The social sharing of emotion (SSE) in online social networks: A case study in Live Journal

Social Sharing of Emotion (SSE) occurs when one person shares an emotional experience with another and is considered potentially beneficial. Though social sharing has been shown prevalent in interpersonal communication, research on its occurrence and communication structure in online social networks is lacking. Based on a content analysis of blog posts (n = 540) in a blog social network site (Live Journal), we assess the occurrence of social sharing in blog posts, characterize different types of online SSE, and present a theoretical model of online SSE. A large proportion of initiation expressions were found to conform to full SSE, with negative emotion posts outnumbering bivalent and positive posts. Full emotional SSE posts were found to prevail, compared to partial feelings or situation posts. Furthermore, affective feedback predominated to cognitive and provided emotional support, empathy and admiration. The study found evidence that the process of social sharing occurs in Live Journal, replicating some features of face to face SSE. Instead of a superficial view of online social sharing, our results support a prosocial and beneficial character to online SSE.     

人机交互中的智能体情感计算研究

机器的情感是通过融入具有情感能力的智能体实现的,虽然目前在人机交互领域已经有大量研究成果,但有关智能体情感计算方面的研究尚处起步阶段,深入开展这项研究对推动人机交互领域的发展具有重要的科学和应用价值。本文通过检索Scopus数据库选择有代表性的文献,重点关注情感在智能体和用户之间的双向流动,分别从智能体对用户的情绪感知和对用户情绪调节的角度开展分析总结。首先梳理了用户情绪的识别方法,即通过用户的表情、语音、姿态、生理信号和文本信息等多通道信息分析用户的情绪状态,归纳了情绪识别中的一些机器学习方法。其次从用户体验角度分析具有情绪表现力的智能体对用户的影响,总结了智能体的情绪生成和表现技术,指出智能体除了通过表情之外,还可以通过注视、姿态、头部运动和手势等非言语动作来表现情绪。并且梳理了典型的智能体情绪架构,举例说明了强化学习在智能体情绪设计中的作用。同时为了验证模型的准确性,比较了已有的情感评估手段和评价指标。最后指出智能体情感计算急需解决的问题。通过对现有研究的总结,智能体情感计算研究是一个很有前景的研究方向,希望本文能够为深入开展相关研究提供借鉴。     

The interactional effects of atmospherics and perceptual curiosity on emotions and online shopping intention

With increasing importance of online stores, a great number of studies have focused on extending our knowledge related to successful functional aspects increasing ease of use and usefulness. More recent studies have focused on identifying the effects produced by hedonic aspects of online store environment such as web atmospherics on emotional responses of customers. However, previous studies have been somewhat deficient in their investigation of studying diverse aspects of online consumer characteristics, which may have an impact on customer evaluation of atmospheric cues. Building on this research tradition, the present study addresses two critical issues. The present study adopting a well validated S–O–R framework tests the effect of atmospheric cues of online stores on the intervening affective emotional states of consumers, which have a subsequent impact on behavioral intention. Additionally, the model hypothesizes that perceptual curiosity (PC) moderates the relationships between atmospheric cues and shoppers’ emotional reactions. Structure equation model confirmed that online atmospherics such as graphics, colors, and links have an impact on customer emotions such as pleasure and arousal, both of which have subsequent effects on intention. The moderating effect of perceptual curiosity has also been supported. Theoretical and practical implications, limitations, and directions for future research are discussed in conclusion.     

Bringing theory to research on computer-mediated comforting communication

The current paper sought to advance the literature on computer-mediated emotional support by outlining a candidate theory of online comforting communication. We present a model that explicates the discursive, cognitive, and affective processes that function to reduce emotional distress and help improve one’s psychosocial well-being. We identify unique attributes of online social interaction, as compared to face-to-face (FtF) interaction, that may be especially useful for facilitating empathic and adaptive comforting communication. Additionally, we explain how unique features of computer-mediated comforting communication may work to facilitate the cognitive and affective processes that result in alleviation of emotional distress. Final sections of the paper advance research questions and hypotheses to guide future empirical research examining the efficacy of online emotional support.     

