Jiaojiao Liu’s research while affiliated with Huazhong University of Science and Technology and other places

This study investigates the complex relationship between internet use and citizen political participation, emphasizing the mediating role of social networks. Using cross-sectional data from the 2021 Chinese Social Survey (CSS), we classify political participation into government-led and citizen-led activities and internet use into informational, entertainment-oriented, and service-oriented types. Overall, our findings reveal a positive correlation between internet use and both citizen-led participation and government-led participation (excluding voting), with a stronger association observed in the former. From a categorical perspective, informational and service-oriented internet use significantly influences all types of political participation, while entertainment-oriented use shows no significant correlation. Additionally, social networks mediate the relationship between internet use and political participation. Service-oriented internet use is more dependent on social networks to connect with political participation compared to information-oriented internet use. This study contributes to the ongoing debate on the impact of digital media on civic engagement by providing nuanced insights into how specific internet activities and social networks shape political participation in China.

Personal physiological data is the digital representation of physical features that identify individuals in the Internet of Everything environment. Such data includes characteristics of uniqueness, identification, replicability, irreversibility of damage, and relevance of information, and this data can be collected, shared, and used in a wide range of applications. As facial recognition technology has become prevalent and smarter over time, facial data associated with critical personal information poses a potential security and privacy risk of being leaked in the Internet of Everything application platform. However, current research has not identified a systematic and effective method for identifying these risks. Thus, in this study, we adopted the fault tree analysis method to identify risks. Based on the risks identified, we then listed intermediate events and basic events according to the causal logic, and drew a complete fault tree diagram of facial data breaches. The study determined that personal factors, data management and supervision absence are the three intermediate events. Furthermore, the lack of laws and regulations and the immaturity of facial recognition technology are the two major basic events leading to facial data breaches. We anticipate that this study will explain the manageability and traceability of personal physiological data during its lifecycle. In addition, this study contributes to an understanding of what risks physiological data faces in order to inform individuals of how to manage their data carefully and to guide management parties on how to formulate robust policies and regulations that can ensure data security.