China’s national image is integral to its soft power and has crucial implications for its overseas interests. By explaining China’s national image with a country’s specific characteristics (e.g., ideology and economic level) and its relationship (e.g., trade relations) with China, previous studies have implicitly assumed that each country’s perception of China is independent of other countries’ views. This study challenges this assumption and argues that an international flow network exists in which one country’s perception of China can influence another’s. We perform a Granger causality analysis using sentiments expressed in English Twitter data from 51 countries between September 2011 and August 2021, which yields yearly international influence networks concerning China’s image. Temporal exponential random graph models show that gross domestic product (GDP) is positively correlated with a country’s ability to influence others and its susceptibility to external influences. A shared colonial history predicts the existence of influence. In addition, lower susceptibility to external influences is predicted by a greater number of high-level official visits with China, but not by measures of economic ties, including the proportions of foreign direct investment and imports from China in the country’s GDP.

This study investigates the mechanisms underlying the diffusion of risk information about genetically modified organisms (GMOs) on the Chinese social media platform Weibo. Drawing upon social contagion theory, we examine how endogenous and exogenous mechanisms shape users’ information-sharing behaviors. An analysis of 388,722 reposts from 2444 original GMO risk-related texts enabled the construction of a comprehensive sharing network, with computational text-mining techniques employed to detect users’ attitudes toward GMOs. To bridge the gap between descriptive and inferential network analysis, we employ a Shannon entropy-based approach to quantify the uncertainty and concentration of attitudinal differences and similarities among sharing and non-sharing dyads, providing an information-theoretic foundation for understanding positional and differential homophily. The entropy-based analysis reveals that information-sharing ties are characterized by lower entropy in attitude differences, indicating greater attitudinal alignment among sharing users, especially among GMO opponents. Building on these findings, the Exponential Random Graph Model (ERGM) further demonstrates that both endogenous network mechanisms (reciprocity, preferential attachment, and triadic closure) and positional homophily influence GMO risk information sharing and dissemination. A key finding is the presence of a differential homophily effect, where GMO opponents exhibit stronger homophilic tendencies than non-opponents. Despite the prevalence of homophily, this paper uncovers substantial cross-attitude interactions, challenging simplistic notions of echo chambers in GMO risk communication. By integrating entropy and ERGM analyses, this study advances a more nuanced, information-theoretic understanding of how digital platforms mediate public perceptions and debates surrounding controversial socio-scientific issues, offering valuable implications for developing effective risk communication strategies in increasingly polarized online spaces.

Communication research on scientific issues has traditionally relied on the deficit model, which posits that increasing scientific knowledge leads to public acceptance. However, this model's effectiveness is questioned due to inconclusive impacts of knowledge on acceptance. To address this, we propose a dual-process framework combining the deficit model (with scientific knowledge as a key predictor) and a normative opinion process model (where perceived majority opinion plays a crucial role) to predict people's risk/benefit perceptions and their support for genetic modification (GM). Using two national surveys in mainland China-Study 1 with 5145 laypeople and Study 2 with 12,268 scientists-we found positive and significant correlations between scientific knowledge or perceived majority opinion and GM support, mediated by risk/benefit perceptions. Importantly, the normative pathway-represented by perceived majority opinion-exerts a stronger direct and indirect impacts on GM support than scientific knowledge across both scientists and laypeople. Moreover, while the normative process shows a greater influence than the informative process on individuals' perceptions of both benefits and risks associated with GM, its prominence differs between scientists and laypeople depending on the types of perceptions-scientists are more sensitive to risk-related social norms, whereas laypeople are more concerned with norms related to benefits. The paper concludes with a discussion on the theoretical and practical implications of these findings.

The influence of social media platforms on content production has been widely discussed in journalism studies, yet there remains limited research on its specific impact on science communication. This Chinese case study explores how social media logic affects the practices of digital science communication within a leading start-up in the field. Using in-depth interviews and participant observation, the study examines how key components of social media logic-such as engagement metrics and the drive to avoid invisibility-shape content production. The findings reveal that these influences intertwine with other factors, including platform regulations, creating a complex environment for content creation. This research offers insights into the broader implications for science communication and highlights potential avenues for future inquiry.