“Okay,My Rant is Over”: The Language of Emotion in Computer-Mediated Communication

Even when instructors take steps to mitigate conflict between students, online discussions are likely to be more emotional than face-to-face discussions, and student posts frequently bear characteristics of ranting. This paper uses a model from the field of psycholinguistics to identify linguistic features that writers use to communicate emotion in CMC to substitute for the nonverbal emotional cues that speakers and listeners rely on in face-to-face conversation. Our analysis of the online forum for a course called Presidential Election Rhetoric illustrates not only that students use linguistic features to express emotion but also that they transmit emotion to one another through the use of these features. Additionally, we suggest that students’ unfamiliarity expressing emotion subtly and accurately using linguistic features contributes to the quality of ranting in CMC. Finally, we recommend specific strategies to help students further hone their skills at expressing and perceiving emotion in CMC.     

Intragroup status and social presence in online fan groups

Low and high status member posts from online fan message board posts were examined. Low status members, as compared to high status members, were found to use more intimacy and immediacy social presence cues, including: praise for the group, self-disclosure, friendly and positive affective language, first person singular pronouns, and present tense verbs. Low status members were less likely than high status members to use articles, larger words, and discrepancy words. Lastly, low status members were rated as more likeable than high status members. The results suggest that low status members may strategically use social presence cues as a means of ingratiating themselves to the group.     

Employing both gender and emotion cues to enhance speaker identification performance in emotional talking environments

Speaker recognition performance in emotional talking environments is not as high as it is in neutral talking environments. This work focuses on proposing, implementing, and evaluating a new approach to enhance the performance in emotional talking environments. The new proposed approach is based on identifying the unknown speaker using both his/her gender and emotion cues. Both Hidden Markov Models (HMMs) and Suprasegmental Hidden Markov Models (SPHMMs) have been used as classifiers in this work. This approach has been tested on our collected emotional speech database which is composed of six emotions. The results of this work show that speaker identification performance based on using both gender and emotion cues is higher than that based on using gender cues only, emotion cues only, and neither gender nor emotion cues by 7.22 %, 4.45 %, and 19.56 %, respectively. This work also shows that the optimum speaker identification performance takes place when the classifiers are completely biased towards suprasegmental models and no impact of acoustic models in the emotional talking environments. The achieved average speaker identification performance based on the new proposed approach falls within 2.35 % of that obtained in subjective evaluation by human judges.     

Explaining personality and contextual differences in beneficial role of online versus offline social support: A moderated mediation model

With more than three billion “Netizens” worldwide, online social support obtained through social networking sites (SNS) has a pervasive influence on their users’ affective experiences. Social support generally fosters affective well-being, but such support can also threaten some recipients’ self-esteem that compromises their affective well-being. However, little is known of whether (a) this self-esteem threat varied by the mode (i.e., online vs. offline) of supportive interactions, and (b) such variations were explained by public self-consciousness across distinct modes of supportive interactions. A moderated mediation model was formulated to test these hypothesized personality and contextual differences using a quasi-experimental design. The results revealed that the mode of supportive interactions moderated the relationship between self-esteem and public self-consciousness, indicating that individuals higher in self-esteem are less likely to feel exposed to the potentially unfavorable evaluations in online (vs. offline) supportive interactions. Moreover, the results showed that the heightened levels of public self-consciousness explained the positive link between self-esteem and negative affect in offline but not online supportive interactions, providing further evidence that social support obtained through SNS is likely superior to that obtained through face-to-face interactions.     

A systematic review on affective computing: emotion models,databases, and recent advances

Affective computing conjoins the research topics of emotion recognition and sentiment analysis, and can be realized with unimodal or multimodal data, consisting primarily of physical information (e.g., text, audio, and visual) and physiological signals (e.g., EEG and ECG). Physical-based affect recognition caters to more researchers due to the availability of multiple public databases, but it is challenging to reveal one's inner emotion hidden purposefully from facial expressions, audio tones, body gestures, etc. Physiological signals can generate more precise and reliable emotional results; yet, the difficulty in acquiring these signals hinders their practical application. Besides, by fusing physical information and physiological signals, useful features of emotional states can be obtained to enhance the performance of affective computing models. While existing reviews focus on one specific aspect of affective computing, we provide a systematical survey of important components: emotion models, databases, and recent advances. Firstly, we introduce two typical emotion models followed by five kinds of commonly used databases for affective computing. Next, we survey and taxonomize state-of-the-art unimodal affect recognition and multimodal affective analysis in terms of their detailed architectures and performances. Finally, we discuss some critical aspects of affective computing and its applications and conclude this review by pointing out some of the most promising future directions, such as the establishment of benchmark database and fusion strategies. The overarching goal of this systematic review is to help academic and industrial researchers understand the recent advances as well as new developments in this fast-paced, high-impact domain.     

