With the widespread application of artificial intelligence in social and emotional companionship, understanding the intimate relationship between humans and AI has become a critical issue. Human-AI Attachment (HAIA) refers to a one-way, non-reciprocal emotional bond formed by individuals towards AI through direct interaction. This paper first sorts out the concept and characteristics of HAIA and proposes a three-stage developmental model, including functional expectation, emotional evaluation, and establishing representations. Human-AI attachment provides a framework for designing emotionally and socially capable AI, while also highlighting the risks of excessive reliance in socio-emotional contexts. Future research should further explore the conceptual structure, develop measurement tools, and examine the generational differences and evolutionary trends of HAIA.

In response to the pervasive threat of online rumors, rumor debunking technologies based on artificial intelligence (AI) have been widely applied. However, existing research has yet to clearly reveal the factors influencing individuals' willingness to adopt AI rumor debunking technologies and their underlying mechanisms. Based on the Task-Technology Fit (TTF) model and the Technology Acceptance Model (TAM), and incorporating the characteristics of AI rumor debunking, this study proposes and validates a theoretical model of the intention to use AI debunking technologies. The results reveal that the interdependence between humans and AI, coupled with the complexity of the task, jointly influence the task-technology fit. This, in turn, affects the credibility of AI and the intention to use it. Perceived usefulness plays a significant mediating role. Additionally, the credibility of rumors, as a set of information that conflicts with debunking, enhances individuals' trust in AI. Perceived insecurity can increase the impact of perceived usefulness and task-technology fit on AI credibility. Innovativeness can enhance the impact of perceived usefulness on usage intention.

Accurately predicting public panic sentiment on social media is crucial for proactive governance and crisis management. Current efforts on this problem face three main challenges: lack of finely annotated data hinders emotion prediction studies, unmodeled risk perception causes prediction inaccuracies, and insufficient interpretability of panic formation mechanisms limits mechanistic insight. We address these issues by proposing a Psychology-driven generative Agent framework (PsychoAgent) for explainable panic prediction based on emotion arousal theory. Specifically, we first construct a fine-grained panic emotion dataset (namely COPE) via human-AI (Large Language Models, LLMs) collaboration, combining scalable LLM-based labeling with human annotators to ensure accuracy for panic emotion and to mitigate biases from linguistic variations. Then, we construct PsychoAgent integrating cross-domain heterogeneous data grounded in psychological mechanisms to model risk perception and cognitive differences in emotion generation. To enhance interpretability, we design an LLM-based role-playing agent that simulates individual psychological chains through dedicatedly designed prompts. Experimental results on our annotated dataset show that PsychoAgent improves panic emotion prediction performance by 13% to 21% compared to baseline models. Furthermore, the explainability and generalization of our approach is validated. Crucially, this represents a paradigm shift from opaque “data-driven fitting” to transparent “role-based simulation with mechanistic interpretation” for panic emotion prediction during emergencies. Our implementation is publicly available at: https://github.com/supersonic0919/PsychoAgent.

This study investigates the provincial variations in self-expression among millions of Sina Weibo users within China, alongside the influence of ecological factors. Online social networking (OSN) platforms enable individuals to express themselves publicly through the use of personal Bios sections. In order to explore regional differences in self-expression, we constructed a self-expression index, grounded in the percentage of users who provided self-descriptions. To do this, we analyzed the Bios information of over 13 million users, obtaining provincial self-expression scores. Our results demonstrated that the self-expression index has face, convergent, and discriminant validity. Moreover, we discovered that several ecological factors exhibited a considerable impact on self-expression among Chinese Sina Weibo users. This research offers valuable insights into the regional variations of self-expression in China and the role of ecological factors in shaping such tendencies.

Face recognition strategies do not generalize across individuals. Many studies have reported robust cultural differences between West Europeans/North Americans and East Asians in eye movement strategies during face recognition. The social orientation hypothesis posits that individualistic vs. collectivistic (IND/COL) value systems, respectively defining West European/North American and East Asian societies, would be at the root of many cultural differences in visual perception. Whether social orientation is also responsible for such cultural contrast in face recognition remains to be clarified. To this aim, we conducted two experiments with West European/North American and Chinese observers. In Experiment 1, we probed the existence of a link between IND/COL social values and eye movements during face recognition, by using an IND/COL priming paradigm. In Experiment 2, we dissected the latter relationship in greater depth, by using two IND/COL questionnaires, including subdimensions to those concepts. In both studies, cultural differences in fixation patterns were revealed between West European/North American and East Asian observers. Priming IND/COL values did not modulate eye movement visual sampling strategies, and only specific subdimensions of the IND/COL questionnaires were associated with distinct eye-movement patterns. Altogether, we show that the typical contrast between IND/COL cannot fully account for cultural differences in eye movement strategies for face recognition. Cultural differences in eye movements for faces might originate from mechanisms distinct from social orientation.