Public support for genetic modification (GM) remains contested, shaped not only by individual risk-benefit evaluations but also by social contexts. Drawing on social influence theory, this study analyzes national survey data from China to examine how perceived majority opinion affects GM support directly and indirectly via perceived risks and benefits, and how these pathways are moderated by perceived important others' opinion and government controllability. Results show that these three perceptions constitute distinct yet interacting forms of normative, referent, and authority-based influence, collectively shaping attitudes toward GM. By embedding individual evaluations within broader social contexts, this study extends prior research focused on cognitive determinants and offers a more comprehensive, context-sensitive account of how public support for controversial technologies is socially constructed.

The rise of new media technologies has reshaped the landscape of science communication. There is little research on scientists' outreach participation and its possible predictors in different media contexts. Based on a national survey of 8,533 scientists in China, this study examined multiple direct and personal norm-mediated predictors of scientists' intentions to participate in public outreach via legacy media versus social media. Our findings revealed two consistent direct predictors (past outreach participation and personal norms) and two inconsistent direct predictors (descriptive norms and intrinsic rewards) that are significant only for participating via social media in the Chinese context. Moreover, our findings suggest a significant mediation effect of personal norms on the influence of various social norms (descriptive and subjective) and rewards (intrinsic and extrinsic) on Chinese scientists' intentions to participate in public outreach via media. The theoretical and practical implications of these findings are discussed.

In this study, we investigate public outreach participation among Chinese scientists through a multiple mediation model. Factors related to the Sagan effect—negative experiences and negative personal norms—are examined as potential predictors and/or mediators. Based on a national survey of 8,533 scientists, we validate the Sagan effect triggered by their negative experiences, which indirectly inhibit their outreach participation intentions through negative personal norms. Moreover, positive social norms and rewards play multilayered roles in mitigating the Sagan effect and improving such intentions. This study provides a more comprehensive examination of the underlying mechanisms behind scientists’ willingness to engage with the public.

硬科普是对前沿科学技术背后的原理进行清晰的介绍,其意义在于影响那些有影响力和传播力的人,从而帮助公众对这些科学技术有更好的理解,同时遏制谣言的传播. "硬科普"这个概念是比较新的,此前相关的报刊、文章里也有提到,但是各自表述、理解很不一样.我想和大家一起来探讨的是,我们怎么去理解"硬科普"这个概念,硬科普的科普效果又该如何进行评价.

研究通过对Web of Science中50篇论及科学传播中的政治正确的核心文献进行分析,总结梳理了已有研究对狭义和广义政治正确概念、政治正确相关的概念或理论工具的运用情况.在此基础上,研究对核心文献中的案例和相关结论进行定性元分析,得出科学传播实践中政治正确运行机制的理论模型.研究发现:狭义和广义政治正确涉及七种规范或观念,对应科学理性、政治忠诚、道德、多元文化和认知习惯五种关于科学传播内容的衡量标准.政治正确在科学传播中的运行伴随着科学理性与其他四种标准之间的冲突与磨合,集中表现为科学家和媒体的过度自我审查行为.通过这种自我审查的科学传播内容,本质上是科学理性向其他四种标准折衷后的结果.

科学资本是科学教育领域的学者们提出的学术概念,从社会不平等的视角关注青少年科学参与不足的问题.我们梳理了科学资本的概念演变过程,明确了科学资本的研究工具属性,并从科学传播的角度重新界定了科学资本的广义内涵,提出了面向一般公众的测量体系.科学资本这一概念体系兼容科学社会学和实证传播科学两种研究取向,以实证方式关注了科学资源分布不均与公众科学参与不平等的问题,为分众科学传播模式的探索与应用提供了理论依据.