An emotion role mining approach based on multiview ensemble learning in social networks

Emotion is a status that combines people’s feelings, thoughts, and behaviors, and plays a crucial role in communication among people. Large studies suggest that human emotions can also be conveyed through online interactions. Previous studies have addressed the mechanism of emotional contagion; however, emotional contagion, through users of online social networks, has not yet been thoroughly researched. Therefore, in this study, initially, the definition of emotion roles, which may play an important role in emotional contagion, is introduced. On this basis, an emotion role mining approach based on multiview ensemble learning (ERM-ME) is proposed to detect emotion roles in social networks by fusing the information contained in different features. The ERM-ME approach includes three stages: detection of emotional communities, local fusion, and global fusion. First, ERM-ME divides emotional communities based on user emotional preferences. Then, emotional features are employed to train basic classifiers, which are then combined into meta-classifiers. Finally, an accuracy-based weighted voting scheme is used to integrate the results of meta-classifiers to achieve a more accurate and comprehensive classification. Experiments and evaluations are performed using Flickr and Microblog datasets to verify the practicability and effectiveness of the proposed method. Extensive experimental results show that the proposed approach outperforms alternative methods. The micro F-score is used as an evaluation indicator. Using the ERM-ME approach, the indicator is improved by approximately 1.09%–14.57% on Flickr and 5.19%–8.95% on Microblog, compared with Graph Convolutional Network, random forest, AdaBoost, bagging, and stacking.     

Modeling Multimodal Expression of User’s Affective Subjective Experience

With the growing importance of information technology in our everyday life, new types of applications are appearing that require the understanding of information in a broad sense. Information that includes affective and subjective content plays a major role not only in an individual’s cognitive processes but also in an individual’s interaction with others. We identify three key points to be considered when developing systems that capture affective information: embodiment (experiencing physical reality), dynamics (mapping experience and emotional state with its label) and adaptive interaction (conveying emotive response, responding to a recognized emotional state). We present two computational systems that implement those principles: MOUE (Model Of User Emotions) is an emotion recognition system that recognizes the user’s emotion from his/her facial expressions, and from it, adaptively builds semantic definitions of emotion concepts using the user’s feedback; MIKE (Multimedia Interactive Environment for Kansei communication) is an interactive adaptive system that, along with the user, co-evolves a language for communicating over subjective impressions.     

Some insights into the impact of affective information when delivering feedback to students

ABSTRACT

The relation between affect-driven feedback and engagement on a given task has been largely investigated. This relation can be used to make personalised instructional decisions and/or modify the affect content within the feedback. However, although it is generally assumed that providing encouraging feedback to students should help them adopt a state of flow, there are instances where those messages might result counterproductive. In this paper, we present a case study with 48 secondary school students using an Intelligent Tutoring System for arithmetical word problem solving. This system, which makes some common assumptions on how to relate affective state with performance, takes into account subjective (user's affective state) and objective information (previous problem performance) to decide the upcoming difficulty levels and the type of affective feedback to be delivered. Surprisingly, results revealed that feedback was more effective when no emotional content was included, and lead to the conclusion that purely instructional and concise help messages are more important than the emotional reinforcement contained therein. This finding shows that this is still an open issue. Different settings present different constraints generating related compounding factors that affect obtained results. This research confirms that new approaches are required to determine when, how and where affect-driven feedback is needed. Affect-driven feedback, engagement and their mutual relation have been largely investigated. Student's interactions combined with their emotional state can be used to make personalised instructional decisions and/or modify the affect content within the feedback, aiming to entice engagement on the task. However, although it is generally assumed that providing encouraging feedback to the students should help them adopt a state of flow, there are instances where those encouraging messages might result counterproductive. In this paper, we analyze these issues in terms of a case study with 48 secondary school students using an Intelligent Tutoring System for arithmetical word problem solving. This system, which makes some common assumptions on how to relate affective state with performance, takes into account subjective (user's affective state) and objective (previous problem performance) information to decide the difficulty level of the next exercise and the type of affective feedback to be delivered. Surprisingly, findings revealed that feedback was more effective when no emotional content was included in the messages, and lead to the conclusion that purely instructional and concise help messages are more important than the emotional reinforcement contained therein. This finding, which coincides with related work, shows that this is still an open issue. Different settings present different constraints and there are related compounding factors that affect obtained results, such as the message's contents and their target, how to measure the effect of the message on engagement through affective variables considering other issues involved, and to what extent engagement can be manipulated solely in terms of affective feedback. The contribution here is that this research confirms that new approaches are needed to determine when, how and where affect-driven feedback is needed. In particular, based on our previous experience in developing educational recommender systems, we suggest the combination of user-centred design methodologies with data mining methods to yield a more effective feedback.     

