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Interactive teaching is very popular in the field of education, especially in college preschool education teaching. In this study, the experimental performance of the evaluation system based on scientific emotion construction is analyzed by the machine learning algorithm. The experimental results show that the classification model realized by the machine learning algorithm is feasible and effective for the construction of an interactive design teaching evaluation system of preschool education in colleges and universities. The main research content of this experiment is for the teaching evaluation text, this paper intends to use NB, KNN, LR, and SVM algorithms as the meta-learning algorithm in the framework, the classification effect of the meta-classifier is ranked as SVM > LR > KNN > NB. From the above data comparison, the SVM meta-classifier in the algorithm in this paper has achieved the best classification effect. The SVM meta-classifier based on the algorithm in this paper performs well, and the classification accuracy rate reaches 92.1%. This shows that this model is very suitable for studying the. And the classification accuracy rate reaches 92.1%. This shows that this model is very suitable for studying the construction of interactive teaching evaluation systems under emotional education. • The experimental results show that the classification model realized by machine learning algorithm. • This shows that this model is very suitable for studying the construction of interactive teaching. • The main research content of this experiment is for the teaching evaluation text. • This paper intends to use NB, KNN, LR and SVM algorithms as the meta-learning algorithm. [ABSTRACT FROM AUTHOR]

The buyer-seller relationship in live streaming commerce is crucial especially in service failure situations, yet it has been underexplored. In this paper, we reconceptualized the relationship between consumers and sellers in live streaming commerce as "parasocial guanxi", which consists of ganqing (emotional attachment), xinren (trust) and reciprocal value. The effects of parasocial guanxi on consumer dissatisfaction and complaint intention in service failure situations were further investigated based on the expectation confirmation theory. Our study involved 425 valid questionnaires and employed the partial least squares structural equation method (PLS-SEM). The findings are mixed: on one side, parasocial guanxi acts as a buffer, mitigating dissatisfaction and complaint intention in the face of product or service problems and reducing the impact of disconfirmation on consumer dissatisfaction. On the other side, it may also increase the likelihood of dissatisfied consumers complaining. Furthermore, the buffering effect weakens as consumers make more frequent purchases. The findings have important theoretical and practical implications for understanding the buyer-seller relationship in live streaming commerce. • This paper reconceptualized the buyer-seller relationship in live streaming commerce as "parasocial guanxi". • The effects of parasocial guanxi in service failure situations were investigated. • Parasocial guanxi has a buffering effect in alleviating consumers' dissatisfaction and complaint intention. • Meanwhile, parasocial guanxi can increase the likelihood of dissatisfied consumers to complain. • The effects of parasocial guanxi vary across different demographic groups. [ABSTRACT FROM AUTHOR]

As social robots are increasingly designed with sophisticated simulations of human skills, it becomes essential to understand boundary conditions of people's engagement of them as mindful actors. The present paper reports a comprehensive secondary analysis of six studies (total N = 967) on the relationship between mind perception (evaluation of mental capacities) and mind ascription (explicit assignment of a mind-having status) when people consider a humanoid robot in different scenarios. Results indicate there is a context-independent, moderate link between perceptions of affective mental capacity (i.e., ability to feel emotion) and explicit mind ascription. We further found hints for a weak relationship between perceptions of reality-interaction capacity (i.e., sensory and agentic abilities) and decisions to ascribe mind that may need larger samples in order to be validated. Perceptions of social-moral capacities (i.e., evaluations of people and goals) were not a significant predictor of mind ascription. Overall, these findings highlight the pivotal role played by robots' display of affective engagement with the world for the acceptance of robots as mindful "beings" in human spheres. • Mind perception in robots does not mean people assign them a mindful being status. • Dimensionality of mind perception allows for various drivers of mind ascription. • Six studies uniformly show that perceived affective capacity is the primary correlate. • Perceptions of basic but not higher-order agency may be a secondary correlate. • Long-established position of denied mindfulness in robots is called into question. [ABSTRACT FROM AUTHOR]

This paper presents the evaluation of the synchronization of three emotional measurement methods (automatic facial expression recognition, self-report, electrodermal activity) and their agreement regarding learners’ emotions. Data were collected from 67 undergraduates enrolled at a North American University whom learned about a complex science topic while interacting with MetaTutor, a multi-agent computerized learning environment. Videos of learners’ facial expressions captured with a webcam were analyzed using automatic facial recognition software (FaceReader 5.0). Learners’ physiological arousal was recorded using Affectiva’s Q-Sensor 2.0 electrodermal activity measurement bracelet. Learners’ self-reported their experience of 19 different emotional states on five different occasions during the learning session, which were used as markers to synchronize data from FaceReader and Q-Sensor. We found a high agreement between the facial and self-report data (75.6%), but low levels of agreement between them and the Q-Sensor data, suggesting that a tightly coupled relationship does not always exist between emotional response components. [ABSTRACT FROM AUTHOR]

