Challenging Social Media Threats using Collective Well-being Aware Recommendation Algorithms and an Educational Virtual Companion

Social media (SM) have become an integral part of our lives, expanding our inter-linking capabilities to new levels. There is plenty to be said about their positive effects. On the other hand however, some serious negative implications of SM have repeatedly been highlighted in recent years, pointing at various SM threats for society, and its teenagers in particular: from common issues (e.g. digital addiction and polarization) and manipulative influences of algorithms to teenager-specific issues (e.g. body stereotyping). The full impact of current SM platform design -- both at an individual and societal level -- asks for a comprehensive evaluation and conceptual improvement. We extend measures of Collective Well-Being (CWB) to SM communities. As users' relationships and interactions are a central component of CWB, education is crucial to improve CWB. We thus propose a framework based on an adaptive "social media virtual companion" for educating and supporting the entire students' community to interact with SM. The virtual companion will be powered by a Recommender System (CWB-RS) that will optimize a CWB metric instead of engagement or platform profit, which currently largely drives recommender systems thereby disregarding any societal collateral effect. CWB-RS will optimize CWB both in the short term, by balancing the level of SM threat the students are exposed to, as well as in the long term, by adopting an Intelligent Tutor System role and enabling adaptive and personalized sequencing of playful learning activities. This framework offers an initial step on understanding how to design SM systems and embedded educational interventions that favor a more healthy and positive society

Analysis and use of the emotional context with wearable devices for games and intelligent assistants

In this paper, we consider the use of wearable sensors for providing affect-based adaptation in Ambient Intelligence (AmI) systems. We begin with discussion of selected issues regarding the applications of affective computing techniques. We describe our experiments for affect change detection with a range of wearable devices, such as wristbands and the BITalino platform, and discuss an original software solution, which we developed for this purpose. Furthermore, as a test-bed application for our work, we selected computer games. We discuss the state-of-the-art in affect-based adaptation in games, described in terms of the so-called affective loop. We present our original proposal of a conceptual design framework for games, called the affective game design patterns. As a proof-of-concept realization of this approach, we discuss some original game prototypes, which we have developed, involving emotion-based control and adaptation. Finally, we comment on a software framework, that we have previously developed, for context-aware systems which uses human emotional contexts. This framework provides means for implementing adaptive systems using mobile devices with wearable sensors

Cyberbullying Victimization and Corresponding Distress in Women of Color

Online discrimination towards women and people of color has reached epidemic levels (Fox, Cruz, & Young Lee, 2015). Any woman or person of color who uses the internet runs the risk of attracting online users who would engage them in demeaning ways. As such, it is important that researchers are able to assess and understand these experiences and the possible effects on their well-being. In Chapter 1, I conducted a systematic review of cyberbullying measures. Although studies have documented the link between cyberbullying experiences and stress (i.e., psychological distress or perceived stress), there is a need to explore factors, such as intersectional identities, that may amplify this relationship. Using minority stress theory and intersectionality theory as a guiding framework, in Chapter 2, I examined three moderators of the relationship between cybervictimization experiences and stress—namely, attributing offenses to one’s race, gender, or both (i.e., being a woman of color). Data were collected from a sample of 275 adult women of color recruited from a large urban university in the southeast and through electronic listservs and social media platforms. Results from the study revelated that cybervictimization experiences were significant and positively related to both measures of stress. My primary hypotheses were partially supported. Attributions of cybervictimization to gender or race were associated with both psychological distress and perceived stress. These results held even after controlling for neuroticism. I did not, however, find that the interaction of race and gender attributions amplified the relationship. I discuss implications for future research and practical implications for practitioners

The knowledge domain of affective computing: a scientometric review

Purpose – The aim of this study is to investigate the bibliographical information about Affective Computing identifying advances, trends, major papers, connections, and areas of research. Design/methodology/approach – A scientometric analysis was applied using CiteSpace, of 5,078 references about Affective Computing imported from the Web-of-Science Core Collection, covering the period of 1991-2016. Findings – The most cited, creative, burts and central references are displayed by areas of research, using metrics and througout-time visualization. Research limitations/implications – Interpretation is limited to references retrieved from theWeb-of-Science Core Collection in the fields of management, psychology and marketing. Nevertheless, the richness of bibliographical data obtained, largely compensates this limitation. Practical implications – The study provides managers with a sound body of knowledge on Affective Computing, with which they can capture general public emotion in respect of their products and services, and on which they can base their marketing intelligence gathering, and strategic planning. Originality/value – The paper provides new opportunities for companies to enhance their capabilities in terms of customer relationships.info:eu-repo/semantics/acceptedVersio

