Computers in Human Behavior最新文献

Despite the wealth of literature vested in the association between social media use and vulnerability to fake news, it remains underexplored how and what kinds of social media usage contribute to fake news susceptibility. To fill this research gap, we draw upon the emergent scholarship of News-Finds-Me and propose a new conceptual model to examine fake news vulnerability and engagement in digital worlds. Drawing upon an online national sample in the US (N = 1014), results corroborated the prevalence of the News-Finds-Me perception, a social media-derived news attainment pattern that propels users to misconceive knowledgeability, over-depend on intimate peers and algorithms, and disengage from active news learning. Furthermore, evidence showed that News-Finds-Me perceptions make individuals more likely to believe and share fake news by creating a biased mentality that one is fake-news-proof while others are fake-news-impressionable. Such an asymmetric cognitive fallacy is called Third-Person Perception in literature. Our findings elucidate that the widely noted social media empowerment hypothesis might be double-sided. While social media can facilitate the dissemination and diversification of knowledge, they may also foster a sense of illusioned knowledgeability and overconfidence. This, in turn, could impede users from being adequately informed.

Social network data, such as Twitter/X, is of Big Social Data type. Big social data describes people's social behaviors and interactions. They have high business value for decision-making in organizations. However, because of the anonymous nature of social network users, their credibility is ambiguous. Credibility expresses the accuracy and value of big social data. Despite extensive research on the credibility of big social data, most methods have not paid sufficient attention to the important dimensions of their assessment, including user expertise based on topic, selecting social network features, and labeling them. Furthermore, these methods cannot manage the time, high volume, and speed of big social data. To address these issues, this paper presents a novel model for assessing the credibility of Twitter/X users by integrating Twitter/X with Google Scholar. The model automatically defines users' credibility labels using Google Scholar. Machine learning feature selection methods also select features that affect the credibility of Twitter/X users based on the topic. This study uses Google Scholar and the BerTopic algorithm for effective topic modeling on Twitter/X. The model considers unrelated data management, dynamic user credibility, and organizing activities based on the Big Data lifecycle. Finally, using Linear Regression, Support Vector Regression, K-Nearest Neighbor, Random Forest, Classification and Regression Trees algorithms, the model predicts the credibility of Twitter/X users and proves that it performed better than similar models through Classification and Regression Trees. In addition, the model is generalizable for all organizational purposes due to the integration of heterogeneous resources and feature selection methods.

Despite the recent growth in the integration of artificial intelligence (AI) into second/foreign language (L2) education, its emotional side has been ignored, to date. In order to address this gap, the present qualitative study aimed to explore the typology of emotions that Chinese English as a foreign language (EFL) teachers had experienced in their AI-based L2 classes. Drawing on the technology acceptance model (TAM) and control value theory (CVT), a sample of 50 EFL teachers were interviewed individually. The results of thematic analysis showed that Chinese EFL teachers, in this study, had experienced a variety of positive and negative emotions due to AI technologies. The most frequently experienced positive emotions were ‘enjoyment’, ‘excitement’, ‘motivation’, and ‘satisfaction’. Conversely, the participants had most repeatedly experienced negative emotions of ‘anxiety’, ‘stress’, ‘worry’, and ‘frustration’ in their AI-based classes. The findings are discussed in light of prior research and suggestions and implications are presented to EFL teachers and educators.

Many, if not most, mobile applications tend to elicit personal information from users to offer personalized services. However, users may not be comfortable with such intrusiveness and therefore hesitate to download or adopt a new app even when its use could be beneficial to their health and well-being. To overcome this friction and help users make informed decisions, we propose message interactivity as a solution. Guided by privacy calculus, the theory of interactive media effects (TIME), and the elaboration likelihood model (ELM), we conducted a 4-condition, pre-registered online between-subjects experiment (N = 305) to assess the effect of message interactivity on attitudes and behavioral intentions pertaining to information disclosure in mobile health apps. Data indicate a significant positive effect via three serial mediators, including perceived contingency, elaboration, and perceived benefits. Theoretical and practical implications are discussed.

Social virtual reality (SVR) attempts to allow for connections akin to face-to-face communication (Ftf). Yet, it is unclear whether the technology successfully mimics Ftf or more closely resembles other mediated communication channels. This study empirically compares SVR and other communication channels in terms of perceived social affordances, privacy, and trust through a between-subjects online survey (n = 743). Findings indicate that SVR and Ftf are similar regarding some perceived affordances (e.g., personalization) but differ with respect to others (e.g., anonymity, presence). Additionally, SVR is perceived as significantly distinct from one or multiple mediated channels for almost every measured social affordance. While SVR is seen as offering relatively greater levels of affordances that benefit interpersonal interaction, privacy concerns and a lack of trust in other users were found to often characterize the current user experience. This study provides theoretical insights for affordance research and practical implications for SVR designers.

Video games offer a unique platform for players to engage interactively with morally challenging topics and dilemmas. Despite the growing popularity of games that offer such content, there is a paucity of research on the player experiences and the specific game mechanics that facilitate moral decision making. To address this gap, this research identifies key game mechanics that support moral decision making through a comprehensive review of related literature and qualitative survey responses from players (n $=$ 30). The effects of these mechanics on players’ decision making processes and their overall impact on player experience were further explored through semi-structured, video-elicitation interviews (n $=$ 11). This research develops a theoretical framework based on the findings from these two exploratory studies, culminating in a set of design guidelines to inform the future development of moral decision making games.

Studies have shown that integrating Artificial Intelligence into robot-assisted language learning ushers in an immersive situation to establish empathy for communication competence and enjoyment. To investigate the usefulness of Artificial Intelligence-driven robots in learning English for Medical Purposes oral skills, this quasi-experimental study was conducted through an embedded mixed methods design in the 2024 academic year. One thousand and forty male (n = 398) and female (n = 642) students of different academic disciplines from the Isfahan University of Medical Sciences were included in the study and grouped under three categories. They were randomly assigned to control (n = 520) and experimental (n = 520) groups. In the qualitative phase, students’ communication enjoyment was debriefed through semi-structured interviews. Simultaneously, in the quantitative phase, once the participants watched simulcast lectures and joined in conversation with Artificial Intelligence-driven (non)-robots, their academic and professional oral skills were assessed. The collected data were the participants’ responses to the interviews and formative assessment of their progress and performance through Objective Structured Video Examinations and Mini-Clinical Evaluation Exercises. The interview results suggested that the participants had empathy in joining conversations with robots. The statistical analysis indicated that in performing role-play to teach oral skills to the Artificial Intelligence-driven robot, the participants achieved significantly greater communication competence than those who did role-play for virtual agents. Students of Medicine were great academic and professional achievers as they were significantly successful in establishing empathy and communication. The findings could open up further prospects for using Artificial Intelligence-driven robots in discipline-specific language learning contexts.