This paper examines how the COVID-19 pandemic associates with Twitch users' emotion, using natural language processing (NLP) as a method. Two comparable sets of text data were collected from Twitch internet relay chats (IRCs): one after the outbreak of the pandemic and another one before that. Positive emotion, negative emotion, and attitude to social interaction were tested by comparing the two text sets via a dictionary-based NLP program. Particularly regarding negative emotion, three negative emotions—anger, anxiety, and sadness—were measured given the nature of the pandemic. The results show that users' anger and anxiety significantly increased after the outbreak of the pandemic, while changes in sadness and positive emotion were not statistically significant. In terms of attitude to social interaction, users used significantly fewer "social" words after the outbreak of the pandemic than before. These findings were interpreted considering the nature of Twitch as a unique live mixed media platform, and how the COVID-19 pandemic is different from previous crisis events was discussed based on prior literature. • During the COVID-19 pandemic, Twitch users feel more anger and anxiety than before. • During the COVID-19 pandemic, Twitch users employ fewer social words than before. • Crisis events generally make people integrate, but the COVID-19 pandemic does not. [ABSTRACT FROM AUTHOR]

Techno-stressors stemming from the use of information technology (IT) have become a major source of stress in the modern workplace. While research shows that techno-stressors negatively affect employees' work attitudes and performance, little is known about their effects on employees' non-work lives. This research investigates the impact of techno-stressors on work-life balance and examines job self-efficacy as a buffer of this negative impact via the attenuating effect of emotional exhaustion. We conducted two survey studies. Study 1 collected data through a paper-and-pencil survey from 316 IT workers in a northern China IT company. Study 2 replicated and strengthened Study 1 by collecting longitudinal data through an online survey platform from 646 southern China female workers across diverse occupations. The results showed that techno-stressors negatively affected work-life balance, but job self-efficacy buffered employees from this negative effect by reducing their emotional exhaustion. Our findings extend understanding of techno-stressors' effects on individuals' non-work domains and shed light on how to cope with techno-stressors. • Techno-stressors are becoming major sources of stress in the modern workplace. • Techno-stressors negatively affect work-life balance. • Employees with high job self-efficacy suffer less from techno-stressors. • The buffering role of job self-efficacy works through lowering emotional exhaustion. [ABSTRACT FROM AUTHOR]

Problematic smartphone usage, associated with impaired daily functioning, has gained increased attention among researchers. However, extant research is focusing on adolescents and younger adults. This paper investigates smartphone usage among older adults, of which less is known. To do so, we conducted a cross-sectional survey of 154 smartphone users (60+ years) in Norway using structural equation modeling (PLS-SEM). We examined the contributing roles of loneliness, habit, social influence, emotional gain, fear of missing out, self-control, and problematic smartphone usage. We further investigated how older adults engage with their smartphones. Our findings suggest that older adults use smartphones for various social and non-social reasons and that social media and news reading are the most common use areas. Furthermore, we found a low prevalence of problematic smartphone usage among older adults. Whereas social influence and habit were strong predictors of smartphone usage among older adults, loneliness was not. Fear of missing out was not prevalent among older adults. As we expected, higher self-control was associated with lower problematic smartphone use. Finally, we discuss the contributions and implications of our findings. • Little is known about smartphone usage among older adults. • Habit and social influence affect their smartphone usage. Loneliness has less influence. • Older adults use their smartphones for both social and non-social purposes. • Social media and news reading are the most prominent use areas. • Problematic smartphone usage is not prevalent among older adults. [ABSTRACT FROM AUTHOR]

To understand rural–urban differences in stressors, this study compared the cognitive and emotional language in geolocated Twitter posts in the United States against survey-reported county-level trends from the Gallup-Sharecare Well-Being Index. Mentions of stress on Twitter can predict population-level trends in stress in both rural (R2=31.6%) and urban (R2=26.7%) communities. While mentions of poor health are limited to only rural areas with low socioeconomic status, higher emotional expression is associated with higher stress across all rural communities. Controlling for socioeconomic status, urban communities reporting higher stress are also more likely to discuss relationships on Twitter. The findings contribute to an understanding of how language use on social media acts as a barometer of the social and cultural differences between regions. The data and stress topics developed in this paper are publicly available and can be accessed at https://osf.io/af8ce/. • Methods to derive data-driven dictionaries from social media, validated against survey data on community-level outcomes. • Social media mentions of stress discuss appraisal, problems, and coping processes aligning with psychological theory. • Controlling for socioeconomic status, rural areas with higher stress are more emotionally expressive on Twitter. • Controlling for socioeconomic status, urban areas with higher stress are more likely to discuss relationships on Twitter. [ABSTRACT FROM AUTHOR]