspective on how data narratives shape inference behavior. Distinct from existing research, this paper focuses on how cognitive mechanisms driven by storytelling influence users' perception and extraction of private information. This research holds practical significance for designing privacy-aware data disclosure strategies that strike a balance between openness and protection. [Method/Process] In order to analyze the cognitive mechanisms underlying privacy risk, we adopted a mixed-methods research design grounded in relevance theory, schema theory, and the S-O-R model. We first constructed a user cognitive connection model that conceptualized how narrative stimuli activated cognitive processing and led to privacy-related inferences. Based on this model, we developed a privacy risk assessment index comprising three primary dimensions: data association and reasoning, data processing and decoding, and implicit suggestion and implication. We then conducted a controlled experiment involving 236 participants, who were randomly divided into a storytelling group and a non-storytelling group. To analyze the collected data, we used the CRITIC method to assign objective weights to evaluation indicators and applied a fuzzy comprehensive evaluation method to quantify and compare privacy risks across groups. [Results/Conclusions] Our results demonstrated that storytelling significantly heightened users' ability to infer sensitive personal information. The average inference score in the storytelling group was significantly higher than that in the non-storytelling group (p<0.05), and the comprehensive privacy risk level was rated as "medium risk" compared to the non-storytelling group's "low risk." Across all three risk dimensions, the storytelling group consistently exhibited greater cognitive engagement and higher potential for privacy exposure. These findings suggested that while storytelling enhanced user understanding, it also increased the risk of privacy violations. As such, we recommended that government data platforms adopt non-storytelling or partially abstracted data presentation strategies to reduce risk while preserving clarity. From a policy perspective, we advocated for the integration of intelligent narrative-generation algorithms and privacy-by-design principles to protect users' information. Although limited by sample size and data diversity, this study offered a foundation for future research into the cognitive underpinnings of privacy risk. Further work may explore other forms of storytelling, demographic influences on inference behavior.

Key words: government open data, situational narrative, user-perceived connection, privacy risk, control experiment