Passive Users Research Articles

Exploring Earable-Based Passive User Authentication via Interpretable In-Ear Breathing Biometrics

Exploring Earable-Based Passive User Authentication via Interpretable In-Ear Breathing Biometrics

The Evolution of Identity and Access Management: Integrating Biometric and Behavioral Authentication

This article explores the transformative role of biometric and behavioral authentication in the evolving landscape of Identity and Access Management (IAM). As traditional password-based systems increasingly fail to meet the challenges posed by sophisticated cyber threats, these advanced authentication methods offer a promising solution. The article provides a comprehensive overview of physical and behavioral biometrics, examining their integration into multi-factor authentication systems and their capacity for continuous, passive user verification. It delves into the significant security benefits these technologies offer, including enhanced resistance to phishing, social engineering, and insider threats, while also highlighting the improvements in user experience and convenience. The article critically analyzes the challenges and considerations associated with implementing biometric and behavioral authentication, including privacy concerns, data security, accuracy issues, and scalability. Furthermore, it explores future directions and research opportunities in the field, such as advancements in biometric technologies, the application of machine learning and AI in behavioral analysis, and the development of context-aware authentication systems. By synthesizing current research and industry trends, this article provides valuable insights for cybersecurity professionals, IT decision-makers, and researchers interested in the future of secure and user-friendly authentication methods. It underscores the potential of biometric and behavioral authentication to significantly enhance organizational security postures while improving user satisfaction in an increasingly digital world.

Utilisation of the Hip Disability and Knee Injury Osteoarthritis Outcome Score in physiotherapy following total hip and knee arthroplasty: a cross-sectional survey

Objective To explore the frequency of administration and the usage of the Hip Disability and Knee injury Osteoarthritis Outcome Scores (HOOS/KOOS) and their Physical function Short forms (HOOS-PS/KOOS-PS) by physiotherapists after total hip and knee arthroplasties (THA/TKA). Design A cross-sectional study using an open online survey. Setting Primary care physiotherapy practices affiliated with the Dutch Association for Quality in Physiotherapy. Participants Physiotherapists with experience treating over five patients with a THA or TKA within the past 5 years. Results One hundred and sixty-six physiotherapists completed the survey (median age: 40.0 years, female: 34%, median experience: 15.0 years). Of those, 32 did not administer the HOOS(-PS) or KOOS(-PS) (‘non-users’), 41 administered only due to organisational requirements or guideline recommendations (‘passive users’) and 93 actively used them for individual patient treatment purposes (‘active users’). ‘Treatment evaluation’, ‘diagnosis’, and ‘prognosis’ were most often reported as potential reasons to actively use the HOOS(-PS) or KOOS(-PS) for individual treatment purposes. Determinants associated with active use of the HOOS(-PS) or KOOS(-PS) appeared to be fewer years of experience as a physiotherapist, a larger treatment volume of THA/TKA, a younger age, and higher attitude scores regarding PROM use. Conclusions Most responding physiotherapists administer the HOOS(-PS) or KOOS(-PS), but their use for individual treatment is limited. Active users appear to be less experienced, younger, treat larger volumes of THA/TKA, and possess a more positive attitude towards using patient-reported outcome measures.

대학생의 생성형 AI 이용경험에 관한 현상학적 연구

Objectives This study aims to explore the direction of Generative AI literacy education through the exploration of college students' experiences using Generative AI. Methods For this purpose, written and face-to-face in-depth interviews were conducted with 12 university stu-dents (5 male students and 7 female students) and analyzed by applying Colaizzi's phenomenological method, which consists of seven steps to explore the nature of common experiences rather than the individual participants. Results As a result of analyzing the interview data, 5 semantic themes and 14 sub-themes were derived under the essence of the experience called ‘co-evolution’. The five semantic themes consist of ‘getting to know new media’, ‘useful but dangerous existence’, ‘sharing roles with Generative AI’, ‘adapting to changes in the learning environment’, and ‘seeking a life that coexists with Generative AI’. The 14 semantic themes consisted of ‘matching up with Generative AI’, ‘creating my own method of using prompts’, ‘limitation of inanimate object’, ‘repeating unnecessary learning activities’, ‘incomplete learning assistant’, ‘my own tutor’, ‘reducing learning time’, ‘setting my own scope of use’, ‘another team member’, ‘passive new media user’, ‘recognition of reality and acceptance of new media’, ‘improving the competence to use Generative AI’, ‘selecting Generative AI according to the purpose of use’, and ‘competition with the human-specific domain’. Conclusions The discussion points on the direction of Generative AI literacy education for university students are as follows. First, it provides education in which prompts can be effectively input. Second, it provides guidelines so that the Generative AI can be used as an auxiliary tool rather than the main tool for learning. Third, it provides education on the characteristics of the Generative AI. Fourth, it enhances education on usage ethics.