Misery rarely gets company: The influence of emotional bandwidth on supportive communication on Facebook

This study introduces the concept of emotional bandwidth to describe a communicator’s ability to use technological features to disclose personal affect online. Strategic use of emotional bandwidth was expected to correspond with interpersonal rewards, specifically the willingness of others to provide social support. Participants (N = 84) viewed hypothetical Facebook profiles that contained manipulated levels of emotional bandwidth and were asked how much support they would provide to the person in the profile. Participants who viewed profiles portraying high emotional bandwidth were less willing to provide social support; however, this finding was qualified by personal qualities. Females, people who perceived a sense of community, and people who had a preference for online social interaction indicated a greater willingness to provide support in the high emotional bandwidth condition. Implications for designing affective affordances in technologies and their psychological effects are discussed.     

Analyzing the emotional outcomes of the online search behavior with search engines

The affective component has been acknowledged as critical to understand information search behavior and user-computer interactions. There is a lack of studies that analyze the emotions that the user feels when searching for information about products with search engines. The present study analyzes the emotional outcomes of the online search process, taking into account the user’s (a) perceptions of success and effort exerted on the search process, (b) initial affective state, and (c) emotions felt during the search process. In addition, we identify profiles of online searchers based on the emotional outcomes of the search process, which allow us to differentiate the emotional processes and behavioral patterns that lead to such emotions. The results of the study stress the importance of the affective component of the online search behavior, given that these emotional outcomes are likely to influence all the subsequent actions that users perform on the Web.     

Self-efficacy as the moderator: Exploring driving factors of perceived social support for mainland Chinese students in Taiwan

More and more mainland Chinese college students study in Taiwan. Social support is one of the main factors helping them study and live happily in Taiwan. This study aims to investigate the relationships among self-efficacy, social identity, and perceived social support through online social networks in helping mainland Chinese college students improve their adaption ability of diversified campuses in Taiwan. We assume social identity affects positively perceived social support, and self-efficacy served as a moderator of the relationships between social identify and social support. A convenience sample of 366 mainland Chinese students from five universities in Taiwan was conducted to validate the hypotheses. The results revealed that affective and cognitive identification were significantly and positively related to perceived emotional, tangible and informational support. The moderator, self-efficacy, was shown to moderate the three relationships from affective identification to emotional, tangible and informational support. Also, it moderated the relationships from cognitive identification to tangible support, but not the relationships from cognitive identification to emotional and informational support. The suggestions for Taiwanese host universities to lighten the stress of study adaptation of mainland Chinese college students are provided.     

Linguistic-based emotion analysis and recognition for measuring consumer satisfaction: an application of affective computing

A growing body of research suggests that affective computing has many valuable applications in enterprise systems research and e-businesses. This paper explores affective computing techniques for a vital sub-area in enterprise systems—consumer satisfaction measurement. We propose a linguistic-based emotion analysis and recognition method for measuring consumer satisfaction. Using an annotated emotion corpus (Ren-CECps), we first present a general evaluation of customer satisfaction by comparing the linguistic characteristics of emotional expressions of positive and negative attitudes. The associations in four negative emotions are further investigated. After that, we build a fine-grained emotion recognition system based on machine learning algorithms for measuring customer satisfaction; it can detect and recognize multiple emotions using customers’ words or comments. The results indicate that blended emotion recognition is able to gain rich feedback data from customers, which can provide more appropriate follow-up for customer relationship management.     