Emotion Recognition by Video: A review

Video emotion recognition is an important branch of affective computing, and its solutions can be applied in different fields such as human-computer interaction (HCI) and intelligent medical treatment. Although the number of papers published in the field of emotion recognition is increasing, there are few comprehensive literature reviews covering related research on video emotion recognition. Therefore, this paper selects articles published from 2015 to 2023 to systematize the existing trends in video emotion recognition in related studies. In this paper, we first talk about two typical emotion models, then we talk about databases that are frequently utilized for video emotion recognition, including unimodal databases and multimodal databases. Next, we look at and classify the specific structure and performance of modern unimodal and multimodal video emotion recognition methods, talk about the benefits and drawbacks of each, and then we compare them in detail in the tables. Further, we sum up the primary difficulties right now looked by video emotion recognition undertakings and point out probably the most encouraging future headings, such as establishing an open benchmark database and better multimodal fusion strategys. The essential objective of this paper is to assist scholarly and modern scientists with keeping up to date with the most recent advances and new improvements in this speedy, high-influence field of video emotion recognition

Computer-Mediated Communication

This book is an anthology of present research trends in Computer-mediated Communications (CMC) from the point of view of different application scenarios. Four different scenarios are considered: telecommunication networks, smart health, education, and human-computer interaction. The possibilities of interaction introduced by CMC provide a powerful environment for collaborative human-to-human, computer-mediated interaction across the globe

Situation inference and context recognition for intelligent mobile sensing applications

The usage of smart devices is an integral element in our daily life. With the richness of data streaming from sensors embedded in these smart devices, the applications of ubiquitous computing are limitless for future intelligent systems. Situation inference is a non-trivial issue in the domain of ubiquitous computing research due to the challenges of mobile sensing in unrestricted environments. There are various advantages to having robust and intelligent situation inference from data streamed by mobile sensors. For instance, we would be able to gain a deeper understanding of human behaviours in certain situations via a mobile sensing paradigm. It can then be used to recommend resources or actions for enhanced cognitive augmentation, such as improved productivity and better human decision making. Sensor data can be streamed continuously from heterogeneous sources with different frequencies in a pervasive sensing environment (e.g., smart home). It is difficult and time-consuming to build a model that is capable of recognising multiple activities. These activities can be performed simultaneously with different granularities. We investigate the separability aspect of multiple activities in time-series data and develop OPTWIN as a technique to determine the optimal time window size to be used in a segmentation process. As a result, this novel technique reduces need for sensitivity analysis, which is an inherently time consuming task. To achieve an effective outcome, OPTWIN leverages multi-objective optimisation by minimising the impurity (the number of overlapped windows of human activity labels on one label space over time series data) while maximising class separability. The next issue is to effectively model and recognise multiple activities based on the user's contexts. Hence, an intelligent system should address the problem of multi-activity and context recognition prior to the situation inference process in mobile sensing applications. The performance of simultaneous recognition of human activities and contexts can be easily affected by the choices of modelling approaches to build an intelligent model. We investigate the associations of these activities and contexts at multiple levels of mobile sensing perspectives to reveal the dependency property in multi-context recognition problem. We design a Mobile Context Recognition System, which incorporates a Context-based Activity Recognition (CBAR) modelling approach to produce effective outcome from both multi-stage and multi-target inference processes to recognise human activities and their contexts simultaneously. Upon our empirical evaluation on real-world datasets, the CBAR modelling approach has significantly improved the overall accuracy of simultaneous inference on transportation mode and human activity of mobile users. The accuracy of activity and context recognition can also be influenced progressively by how reliable user annotations are. Essentially, reliable user annotation is required for activity and context recognition. These annotations are usually acquired during data capture in the world. We research the needs of reducing user burden effectively during mobile sensor data collection, through experience sampling of these annotations in-the-wild. To this end, we design CoAct-nnotate --- a technique that aims to improve the sampling of human activities and contexts by providing accurate annotation prediction and facilitates interactive user feedback acquisition for ubiquitous sensing. CoAct-nnotate incorporates a novel multi-view multi-instance learning mechanism to perform more accurate annotation prediction. It also includes a progressive learning process (i.e., model retraining based on co-training and active learning) to improve its predictive performance over time. Moving beyond context recognition of mobile users, human activities can be related to essential tasks that the users perform in daily life. Conversely, the boundaries between the types of tasks are inherently difficult to establish, as they can be defined differently from the individuals' perspectives. Consequently, we investigate the implication of contextual signals for user tasks in mobile sensing applications. To define the boundary of tasks and hence recognise them, we incorporate such situation inference process (i.e., task recognition) into the proposed Intelligent Task Recognition (ITR) framework to learn users' Cyber-Physical-Social activities from their mobile sensing data. By recognising the engaged tasks accurately at a given time via mobile sensing, an intelligent system can then offer proactive supports to its user to progress and complete their tasks. Finally, for robust and effective learning of mobile sensing data from heterogeneous sources (e.g., Internet-of-Things in a mobile crowdsensing scenario), we investigate the utility of sensor data in provisioning their storage and design QDaS --- an application agnostic framework for quality-driven data summarisation. This allows an effective data summarisation by performing density-based clustering on multivariate time series data from a selected source (i.e., data provider). Thus, the source selection process is determined by the measure of data quality. Nevertheless, this framework allows intelligent systems to retain comparable predictive results by its effective learning on the compact representations of mobile sensing data, while having a higher space saving ratio. This thesis contains novel contributions in terms of the techniques that can be employed for mobile situation inference and context recognition, especially in the domain of ubiquitous computing and intelligent assistive technologies. This research implements and extends the capabilities of machine learning techniques to solve real-world problems on multi-context recognition, mobile data summarisation and situation inference from mobile sensing. We firmly believe that the contributions in this research will help the future study to move forward in building more intelligent systems and applications