Hikikomori in the Middle East: The role of problematic gaming, social media use, and loneliness

Hikikomori, a form of severe social withdrawal has been found to be associated with behavioral addictions such as gaming addiction and problematic social media use (PSMU). Since literature related to hikikomori is lacking in the Middle East, this study aimed to determine whether there are significant differences in hikikomori-like traits between two different types of social media users and gamers and investigate loneliness as a potential mediator between hikikomori and the two types of problematic behaviors. A cross-sectional, correlational design was employed, collecting a final sample of 220 participants residing in Middle Eastern countries using a mixture of convenience and snowball sampling. Results showed that passive social media users demonstrated significantly greater hikikomori-like traits compared to active users. Furthermore, hikikomori-like traits exhibited significant positive associations with problematic gaming, PSMU, and loneliness. Two separate mediation analyses, the first with PSMU and a second with problematic gaming as predictors, revealed that loneliness acted as a significant mediator between both these problematic behaviors and hikikomori-like traits.

Social Media Use: A Cognitive Dissonance Analysis of Heavy Users and Engagement Dynamics

Abstract Purpose – This research investigates the correlation between the utilization of social networking sites, user engagement, and cognitive dissonance. It explores the mediating influence of e-compulsive buying behavior (ECBB) in the connection between SNS use, user engagement, and cognitive dissonance, while also examining the moderating impact of self-esteem on SNS use, user engagement, and ECBB. Methodology – Utilizing purposive sampling, this study collected cross-sectional data from 232 female participants actively engaged in social networking sites (SNSs) and who had made 4-5 online purchases during the last six months. Data were gathered through an electronically administered questionnaire. Descriptive data analysis was conducted using SPSS version 21, while SmartPLS version 4.0 was employed to test the proposed hypotheses. Findings – The findings indicate a significant association between the utilization of social networking sites (SNSs) and user engagement with cognitive dissonance and ECBB. Additionally, it was observed that self-esteem played a moderating role in the relationship between SNS use, user engagement, ECBB, and cognitive dissonance, revealing a heightened impact of cognitive dissonance at lower levels of self-esteem. Practical Implications – These results carry implications for implementing interventions aimed at mitigating the adverse consequences associated with social networking site (SNSs) use, thereby fostering favorable mental health outcomes within the community. Moreover, this study offers valuable insights for managers seeking to enhance their social media strategies by analyzing the varied impacts of social media content and understanding the dynamics between posts to engage both active and passive users effectively. Originality/Value – This study contributes to the limited body of empirical research by examining the correlation between social networking site (SNSs) use, user engagement, and cognitive dissonance through the lens of ECBB. Additionally, in response to recent calls from various empirical studies in this field, the inclusion of self-esteem as a moderating variable predicting cognitive dissonance enhances the novelty of this research.

Context-aware prediction of active and passive user engagement: Evidence from a large online social platform

The success of online social platforms hinges on their ability to predict and understand user behavior at scale. Here, we present data suggesting that context-aware modeling approaches may offer a holistic yet lightweight and potentially privacy-preserving representation of user engagement on online social platforms. Leveraging deep LSTM neural networks to analyze more than 100 million Snapchat sessions from almost 80.000 users, we demonstrate that patterns of active and passive use are predictable from past behavior (R2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${R^2}$$\\end{document}=0.345) and that the integration of context features substantially improves predictive performance compared to the behavioral baseline model (R2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${R^2}$$\\end{document}=0.522). Features related to smartphone connectivity status, location, temporal context, and weather were found to capture non-redundant variance in user engagement relative to features derived from histories of in-app behaviors. Further, we show that a large proportion of variance can be accounted for with minimal behavioral histories if momentary context is considered (R2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${R^2}$$\\end{document}=0.442). These results indicate the potential of context-aware approaches for making models more efficient and privacy-preserving by reducing the need for long data histories. Finally, we employ model explainability techniques to glean preliminary insights into the underlying behavioral mechanisms. Our findings are consistent with the notion of context-contingent, habit-driven patterns of active and passive use, highlighting the value of contextualized representations of user behavior for predicting user engagement on online social platforms.