Influence of computer feedback on attentional biases to emotional faces in children

We examined which type of corrective feedback in a computerized task produces an optimal balance between performance and emotional reactions in children. To that end, we conducted an emotional dot-probe task. We employed three types of corrective feedback (negative, positive, or mixed) along with a control, non-feedback condition. We tested the effect of feedback on: (i) task performance; (ii) immediate emotional reactions in terms of attentional preferences toward emotional faces (happy, sad, and angry); and (iii) self-reported affective experience after the task. Results showed that children committed more errors in the non-feedback group than in the mixed and negative feedback groups. Furthermore, the mixed feedback and the positive feedback groups showed an attentional bias away from sad faces. In contrast, the negative feedback group showed an attentional bias toward angry faces and felt unhappy after the task. Thus, the preferred type of feedback in children, in terms of better performance and a positive emotional reaction in a computerized task, is mixed feedback.     

Error-correcting output codes for multi-label emotion classification

Multi-modal affective data such as EEG and physiological signals is increasingly utilized to analyze of human emotional states. Due to the noise existed in collected affective data, however, the performance of emotion recognition is still not satisfied. In fact, the issue of emotion recognition can be regarded as channel coding, which focuses on reliable communication through noise channels. Using affective data and its label, the redundant codeword would be generated to correct signals noise and recover emotional label information. Therefore, we utilize multi-label output codes method to improve accuracy and robustness of multi-dimensional emotion recognition by training a redundant codeword model, which is the idea of error-correcting output codes. The experiment results on DEAP dataset show that the multi-label output codes method outperforms other traditional machine learning or pattern recognition methods for the prediction of emotional multi-labels.     

Emotional display behavior in different forms of Computer Mediated Communication

The primary purpose of the current study is to explore whether emotional-display behavior varies on different forms of CMC in a context of one-to-one online chat. Eighty college students (40 males and 40 females) participated in this experiment, and participants were randomly and equally assigned to one of the four different chat conditions (i.e., joint-view, no-view, view-in, and view-out), manipulating visibility (whether or not participants could see their chat partner) and monitorability (whether or not participants could be monitored by their chat partner). In an assigned chat condition, participants were asked to read, consecutively, two different emotional (happy and disgusting) stories typed by their chat partner. The emotional behavior participants displayed while reading the emotional stories was measured by self-reports and a facial-action coding system. Results reveal (1) no main effects for visibility and monitorability on the degree of social presence; (2) significant differences in the use of emotion-management techniques in response to happy and disgust emotions, respectively; and (3) less likelihood of a facial expression of disgust in the monitored conditions than in the unmonitored conditions. The results indicate that there are some differences between text-based chat and video-based chat in terms of emotional-display behavior. These findings make meaningful contributions to the ongoing debate regarding communication behavior in CMC.     

Analysing the role of a pedagogical agent in psychological and cognitive preparatory activities

There is a lack of studies that examine the role of a pedagogical agent on student development in a specific learning situation that involves psychological and cognitive preparatory activities in high school settings. We examined the effectiveness of pedagogical agent (APT) cognitive and affective feedback on learner motivation and well-being. We applied an experimental research design, involving 45 fourth-year high school students, divided in experimental and control groups (APT vs. human tutor). We performed a quantitative analysis to collect and analyse data of students using our APT. APT cognitive feedback had a positive effect on students' motivation for learning by encouraging students' proposals and initiatives and arousing students' interest in the topic. In addition, APT affective feedback fostered an appropriate emotional climate and creative environment for learning by enhancing students' curiosity, creativity and confidence for carrying out the activity, while reducing students' negative emotions such as boredom and anger. This study provided us useful insights about the affective (and cognitive) competencies that a virtual affective pedagogical agent needs to have in order to support students' mental and emotional health throughout a learning situation. Yet, further research is needed to consolidate these findings and make APT more adaptive to different learning situations.