PurposeThe purpose of this paper is to achieve effective governance of online rumors through the proposed rumor propagation model and immunization strategy.Design/methodology/approachThe paper leverages the agent-based modeling (ABM) method to model individuals from two aspects, behavior and attitude. Based on the analysis and research of online data, we propose a rumor propagation model, namely the Untouched view transmit removed-Susceptible hesitate agree disagree (Unite-Shad), and devise an immunization strategy, namely the Gravity Immunization Strategy (GIS). A graph-based framework, namely Pregel, is used to carry out the rumor propagation simulation experiments. Through the experiments, the rationality of the Unite-Shad and the effectiveness of the GIS are verified.FindingsThe study discovers that the inconsistency between human behaviors and attitudes in rumor propagation can be explained by the Unite-shad model. Besides, the GIS, which shows better performance in small-world networks than in scale-free networks, can effectively suppress rumor propagation in the early stage.Research limitations/implicationsThis paper provides an effective immunization strategy for rumor governance. Specifically, the Unite-Shad model reveals the mechanism of rumor propagation, and the GIS provides an effective governance method for selecting immune nodes.Originality/valueThe inconsistency of human behaviors and attitudes in real scenes is modeled in the Unite-Shad model. Combined with the model, the definition of diffusion domain is proposed and a novel immunization strategy, namely GIS, is designed, which is significant for the social governance of rumor propagation.

Medical artificial intelligence (AI) is important for future health care systems. Research on medical AI has examined people's reluctance to use medical AI from the knowledge, attitude, and behavioral levels in isolation using a variable-centered approach while overlooking the possibility that there are subpopulations of people who may differ in their combined level of knowledge, attitude and behavior. To address this gap in the literature, we adopt a person-centered approach employing latent profile analysis to consider people's medical AI objective knowledge, subjective knowledge, negative attitudes and behavioral intentions. Across two studies, we identified three distinct medical AI profiles that systemically varied according to people's trust in and perceived risk imposed by medical AI. Our results revealed new insights into the nature of people's reluctance to use medical AI and how individuals with different profiles may characteristically have distinct knowledge, attitudes and behaviors regarding medical AI.

BackgroundPrevious studies have shown that suicide reporting in mainstream media has a significant impact on suicidal behaviors (eg, irresponsible suicide reporting can trigger imitative suicide). Traditional mainstream media are increasingly using social media platforms to disseminate information on public-related topics, including health. However, there is little empirical research on how mainstream media portrays suicide on social media platforms and the quality of their coverage. ObjectiveThis study aims to explore the characteristics and quality of suicide reporting by mainstream publishers via social media in China. MethodsVia the application programming interface of the social media accounts of the top 10 Chinese mainstream publishers (eg, People’s Daily and Beijing News), we obtained 2366 social media posts reporting suicide. This study conducted content analysis to demonstrate the characteristics and quality of the suicide reporting. According to the World Health Organization (WHO) guidelines, we assessed the quality of suicide reporting by indicators of harmful information and helpful information. ResultsChinese mainstream publishers most frequently reported on suicides stated to be associated with conflict on their social media (eg, 24.47% [446/1823] of family conflicts and 16.18% [295/1823] of emotional frustration). Compared with the suicides of youth (730/1446, 50.48%) and urban populations (1454/1588, 91.56%), social media underreported suicides in older adults (118/1446, 8.16%) and rural residents (134/1588, 8.44%). Harmful reporting practices were common (eg, 54.61% [1292/2366] of the reports contained suicide-related words in the headline and 49.54% [1172/2366] disclosed images of people who died by suicide). Helpful reporting practices were very limited (eg, 0.08% [2/2366] of reports provided direct information about support programs). ConclusionsThe suicide reporting of mainstream publishers on social media in China broadly had low adherence to the WHO guidelines. Considering the tremendous information dissemination power of social media platforms, we suggest developing national suicide reporting guidelines that apply to social media. By effectively playing their separate roles, we believe that social media practitioners, health institutions, social organizations, and the general public can endeavor to promote responsible suicide reporting in the Chinese social media environment.