Social Media, Sentiments and Political Discourse ? An Exploratory Study of the 2021 Canadian Federal Election

Social media are widely used for online political discourse. Opinions shared on social media have different sentiments associated with them. Given the very high adoption rates of Twitter (now X) among adults, those who share their opinions on Twitter (X) not only represent a sizable segment of the society, but also influence (through emotion contagion) an even larger segment who are passive (non-contributing) users of the platform. Further, the discourse that is initiated on Twitter (X) typically spreads to other more traditional media. As a result, Twitter (X) is influential, which makes it useful to understand the factors related to the sentiments expressed in tweets. Such understanding can help policymakers to take actions that align with public needs and priorities. This research focuses on identifying the drivers (keywords) of sentiments associated with political discourse on Twitter (X). We also explore virality, i.e., how much a message (the tweet) spreads, and the relationship between sentiments and virality. Finally, we explore whether the clustering of tweets among sentiment and virality groups can improve the potential of social media content for predicting election results. Sentiment Analysis of 764,000 tweets related to the 2021 Canadian Federal election was followed by text clustering to identify sentiment-driving topics. We found some keywords predominantly present within a positive or negative sentiment that are suggestive of entities or ideas to invest in or mitigate by political decision makers. We were also able to find partial evidence for “negativity bias” by detecting a negative relationship between sentiment (positivity) and virality (number of retweets). Finally, we demonstrated that high positivity on the political discourse does not reflect election outcomes and examining Twitter (X) content in more neutral groups can improve predictive power. Our findings have implications for political decision makers and social media analytics researchers.

Exploiting Screen-Touch Trajectory for Passive User Authentication in Industrial Internet of Things Systems

Exploiting Screen-Touch Trajectory for Passive User Authentication in Industrial Internet of Things Systems

Social Media Use and Internalizing Symptoms in Clinical and Community Adolescent Samples

In response to widespread concerns about social media's influence on adolescent mental health, most research has studied adolescents from the general population, overlooking clinical groups. To synthesize, quantify, and compare evidence on the association between social media use and internalizing symptoms in adolescent clinical and community samples. Peer-reviewed publications from MEDLINE, Web of Science, PsycInfo, and Scopus (initially reviewed in May 2022 and updated in October 2023) and preprints from Europe PubMed Central (February 2023) published in English between 2007 and 2023. Two blinded reviewers initially identified 14 211 cross-sectional and longitudinal studies quantifying the association between social media use and internalizing symptoms, excluding experimental studies and randomized clinical trials. PRISMA and MOOSE guidelines were followed, pooling data using a random-effects model and robust variance estimation. The quality of evidence was assessed using the Quality of Survey Studies in Psychology Checklist. Articles were included if they reported at least 1 quantitative measure of social media use (time spent, active vs passive use, activity, content, user perception, and other) and internalizing symptoms (anxiety, depression, or both). The 143 studies reviewed included 1 094 890 adolescents and 886 effect sizes, 11% of which examined clinical samples. In these samples, a positive and significant meta-correlation was found between social media use and internalizing symptoms, both for time spent (n = 2893; r, 0.08; 95% CI, 0.01 to 0.15; P = .03; I2, 57.83) and user engagement (n = 859; r, 0.12; 95% CI, 0.09 to 0.15; P = .002; I2, 82.67). These associations mirrored those in community samples. The findings in this study highlight a lack of research on clinical populations, a critical gap considering public concerns about the increase in adolescent mental health symptoms at clinical levels. This paucity of evidence not only restricts the generalizability of existing research but also hinders our ability to evaluate and compare the link between social media use and mental health in clinical vs nonclinical populations.

