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This paper investigates the effect of users' personality traits and emotions expressed through textual communications on YouTube to detect cyberbullying using a series of ensemble classifiers. Personality traits were determined using the Big Five model whereas emotions were based on Ekman's basic emotion theory. Annotated YouTube textual comments in English (N = 5152; i.e. 2576 number of bullying versus 2576 non-bullying instances) were used to detect cyberbullying incidents using several ensemble classifiers, including Random Forest and AdaBoost. Performance metrics revealed both personality traits and emotion to significantly improve the identification of cyberbullying presence, with accuracy and F-score values of more than 95%. Further fine-grained analysis revealed anger and openness to be more profound compared to other emotions and personalities. Further, neurotic individuals tend to be driven to cyberbullying by joy, disgust and fear. The findings show that personality and emotions play pertinent roles in cyberbullying, and the identification of specific traits and emotions can help in designing a more strategic intervention programme. [ABSTRACT FROM AUTHOR]

In this paper, we propose a reinforcement learning (RL) mechanism for social robots to select an action based on users' learning performance and social engagement. We applied this behavior selection mechanism to extend the emotion and memory model, which allows a robot to create a memory account of the user's emotional events and adapt its behavior based on the developed memory. We evaluated the model in a vocabulary-learning task at a school during a children's game involving robot interaction to see if the model results in maintaining engagement and improving vocabulary learning across the four different interaction sessions. Generally, we observed positive findings based on child vocabulary learning and sustaining social engagement during all sessions. Compared to the trends of a previous study, we observed a higher level of social engagement across sessions in terms of the duration of the user gaze toward the robot. For vocabulary retention, we saw similar trends in general but also showing high vocabulary retention across some sessions. The findings indicate the benefits of applying RL techniques that have a reward system based on multi-modal user signals or cues. [ABSTRACT FROM AUTHOR]

The characteristics of multiple brands involved and users' original content, facilitate a brand-new online platform's emergence, that is, a product-experience-shared community (PESC), being distinguished from the brand community that has been extensively studied. While popular nowadays, PESC still involves limited research. Mainly, why users volunteer to write a flow of content remains unclear. Our purpose is to analyse the effects of different motivational factors on different participation behaviours, which further influence continued generating intention in the PESC context. Employing the theory of Uses and Gratification (U&G) on a sample of 729 users and using Partial Least Squares-Structural Equation Modelling (PLS-SEM), the study deeply identified the participation behaviour into three levels, i.e. content consuming, reacting, and generating, as well as investigated their relations with motives (utilitarian, recreational, cultural and social motives) and continued generating behaviour. The results show that different participation behaviours are relevant to utilitarian, recreational, cultural, and/or social motives. Furthermore, users continued generating intention is affected by their complete satisfaction with their participation experience. This paper also gives theoretical and practical implications. [ABSTRACT FROM AUTHOR]

Research has proven that having high level of emotional intelligence (EI) can reduce the chance of getting mental illness. EI, and its component, can be improved with training, but currently the process is less flexible and very time-consuming. Machine learning (ML), on the other hand, can analyse huge amount of data to discover useful trends and patterns in shortest time possible. Despite the benefits, ML usage in EI training is scarce. In this paper, we studied 92 journal articles to discover the trend of the ML utilisation in the study of EI and its components. This survey aims to pave way for future studies that could lead to implementation of ML in EI training, and to rope in researchers in psychology and computer science to find possibilities of having a generic ML algorithm for every EI's components. Our findings show an increasing trend to apply ML on EI components, and Support Vector Machine and Neural Network are the two most popular ML algorithms used in those researches. We also found that social skill and empathy are the least exposed EI components to ML. Finally, we provide recommendations for future research direction of ML in EI domain, and EI in ML. [ABSTRACT FROM AUTHOR]

Cognitive cognitive load theory (CLT) has been conceived for improving instructional design practices. Although researched for many years, one open problem is a clear definition of its cognitive load types and their aggregation towards an index of overall cognitive load. In Ergonomics, the situation is different with plenty of research devoted to the development of robust constructs of mental workload (MWL). By drawing a parallel between CLT and MWL, as well as by integrating relevant theories and measurement techniques from these two fields, this paper is aimed at investigating the reliability, validity and sensitivity of three existing self-reporting mental workload measures when applied to long learning sessions, namely, the NASA Task Load index, the Workload Profile and the Rating Scale Mental Effort, in a typical university classroom. These measures were aimed at serving for the evaluation of two instructional conditions. Evidence suggests these selected measures are reliable and their moderate validity is in line with results obtained within Ergonomics. Additionally, an analysis of their sensitivity by employing the descriptive Harrell-Davis estimator suggests that the Workload Profile is more sensitive than the Nasa Task Load Index and the Rating Scale Mental Effort for long learning sessions. [ABSTRACT FROM AUTHOR]

Public-facing social media platforms, such as Facebook and WeChat, are increasingly being embedded into corporate processes and routines. The use of public social media by employees has aroused widespread interest among scholars in recent years. This study summarises published theories and models and proposes a causal-chain framework for research exploration into employee usage of public social media platforms by systematically analysing the antecedent variables, mediators, moderators, and outcome variables used in 59 quantitative papers. The representative theories include: Social Capital Theory, Job Demands-Resources Model, Boundary Theory, Media Synchronicity Theory, Social Cognitive Theory, Technology Acceptance Model, Self-Determination Theory, and Media Richness Theory. Historically, researchers have studied social media usage behaviours as antecedents, rather than social factors, with many focusing on outcome variables such as job performance and job satisfaction, while the impact of employee social media usage on physical and mental health is less studied. In terms of moderators, variables such as use behaviour, user characteristics and job characteristics receive most attention. With regards to mediators, social capital, job satisfaction, and work conflict are most significant. This study proposes future research directions for this field, including topics relating to platform attributes, social power, organisational culture, and employee health. [ABSTRACT FROM AUTHOR]