Chatbots are increasingly employed to provide basic medical advice and medication guidance among other health information services. Despite their utility, many users feel a disconnect due to perceived lack of empathy in these systems, leading to resistance toward using chatbot services. Prior research in human–computer interaction has highlighted the significant role of empathy in enhancing user experience, yet it remains uncertain whether cognitive empathy and emotional empathy differ in their impact. Informed by the Computers as Social Actors (CASA) theory, this study conducted a between-subjects experiment to investigate how different empathy types in health-assistant chatbots influence user satisfaction and usage intention. Additionally, it examined the mediating role of social presence and the moderating role of gender. The findings revealed that emotional empathy significantly improved user satisfaction and intention to use compared to cognitive empathy, with no notable gender differences. Social presence partially mediated the relationship between the chatbot’s empathy type and user outcomes. These results not only enhance our understanding of empathy’s mechanisms and effects in human–computer interactions but also offer crucial insights for developing effective communication strategies in health-assistant chatbots.

This study aims to explore how perceived influence of game streamers affects viewer video game engagement. We propose a research model based on an extended means-end chain framework and validate this model through a two-wave field survey with 465 respondents. The results show that perceived attractiveness, perceived competence, and perceived trustworthiness are important attributes of perceived influence of game streamers. Additionally, viewer value perception—encompassing perceived utility value, perceived hedonic value, and perceived symbolic value—which is derived from watching game streaming, partially mediates the relationship between perceived influence of game streamers and viewer video game engagement. These findings not only provide insights into the psychological mechanisms underlying the impact of game streamers on viewer behavioral intention but also contribute to a better understanding of the contemporary game streaming landscape.

Artificial intelligence (AI) is reshaping health information consumption on social media by tailoring content through algorithmic personalization. While user engagement with online health content has been widely studied, less is known about how technological features influence intention to use AI-powered social media for health information. Building on the affordance theory, this study proposes a research model linking algorithmic affordances with usage intention. An online survey with 1,051 users showed that content filtering was positively related to perceived information usefulness and willingness to rely on the platform. In contrast, automated decision-making and human-algorithm interplay were negatively related to willingness to rely on the platform and information usefulness, respectively. Both information usefulness and willingness to rely on the platform were positively related to intention to use these AI-driven platforms. The findings advance theoretical understanding of algorithmic affordances in health communication and provide practical guidance for improving user experiences on AI-driven platforms.

Analyzing and documenting human information behaviors in the context of global public health crises such as the COVID-19 pandemic are critical to informing crisis management. Drawing on the Elaboration Likelihood Model, this study investigates how three types of peripheral cues-content richness, emotional valence, and communication topic-are associated with COVID-19 information sharing on Twitter. We used computational methods, combining Latent Dirichlet Allocation topic modeling with psycholinguistic indicators obtained from the Linguistic Inquiry and Word Count dictionary to measure these concepts and built a research model to assess their effects on information sharing. Results showed that content richness was negatively associated with information sharing. Tweets with negative emotions received more user engagement, whereas tweets with positive emotions were less likely to be disseminated. Further, tweets mentioning advisories tended to receive more retweets than those mentioning support and news updates. More importantly, emotional valence moderated the relationship between communication topics and information sharing-tweets discussing news updates and support conveying positive sentiments led to more information sharing; tweets mentioning the impact of COVID-19 with negative emotions triggered more sharing. Finally, theoretical and practical implications of this study are discussed in the context of global public health communication.

While previous research suggests that smartphone overuse relates to users’ adverse health issues such as insomnia, nomophobia, and poor eyesight, few studies have explored the mediating role of such health issues in the relationship between smartphone overuse and academic performance. Guided by the Stimulus-Organism-Response (S-O-R) framework, this study develops a model to understand the relationships among students’ smartphone overuse, health issues (i.e. insomnia, nomophobia, and poor eyesight), and academic performance. Moreover, we introduce a moderating role of health information literacy in the relationship between smartphone overuse and health issues. To validate the model, we collect representative data through a large-scale field survey at a public university in China. 6,855 valid responses are retained for data analysis using a structural equation modelling technique. The main results are: (1) health issues – insomnia, nomophobia, and poor eyesight – partially mediate the relationship between smartphone overuse and students’ academic performance; (2) health information literacy can moderate the relationship between smartphone overuse and the health issues including insomnia and poor eyesight, while the relationship between smartphone overuse and nomophobia is not affected. Finally, we draw related theoretical and practical implications.

As the online dissemination of scholarly outputs gets faster and easier, altmetrics, social media based indices, have emerged alongside traditional metrics for research evaluation. In a two‐phase survey, we investigate scholars' familiarity and usage of traditional metrics and altmetrics. In this paper, we present the second phase with 448 participants. We found few traditional metrics, like the Journal Impact Factor and number of citations, are familiar to and often used by scholars for research evaluation. Among altmetrics, only views/downloads, readers, and followers are known to more than half the respondents. Unseen benefits and lack of time are hindrances to using metrics for the evaluation of research outputs. Although social media are well‐known, scholars prefer promoting their research by publishing in journals and attending conferences. We found social media usage, perceived ease of use and usefulness of altmetrics affect the usage of altmetrics. Findings suggest altmetrics have attracted attention in academia and could be considered complementary to traditional metrics. We acknowledge that due to the limited sample size, statistics and demographics in this study, findings cannot be said to be representative of the entire academic population worldwide. Future studies are needed that cover a wider range of academic disciplines around the world.

Recently, social media has become a potentially new way for scholarly journals to disseminate and evaluate research outputs. Scholarly journals have started promoting their research articles to a wide range of audiences via social media platforms. This article aims to investigate the social media presence of scholarly journals across disciplines. We extracted journals from Web of Science and searched for the social media presence of these journals on Facebook and Twitter. Relevant metrics and content relating to the journals' social media accounts were also crawled for data analysis. From our results, the social media presence of scholarly journals lies between 7.1% and 14.2% across disciplines; and it has shown a steady increase in the last decade. The popularity of scholarly journals on social media is distinct across disciplines. Further, we investigated whether social media metrics of journals can predict the Journal Impact Factor (JIF). We found that the number of followers and disciplines have significant effects on the JIF. In addition, a word co‐occurrence network analysis was also conducted to identify popular topics discussed by scholarly journals on social media platforms. Finally, we highlight challenges and issues faced in this study and discuss future research directions.