DISCRETE-CONTINUOUS PROCESSES AS THE MATHEMATICAL BASIS OF THE RELIABILITY OF THE BEHAVIOR OF SUBSYSTEMS OF THE "DRIVER-VEHICLE-ROAD-ENVIRONMENT" SYSTEM

The work is devoted to the study of the reliability of the behavior of subsystems of the driver-car-road-environment system. According to the definition of a number of researchers, traffic safety is a complex and system of rules, measures and means that ensure safe traffic conditions, which are aimed at protecting and preserving the life and health of active and passive road users, as well as protecting and preserving the environment and property. Therefore, the question of studying all aspects of the operation of the "Driver-car-road-environment" system becomes acute. The general approach to the formation of reliability models of the B-A-D-S system consists in the fact that this model is described in a formalized form from the standpoint of the reliability of the interaction of subsystems in the process of functioning and reflects the degree of influence of the reliability of individual subsystems on the reliability of the system as a whole. Usually, reliability indicators of system elements (failure intensity) are determined on the basis of the laws of distribution of time of failure-free operation, and the concept of "failure" is clearly defined for each subsystem. Reliability models formed in terms of element failures are the main type of system reliability models. It follows that the set of combinations of possible states of elements determines the set of possible states of the system in general, which is called the space of system states (control theory). When writing the work, the research of domestic and foreign authors on the issues of the basics of reliability and risk assessment was studied. The purpose of the publication is to study approaches to ensuring the reliability of complex systems. It was determined that for all mechanical elements (car parts, road elements) the most suitable failure model is the DM-distribution, while the estimation of the coefficient of variation can be in the range from 0.3 to 0.7

Differences between Active and Passive Social Media Usage on Well-Being in Reference to Adolescents

Today in this fast pacing world and in the age of digital era, internet is easily accessible to every human being. Many researchers in the past have explored that social media is directly or indirectly affecting the physiological and mental health of an individual. Active users are the ones who are actively involved in the social media sites whereas the passive users are the ones who are not actively participating yet observing the other’s profiles and posts. The aim of this paper is to identify the significant difference between genders (male and female) on well-being in reference to adolescents and significant difference between active and passive social media users on the well-being in reference to adolescents. The total sample size taken was N=120, which consists of 60 males and 60 females of both the groups that is active and passive social media users. Random purposive sampling was used for collecting data. The research design for the study is 2x2 factorial design. The independent variables of the study are Active and passive social media users whereas the dependent variables for the study are dimensions of well-being given by Carol Ryff i.e. autonomy, environmental mastery, personal growth, positive relations, purpose in life, self-growth. Self-made online survey was done for collecting the sample for active and passive social media users and Ryff’s well-being scale was used in the study. The analysis of data was done by using descriptive analysis and ANOVA. The findings of the study suggest that there was a significant difference between active and passive social media users on well-being also there was a significant difference between genders (male and female) on well-being in reference to adolescents.

Adaptive Federated Learning for Automatic Modulation Classification Under Class and Noise Imbalance

The ability to rapidly understand and label the radio spectrum in an autonomous way is key for monitoring spectrum interference, spectrum utilization efficiency, protecting passive users, monitoring and enforcing compliance with regulations, detecting faulty radios, dynamic spectrum access, opportunistic mesh networking, and numerous NextG regulatory and defense applications. We consider the problem of automatic modulation classification (AMC) by a distributed network of wireless sensors that monitor the spectrum for signal transmissions of interest over a large deployment area. Each sensor receives signals under a specific channel condition depending on its location and trains an individual model of a deep neural network (DNN) accordingly to classify signals. To improve modulation classification accuracy, we consider federated learning (FL) where each individual sensor shares its trained model with a centralized controller, which, after aggregation, initializes its model for the next round of training. Without exchanging any spectrum data (such as in cooperative spectrum sensing), this process is repeated over time. A common DNN is built across the net- work while preserving the privacy associated with signals collected at different locations. Given their distributed nature, the statistics of the data across these sensors are likely to differ significantly. We propose the use of adaptive federated learning for AMC. Specifically, we use FEDADAM -an algorithm using Adam for server optimization – and ex- amine how it compares to the FEDAVG algorithm -one of the standard FL algorithms, which averages client parameters after some local iterations, in particular in challenging scenarios that include class imbalance and/or noise-level imbalance across the network. Our extensive numerical studies over 11 standard modulation classes corroborate the merit of adaptive FL, outperforming its standard alternatives in various challenging cases and for various network sizes.