Organ donation provides a life-saving opportunity for patients with organ failure. China, like most countries, is faced with organ shortages. Understanding public opinion regarding organ donation in China is critical to ensure an increased donation rate. Our study explored public concerns and attitudes toward organ donation, factors involved, and how the public pays attention to organ donation. Sixteen million users' public information (i.e. gender, age, and geographic information) and posts from January 2017 to December 2017 were collected from Weibo, a social media platform. Of these, 1755 posts related to organ donation were included in the analysis. We categorized the posts and coded the users' attitudes toward organ donation and the associations between the demographics. The most popular posts mentioning organ donation were "publicly expressing the willingness to donate organs." Furthermore, 87.62% of posts exhibited a positive attitude toward organ donation, whereas only 7.44% exhibited a negative attitude. Most positive posts were "saluting the organ donors," and most negative posts involved "fear of the family's passive medical decision." There was no significant gender difference in the users' attitudes, but older people generally had a more negative attitude. Users with negative attitudes mainly distrust the medical system and are worried that the donated organs may be used in improper trading. Social media may be an important channel for promoting organ donation activities, and it is important to popularize scientific knowledge related to organ donation in order to eliminate the public's misunderstanding of organ donation and transplantation.

Health rumors not only incite unnecessary fears and skepticism, but may also cause individuals to refuse effective remedy and thus delay their treatment. Studies have found that health literacy may help the public identify the falsity of health rumors and avoid their negative impact. However, whether other types of literacy work in helping people disbelieve health rumors is still unknown. With a national survey in China (N = 1646), our study examined the effect of science literacy on rumor belief and further analyzed the moderating role of self-efficacy of science literacy in their relationship. Hierarchical regression analysis showed that science literacy significantly decreased the likelihood of people believing in health rumors, and moderator analysis showed that self-efficacy of science literacy plays a moderating role in this relationship; such that the relationship between science literacy and health rumor belief would be weakened if one′s self-efficacy of science literacy was low. This finding reveals that during campaigns to combat health rumors, improving and enhancing the self-efficacy of people′s science literacy is an effective way to prevent them from believing in health rumors. Our study highlights the benefits of science education in public health and the improvement of public science literacy.

In the world of social media, people are free to choose names based on their preferences, which may potentially reflect certain levels of uniqueness. In this study, we have attempted to explore the possibility of applying the ecological theory of individualism/collectivism in the context of social media. We, thus, examined provincial variations in the uniqueness of nicknames among more than 13 million Sina Weibo users. Initially, the nickname uniqueness indicator was set at the provincial level. It was found that the uniqueness of nicknames was the highest in provinces with temperate climates, for example Guangdong, and the lowest in provinces with demanding climate, such as Ningxia. Regression analysis results partially supported that inhabitants in provinces with temperate climate were more likely to use unique nicknames on social media compared to those from harsh climate. This finding is significant in terms of ecology.

A high quality of life (QoL), an individual's subjective assessment of overall life condition, has been shown to have a protective effect against negative behaviors. However, whether QoL protects people from the harmful impact of health rumors is still unknown. In this study, a national survey in China (n = 3633) was conducted to explore the relationship between health rumor belief (HRB) and QoL, which includes physical, psychological, social, and environmental domains. The results show that people with a poor perception of their physical health are more likely to believe health rumors. Additionally, those who had better self-reported satisfaction in social relationships were more susceptible to health rumors. Furthermore, women and older adults showed a greater belief in health rumors. This study expands upon our understanding of how people with different QoL levels interact with false health-related information. Based on health-rumor-susceptible groups, several essential online and offline strategies to govern health rumors are also proposed.

本研究以全球人工智能产业的专利数据为研究样本,以技术创新主体、技术创新领域为分析内容,结合方差分析、动态网络分析及相关软件,从动态、多维关系视角和宏观、微观层面全面揭示了人工智能产业技术创新网络的演化态势.研究发现:人工智能领域的技术创新主体、技术创新领域均随时间不断演化与更迭.但技术创新主体比技术创新领域的变化更为剧烈和频繁,技术创新领域呈现出稳中有变的趋势,且领域之间融合态势不断加强.研究表明,了解技术创新领域的演进路径,可提高相关组织、机构的竞争效率,以便在市场竞争中获得较高的战略优势.

目的 调查受访者对卫生健康领域热点事件的情绪与态度,分析其倾向、特点及影响因素,为卫生健康管理部门开展针对性的舆情引导提供对策建议.方法 围绕公众对5个卫生健康热点事件的心理距离、总体感受和责任认知等进行问卷调查.通过描述性分析和logistic回归分析公众对事件感受及影响因素.结果 在5个卫生健康热点事件中,29.760的受访者认为距离感最近的事件是“取消药品加成”,平均得分(3.72±0.93)分;30.35％的受访者认为距离感最远的事件是“安徽男子‘丢肾’事件”,平均得分(4.44±0.96)分.33.44％的受访者感受负面程度最高的为“潍坊纱布门事件”,平均得分(3.38±0.69)分,36.88％的受访者感受正面程度最高的为“取消药品加成”,平均得分(4.87±0.99)分.在四个负面事件的责任归属问题上,公众认为医院或医生是最大责任方.年龄、文化程度、月收入和患慢性病情况是受访者对于“潍坊纱布门事件”产生较强负面感受的影响因素(P＜0.05).结论 卫生健康领域舆情引导中,应把握事件背后的情绪结构,应有群体针对性,引导公众多元归因、理性归因.