The COVID-19 pandemic has accelerated the integration of algorithms in online platforms to facilitate people’s work and life. Algorithms are increasingly being utilized to tailor the selection and presentation of online content. Users’ awareness of algorithmic curation influences their ability to properly calibrate their reception of online content and interact with it accordingly. However, there has been a lack of research exploring the factors that contribute to users’ algorithmic awareness, especially in the roles of personality traits. In this study, we explore the influence of Big Five personality traits on internet users’ algorithmic awareness of online content and examine the mediating effect of previous knowledge and moderating effect of breadth of internet use in in China during the pandemic era. We adapted the 13-item Algorithmic Media Content Awareness Scale (AMCA-scale) to survey users’ algorithmic awareness of online content in four dimensions. Our data were collected using a survey of a random sample of internet users in China (n = 885). The results of this study supported the moderated mediation model of open-mindedness, previous knowledge, breadth of internet use, and algorithmic awareness. The breadth of internet use was found to be a negative moderator between previous knowledge and algorithmic awareness.

With digital infrastructures becoming the foundation of modern life and a shared lifestyle, the internet has become a popular leisure tool for middle-aged and elderly individuals. However, inappropriate use of the internet can jeopardize their health and quality of life, and excessive internet use by middle-aged and older adults is a cause for concern. This study found that middle-aged and older adults are vulnerable to excessive internet use. One predictor of excessive use is loneliness, but its effect is relatively limited. It is a mediating variable rather than the essential cause of excessive internet use by middle-aged and older adults. The effect of sensation seeking is a strong predictor of middle-aged and older adults’ excessive internet use, which means they have a strong desire to use the internet to satisfy their emotional needs, thus, resulting in excessive internet use. The social nature of digital infrastructure in a relational framework and the impact of the internet on different populations are likely more complex than we imagine and have the potential to cause many unintended effects.

本文在回顾数字素养的概念及相关研究的基础上,结合《提升全民数字素养与技能行动纲要》开发了一个基于数字化应用场景的数字素养框架,用于测量和评估我国民众的数字素养,为我国开展全民数字素养提升项目提供可参考的指标体系.而后从数字使用不平等的视角出发,基于中国综合社会调查(CGSS)(2017)的调查数据,关注数字社会中的弱势群体,揭示我国民众内部数字素养不平衡的分布特征,为面向不同人群开展针对性数字素养教育提供数据基础.

This century is marked by a burgeoning information society around the globe; accordingly, the adoption and use of information and communication technologies (ICTs) in general and the Internet in particular have been one of the most fruitful domains in the broader field of communication sciences. The observed persistent academic interest can, to a large extent, be attributed to the polymorphic nature of ICTs of various modalities, functioning as ICTs technology clusters and/or meta operating systems that accommodate numerous technologies, functions and applications. Beyond that, ICTs or Internet adoption is reflective of a social process of development, during which the informational mode of development is interwoven with other social systems and varies across diverse social settings. Most existing empirical research and theoretical approaches have overwhelmingly focused on the Internet adoption in developed economies, but in-depth investigations on the developing economies such as China are scarce, if any. Compared to most developed countries, China’s informatization-urbanization model marks a unique path of modernization, which further provides a huge opportunity to build momentum for the rapid and large-scale Internet adoption in urban China. In order to present a whole-range holistic portrait of China’s Internet development, the intrinsic logics and social outcomes of China’s informatization-urbanization model necessitate in-depth investigations.

The outbreak of the COVID -19 pandemic is accompanied by numerous rumors spreading on the social media platform, which seriously affects the stability of society and the safety of public. Existing quantitative analyses of COVID -19 related social media rumors only focus on single element of communication, such as content, while ignoring other basic elements of communication, including communicator, audience, and effect. Besides, compared with the real social media rumor data, the rumor data of these studies have distribution bias and lack of information. Therefore, we conduct a more comprehensive quantitative analysis on the communication of COVID -19 related social media rumors based on the Sina Weibo platform. Specifically, we first analyze the communication content of rumors, including the analysis of the topic, involved regions, event tendency and sentiment. Further, we investigate the users engaged in rumor communication and divide the users into three categories, namely, rumor posters, rumor spreaders, and rumor informers. We explore the basic attributes, topic preferences, individual sentiments, and self-network characteristics of the engaged users. Finally, we study the public opinion triggered by rumors, including the overall sentiment distribution, its correlation with topics, keywords and regions, as well as the evolution of sentiment. To conclude, this study first quantitatively analyzes COVID -19 related social media rumors from the perspective of different basic elements in communication. It provides a more comprehensive and profound understanding of COVID -19 related social media rumors and is of great value for both research and management of rumor in public emergencies. © 2021, Science Press. All right reserved.