The screening of cannabis addiction using machine learning, MoCA, and anxiety/depression tests

A new cannabis addiction screening approach is developed based on the use of Machine Learning (ML) alongside with psychological and cognitive assessment tests. The Hospital Anxiety and Depression (HAD) test is used to determine the Absence of Anxiety and Depression “No Disorder” or the presence of “Anxiety Only”, “Depression Only”, and both “Anxiety and Depression”. These features are considered to evaluate the psychological effects of cannabis use. In addition to that, the Montreal Cognitive Assessment (MoCA) test is chosen as a feature to assess the cognitive state of the participants. Also, the Age of First Cannabis Use (AFCU) is included as a feature to incorporate participants with no prior cannabis use alongside with those who have used cannabis before. The portion of participants with prior experience consuming cannabis comprises a subset containing passive users and a subset of users that have developed cannabis addiction. This 6-month study was conducted in Marrakech, Morocco in a center affiliated to the National Association of Drug-Risk Reduction of Morocco known as RdR-Maroc with the participation of 146 subjects. The participants in this study are grouped into two groups of 73 participants each. Both case and control groups were clinically assessed by a professional. In this paper, the classical statistical data analysis for the AFCU (M=15.6; SD=2.4), MoCA (M=25.0; SD=2.4), and HAD (58 cases with no disorder, 88 cases with disorder) failed to separate between the two classes. Also, the feature selection for modeling is performed using Spearman correlation, Kendall correlation, and the Odds Ratio (OR). These statistics showed that the selected features were associated to addiction except for “Anxiety Only”. Moreover, ML models such as Logistic Regression (LR), Support Vector Machine (SVM), Artificial Neural Network (ANN), kth-Nearest Neighbor (KNN), and Decision Tree (DT) were used to classify participants into two categories (Addict vs. Non-Addict) based on the selected features mentioned earlier. The Receiver Operating Characteristic (ROC) curve is used to overview the quality of the models and showed that they all captured the nonlinearities underlined in the data. On the other hand, the Area Under the Curve (AUC) allowed the ranking of these models and indicated that the SVM model outperformed all other models with an AUC of 0.97. After that, the SVM model was compared to the Cannabis Use Disorder (CUD) based screening method issued by the fifth edition of the Diagnostic and Statistical Manual of mental disorders (DSM-5). The SVM model's accuracy of 96.6 % outperformed the CUD questionnaires accuracy of 87.7 %. The validity of the proposed approach was investigated where the SVM model presented high validity characteristics (sensitivity=1, specificity=0.93) compared to the CUD questionnaire (sensitivity=0.75, specificity=1). Hence, the proposed approach provided satisfactory results compared to current adopted CUD based screening test. Our findings expand current research on the development of simple and powerful tools for cannabis addiction screening with the purpose of early detection in order to establish preventive and therapeutic strategies that could potentially mitigate the prevalence of cannabis addiction.

Passive social networking sites use and disordered eating behaviors in adolescents: The roles of upward social comparison and body dissatisfaction and its sex differences

Previous research has indicated a link between social networking site (SNS) use and eating behaviors, but the underlying mechanisms have not been well explored. This study investigated the role of upward social comparison and body dissatisfaction in the relationship between passive SNS use and disordered eating (DE) behaviors, as well as sex differences. A total of 744 middle school students (51.6% female, Mage = 12.87 years, SD = 0.68) completed self-report questionnaires regarding passive SNS use, upward social comparison, body dissatisfaction, and DE behaviors. Results revealed that: 1) girls reported significantly higher levels of passive SNS use, body dissatisfaction, and DE behaviors than boys; 2) passive SNS use was a significant predictor of DE behaviors both sexes; 3) body dissatisfaction mediated the relationship between passive SNS use and DE behaviors for girls; 4) the chain mediating effects of upward social comparison and body dissatisfaction between passive SNS use and DE behaviors were found only among adolescent girls; 5) Compared to boys, body dissatisfaction in girls is more significantly related to DE behaviors, and upward social comparison is more closely related to body dissatisfaction in girls. These findings suggest that passive SNS use, rather than general SNS use, was related to body dissatisfaction and eating concerns. Interventions targeting specific passive SNS users may be an effective avenue for the prevention and management of body-related concerns and disordered eating behavior in adolescent girls.