BACKGROUND:High-quality medical resources are in high demand worldwide, and the application of artificial intelligence (AI) in medical care may help alleviate the crisis related to this shortage. The development of the medical AI industry depends to a certain extent on whether industry experts have a comprehensive understanding of the public's views on medical AI. Currently, the opinions of the general public on this matter remain unclear.OBJECTIVE:The purpose of this study is to explore the public perception of AI in medical care through a content analysis of social media data, including specific topics that the public is concerned about; public attitudes toward AI in medical care and the reasons for them; and public opinion on whether AI can replace human doctors.METHODS:Through an application programming interface, we collected a data set from the Sina Weibo platform comprising more than 16 million users throughout China by crawling all public posts from January to December 2017. Based on this data set, we identified 2315 posts related to AI in medical care and classified them through content analysis.RESULTS:Among the 2315 identified posts, we found three types of AI topics discussed on the platform: (1) technology and application (n=987, 42.63%), (2) industry development (n=706, 30.50%), and (3) impact on society (n=622, 26.87%). Out of 956 posts where public attitudes were expressed, 59.4% (n=568), 34.4% (n=329), and 6.2% (n=59) of the posts expressed positive, neutral, and negative attitudes, respectively. The immaturity of AI technology (27/59, 46%) and a distrust of related companies (n=15, 25%) were the two main reasons for the negative attitudes. Across 200 posts that mentioned public attitudes toward replacing human doctors with AI, 47.5% (n=95) and 32.5% (n=65) of the posts expressed that AI would completely or partially replace human doctors, respectively. In comparison, 20.0% (n=40) of the posts expressed that AI would not replace human doctors.CONCLUSIONS:Our findings indicate that people are most concerned about AI technology and applications. Generally, the majority of people held positive attitudes and believed that AI doctors would completely or partially replace human ones. Compared with previous studies on medical doctors, the general public has a more positive attitude toward medical AI. Lack of trust in AI and the absence of the humanistic care factor are essential reasons why some people still have a negative attitude toward medical AI. We suggest that practitioners may need to pay more attention to promoting the credibility of technology companies and meeting patients' emotional needs instead of focusing merely on technical issues.

Geographical psychology aims to study the spatial distribution of psychological phenomenon at different levels of geographical analysis and their relations to macro-level important societal outcomes. The geographical perspective provides a new way of understanding interactions between humankind psychological processes and distal macro-environments. Studies have identified the spatial organizations of a wide range of psychological constructs, including (but not limited among) personality, individualism/collectivism, cultural tightness-looseness, and well-being; these variations have been plotted over a range of geographical units (e.g., neighborhoods, cities, states, and countries) and have been linked to a broad array of political, economic, social, public health, and other social consequences. Future research should employ multi-level analysis, taking advantage of more deliberated causality test methods and big data techniques, to further examine the emerging and evolving mechanisms of geographical differences in psychological phenomena.

The rapid development of information technology has provided a hotbed for rumors, and the study of the characteristics of rumors propagation is essential for taking intervention measures. This paper proposes the NF-S (LIR) model which considers users' behavior and attributes separately at the individual level. The data are collected in the form of a questionnaire, and two sets of experiments are conducted using simulation methods to verify the rationality of the model and predict the effects of different interventions in different scenarios. The mechanism of rumor spreading is studied in our work, and the effects of government interventions are testified in the experiments.

With the advantages of convenient access and free parking, urban dockless shared bikes are favored by the public. However, the irregular flow of dockless shared bikes poses a challenge for the research of flow pattern. In this paper, the flow characteristics of dockless shared bikes are expounded through the analysis of the time series location data of ofo and mobike shared bikes in Beijing. Based on the analysis, a model called DestiFlow is proposed to describe the spatio-temporal flow of urban dockless shared bikes based on points of interest (POIs) clustering. The results show that the DestiFlow model can find the aggregation areas of dockless shared bikes and describe the structural characteristics of the flow network. Our model can not only predict the demand for dockless shared bikes, but also help to grasp the mobility characteristics of citizens and improve the urban traffic management system.

In 2019, the development plan for the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) was officially announced. This will be a useful boost to cooperation among Hong Kong, Macao, and the Pan-Pearl River Delta, and requires positive social mentality as a driving force. Analyzing the netizens' expression on social media has become the most important method to comprehend social mentality. Therefore, this study explores the social mentality of GBA netizens through social cognition, social perception, and social development efficiency. Specifically, this study utilizes big data methods to analyze how netizens in the GBA describe their social mentality.