Technology-facilitated intimate partner violence in Italy: the role of education in preventing abusive behaviours in intimate relationships

openThis thesis develops around the phenomenon of technology-facilitated intimate partner violence, by offering a theoretical, conceptual, and contextual framework to better understand its prevalence, impact, causes, and consequences, with a specific focus on Italy. Technology-facilitated intimate partner violence (TFIPV) is a specific form of intimate partner violence perpetrated within the context of dating or an intimate relationship by current or former partners through the use of ICT means. Although TFIPV research has been increasing and developing in recent years, not much is known about the scope and magnitude of this issue, especially due to its nature that transcends temporal and geographical boundaries. After a general introduction and analysis of the phenomenon, firstly by placing it within the broader framework of online GBV, the second part of this research will focus specifically on the Italian national context. A literature review of some already existing Italian studies will be presented, to show the state-of-art of research on the issue. Finally, the third part will reflect on the role of education in preventing TFIPV, particularly by presenting some prevention programs, as well as a number of interviews with experts in the field, to stress the need to educate people, especially young people, on what constitutes a healthy and respectful relationship and what betrays a dysfunctional, problematic and toxic one.This thesis develops around the phenomenon of technology-facilitated intimate partner violence, by offering a theoretical, conceptual, and contextual framework to better understand its prevalence, impact, causes, and consequences, with a specific focus on Italy. Technology-facilitated intimate partner violence (TFIPV) is a specific form of intimate partner violence perpetrated within the context of dating or an intimate relationship by current or former partners through the use of ICT means. Although TFIPV research has been increasing and developing in recent years, not much is known about the scope and magnitude of this issue, especially due to its nature that transcends temporal and geographical boundaries. After a general introduction and analysis of the phenomenon, firstly by placing it within the broader framework of online GBV, the second part of this research will focus specifically on the Italian national context. A literature review of some already existing Italian studies will be presented, to show the state-of-art of research on the issue. Finally, the third part will reflect on the role of education in preventing TFIPV, particularly by presenting some prevention programs, as well as a number of interviews with experts in the field, to stress the need to educate people, especially young people, on what constitutes a healthy and respectful relationship and what betrays a dysfunctional, problematic and toxic one

College Students\u27 Social Media Uses and Affective Correlates

Given the high prevalence of mental health conditions such as depression and anxiety among college students, research on social media use, a salient feature of the modern college experience, is increasingly warranted. While research documents a link between negative psychological symptomology and social media use, few studies have examined what specific patterns of use may be more or less harmful than others. Therefore, the present study investigated whether specific types of social media use (socially oriented uses, information seeking uses, and entertainment uses) are more or less strongly associated with affective variables (depression, anxiety, positive affect, and negative affect). Utilizing four hierarchical linear regression models, we examined the degree to which the different types of social media use account for the variance in our four affective criterion variables. Contrary to our hypotheses, none of the three types of use were significant predictors of depression, anxiety, or positive affect (ps\u3e.05). However, both social and information seeking use were found to be significant predictors of negative affect, such that higher social use predicted lower negative affect (B= - .218, t(197) = -2.198, p \u3c .05) and higher information seeking use predicted higher negative affect (B= .240, t(197) = 2.706, p \u3c .01). These results suggest that while these three types of social media use may not have differential relationships with specific symptoms of psychopathology, social and information seeking use do seem related to more global experiences of negative affect. Further, while the link between information seeking and negative affect reflects findings in other research on news exposure, our findings on social use and lower negative affect were unexpected given prior documentation of a link between socially oriented uses and increased psychological distress and depression symptoms. Our findings suggest that the relationship between socially oriented use of social media and negative affect is likely more complex than previously suggested, with the possibility for both harmful and beneficial impacts of interacting with others online. Implications of these findings and directions for future research will be discussed