Computational Social Science (CSS), aiming at utilizing computational methods to address social science problems, is a recent emerging and fast-developing field. The study of CSS is data-driven and significantly benefits from the availability of online user-generated contents and social networks, which contain rich text and network data for investigation. However, these large-scale and multi-modal data also present researchers with a great challenge: how to represent data effectively to mine the meanings we want in CSS? To explore the answer, we give a thorough review of data representations in CSS for both text and network. Specifically, we summarize existing representations into two schemes, namely symbol-based and embedding-based representations, and introduce a series of typical methods for each scheme. Afterwards, we present the applications of the above representations based on the investigation of more than 400 research articles from 6 top venues involved with CSS. From the statistics of these applications, we unearth the strength of each kind of representations and discover the tendency that embedding-based representations are emerging and obtaining increasing attention over the last decade. Finally, we discuss several key challenges and open issues for future directions. This survey aims to provide a deeper understanding and more advisable applications of data representations for CSS researchers.

以国内采用中西医结合成功进行新冠肺炎疫情诊疗为背景,探讨积极参与疫情危机的应对对中医社会形象建构所带来的变化.采用大数据技术辅助在线内容分析法,以媒介形象来化约社会形象,从科学、技术产品、文化、机构和从业者五个方面来概念化中西医媒介形象,对9 981篇有关中西医的报道进行文本数据挖掘与处理.研究发现,疫情前后中医或中西医结合议题的正面论调比例显著提升,而中西医各维度媒介形象在疫情前后的变化各不相同;媒体对中西医各维度媒介形象的建构路径在疫情暴发前后基本一致,但存在部分差异,疫情暴发前主要从时间纵深视角强调中医作为民族文化的传承与延续,疫情暴发后则型变为空间上的纵横,突出“世界的中医”的中医文化角色.总体而言,此次中西医结合抗疫,一定程度上巩固了中医的生存空间,提升了其社会形象.

社交媒体一方面加快了科学传播的速度,另一方面也加强了争议性科学议题(如疫苗、核电和转基因技术)在公众当中的讨论.在社交媒体等新信息传播技术赋予普通人更多表达和参与机会的今天,科学与民意的碰撞愈演愈烈,面对民众的强烈质疑,科学家常归咎于民众科学知识的缺乏.本研究通过一项全国性的抽样调查(N=1235),探讨不同新媒体平台的科学信息如何影响受众科学知识的建构,并通过经典O-S-O-R模型的引入,检验和解释新媒体使用、科学知识与态度之间的多元关联.数据结果很好地证明了O-S-O-R模型在争议科学技术议题当中的适用性.研究发现,关注微信公众号中的转基因信息,既可以对受众的态度产生直接影响,也可以透过科学知识这一变量产生间接的影响;关注微博信息,可以直接影响受众的购买行为,但无法透过科学知识产生间接影响.最后,针对知识及其影响,研究发现并非所有的科学知识内容都能对态度产生正向影响,特别是当受众越了解转基因发展现况的知识点,反而越倾向拒绝购买转基因相关产品.

本研究以转基因抗虫玉米的科学传播为例,探讨叙事对科学传播的第三人效果产生的影响.研究通过对207名在读高中生和部分大学生进行实验来进行.研究者采用了2010年方舟子发表在《中国青年报》上的《转基因玉米更有益健康》一文作为实验刺激物原型,通过对其中的叙事性语句进行编码和删减,形成了三篇叙事程度不同的文本供随机分组的三组被试阅读,并在阅读前后测试其对转基因的态度,后测部分则同时询问了其对所读文章可能产生的对其他人影响的看法.研究证实了科普信息传播中第三人效应的存在,但未发现叙事在改进说服效果方面的实质性作用.此外,研究发现,基于不同叙事程度的文本内容的阅读,也不影响人们在对信息影响评估时的第三人效应的强弱.本研究还对研究结果及其对争议性科学传播实践的启示做了讨论.