The ethical dimensions of human-AI (artificial intelligence) interaction demand attention. As artificial intelligence assistants become more anthropomorphized, will the public interact with AI as humans morally? This study applied content analysis to data from an online question-and-answer platform in China (N = 287) to explore the public's judgments of gratitude toward artificial intelligence assistants. The findings revealed the majority supports expressing gratitude, while a significant minority disagrees, indicating diverse ethical judgments. By further analyzing people's reasoning, this study found that supporters attribute gratitude to moral autonomy driven by virtue ethics, moral responsibility for responsible AI, and the perceived source identity of anthropomorphized AI as human, aligning with the Computers-are-Social-Actors paradigm. In contrast, opponents doubt AI's moral agency, highlighting the perceived source of AI as machines, and they judge that treating it with human manners is useless and potentially dangerous. These insights enhance the understanding of the public's view of ethical considerations regarding AI assistants, contribute to gratitude research in the context of human-AI interaction, extend the moral dimension of the Computers-are-Social-Actors paradigm, and emphasize the importance of moral and responsible AI use. Suggestions for future research based on the exploratory findings are also discussed.

This paper investigates the development and characteristics of research data management（RDM）of Ivy League University libraries in the United States, which shed light to the RDM services in China.Based on the network research, it is found that the RDM of 8 Ivy League University libraries, including Harvard University, University of Pennsylvania and Yale University, has the characteristics of clear responsibilities of management organizations, providing scientific research data lifecycle management services, and establishing a wide range of cooperation networks inside and outside the university.Chinese university libraries can improve their RDM service capabilities by accelerating the construction of a library-centered RDM system, embedding the life cycle of scientific research data, and focusing on cooperation and mutual learning inside and outside the university.

Climate change misinformation leads to significant adverse impacts and has become a global concern. Identifying misinformation and investigating its characteristics are of great importance to counteract misinformation. Therefore, this study aims to characterize the semantic features (frames and authority references) of climate change misinformation in the context of Chinese social media. Posts concerning climate change were collected from Weibo between January 2010 and December 2020. First, veracity, frames, and authority references were manually labeled. Then, we applied logistic regression to examine the relationship between information veracity and semantic features. The results revealed that posts concerning environmental and health impact and science and technology were more likely to be misinformation. Moreover, posts referencing non-specific authority sources are more likely to be misinformed than posts making no references to any authority references. This study provides a theoretical understanding of the semantic characteristics of climate change misinformation and practical suggestions for combating them.

科研数据管理是数据密集型科学范式和开放科学趋势下各高校图书馆的重要服务性工作.论文采用网络调研和内容分析法,从科研数据管理服务体系、科研数据管理政策、科研数据管理服务等方面,总结帝国理工学院、伦敦大学学院、伦敦政治经济学院等英国G5大学图书馆科研数据管理实践经验.从完善科研数据管理法规,颁行科研数据管理细则,加强国家层面科研数据管理服务支持,加快构建以图书馆为核心的科研数据协同管理体系,提升服务管理能力等方面,探讨对我国高校开展科研数据管理实践的启示和借鉴.

Misinformation on social media is a nonnegligible phenomenon that causes successive adverse impacts. Numerous scholarly efforts have been devoted to automatic misinformation detection to address this problem. The effective feature is the key to achieving high identification perfor-mance. However, the effectiveness of the feature may change in different issues and time considering the manifold social contextual reasons. Most extant literature on misinformation detection does not differentiate between topics, issues or domains. Although some research compares detection across domains, they concentrate on the model's overall performance, neglecting the effectiveness of individual features. Furthermore, the comparison studies mainly incorporate single-domain issues rather than issues that cover multiple domains. It is still difficult to determine which domain's misinformation characteristics will match those of multi-dimensional issues. Since the misinformation nowadays covers multiple domains, finding robust features in misinformation detection over issues and time is an urgent research agenda. In this study, we collected datasets of two issues, climate change and genetically modified organisms (GMOs), between January 1st, 2010 and December 31st, 2020 on Weibo, manually annotated the veracity status of the posts, and compared the performance of the proposed features in identifying misinformation by applying logistic regression. The results demonstrate that (1) the predicting power of content-based features, including topic and sentiment, is relatively robust compared to user-based and propagation-based features across issues and time. (2) The feature effectiveness varied at different time points. Our findings imply that future research could consider focusing more on content-based features, especially implicit features from the content in misinformation detection. Moreover, researchers should evaluate the feature effectiveness at different time stages to improve the efficiency of misinformation detection.

Objective: In the aging world, the depression of older adults has aroused great concern. It brings detrimental side effects to old adults and the sustainability of society. The information and communication technologies have reshaped how people live among which the Internet has gained much popularity in the senior community. This study aims to explore the association between Internet use and depression in older adults.Methods: This study applied a representative national dataset (China Longitudinal Aging Social Survey, CLASS 2018) to examine by conducting regression analysis. Inspired by the social capital theory, we further examined the mediating role of general social networks (as a general concept) and specific networks (family and friend networks) in reducing depression. All calculations and analyses were conducted by STATA.Results: (a) Internet use significantly reduces depressive symptoms among Chinese older adults; (b) internet use enhances social network support for Chinese older adults; and (c) social networks in general and family networks and friend networks in specific all play a mediating role between internet use and depression symptoms.Conclusion: This work proved that internet use could reduce depression levels in older adults in China, and social networks, including family networks and friend networks, have a mediation role in the relationship between internet use and depression in older adults in China. Combined with the Chinese social context, we explained that the existence of an empty-nest elderly community in Chinese society and the emphasis on kinship in Chinese tradition may be the reasons. Based on the main findings, tailor-made suggestions for addressing depression issues among older adults were discussed.