An Intersectional Lifecourse Lens and Participatory Methods as the Foundations for Co-Designing with and for Minoritised Older Adults

The design of digital technologies for older adults is often premised on deficit models of ageing that position older people as a homogenous group and as passive users of technology, with an overwhelming focus on meeting practical needs in older age. In response, a growing number of scholars in HCI and Science and Technology Studies (STS) are engaging with processes of co-design that situate older adults as experts in their own lives and as central to the design process. These scholars highlight how an essential first phase of co-design is understanding and foregrounding the lifeworlds, experiences and expertise of older adults. This paper responds to these calls, alongside the lack of consideration of minoritised older adults in co-design. It draws on the empirical findings from the first phase of the Connecting Through Culture As We Age project, which places twenty minoritised older adults who identify as disabled, and/or racially and/or socio-economically minoritised, at the centre of a digital innovation process. Through a case study approach, we focus on two of the minoritised older adults involved, to demonstrate the value of bringing together participatory methods with an interdisciplinary lifecourse lens. We highlight the power of this approach for understanding minoritised older adults' relationships with technology, as shaped by experiences across the lifecourse, for building relationships, and ensuring their agency and voice underpin the co-design process.

Do laundry when the sun shines: Factors that promote loadshifting in Dutch households with solar panels

The installation of solar panels by residential households is vital for the energy transition. However, the rapid uptake of solar panels by households leads to congestion in the electricity grid. Specifically, when the sun shines, these solar panels simultaneously produce a lot of electricity that is fed into the grid, which is inefficient and can destabilize the grid. Consequently, it is better if self-produced solar energy is directly consumed when the sun is shining. Loadshifting involves shifting energy use (e.g., doing laundry) to periods in which energy is produced. This necessitates behavior change within the household, and it is not yet well understood why people struggle to loadshift. To assess the individualistic and contextual factors influencing laundry loadshifting behavior in the Netherlands, we conducted a survey among 283 Dutch households with solar panels. The survey builds on a framework that integrates aspects of the theory of planned behavior and social practice theory. The framework comprises eight factors (sufficiency attitude, user beliefs, know-how, monitoring skills, habits, hassle, practical knowledge provided, and feedback provision by system design), which are quantitatively measured and analyzed. We used multiple regression analysis to explore the collected responses. Results show the relevance of monitoring skills, strong habits, passive user beliefs and practical knowledge for laundry loadshifting behavior. Findings highlight that instead of asking people to adjust to technologies, technologies should support behavior change and understand its intricacies and connections to its broader context. Additionally, it is important to strengthen households' beliefs regarding their active role in the energy system.

Unveiling the silent majority: stance detection and characterization of passive users on social media using collaborative filtering and graph convolutional networks

Social Media (SM) has become a popular medium for individuals to share their opinions on various topics, including politics, social issues, and daily affairs. During controversial events such as political elections, active users often proclaim their stance and try to persuade others to support them. However, disparities in participation levels can lead to misperceptions and cause analysts to misjudge the support for each side. For example, current models usually rely on content production and overlook a vast majority of civically engaged users who passively consume information. These “silent users” can significantly impact the democratic process despite being less vocal. Accounting for the stances of this silent majority is critical to improving our reliance on SM to understand and measure social phenomena. Thus, this study proposes and evaluates a new approach for silent users’ stance prediction based on collaborative filtering and Graph Convolutional Networks, which exploits multiple relationships between users and topics. Furthermore, our method allows us to describe users with different stances and online behaviors. We demonstrate its validity using real-world datasets from two related political events. Specifically, we examine user attitudes leading to the Chilean constitutional referendums in 2020 and 2022 through extensive Twitter datasets. In both datasets, our model outperforms the baselines by over 9% at the edge- and the user level. Thus, our method offers an improvement in effectively quantifying the support and creating a multidimensional understanding of social discussions on SM platforms, especially during polarizing events.

Patterns of Social Media Use across Age Groups during the COVID-19 Pandemic: A Study across Four Countries

The aim of this study was to examine patterns of social media use across age groups in four countries (Norway, USA, UK, and Australia) two years after the COVID-19 pandemic outbreak, and whether types of use and time spent using social media was related to health worries. A cross-sectional online survey was completed by 1578 adult participants. The data were analysed with one-way analyses of variance and a linear regression analysis. Younger people spent more time on and were more likely to be passive users of social media than older people. Motives for social media use, and perceived effects of using social media, varied by participants’ age. Passive social media use and more time spent using social media were related to higher levels of health worries. Thus, an age perspective is relevant for understanding patterns of social media use, and different types of social media use appear to be differently related to health worries.

Educational Robots, Emotion Recognition and ASD: New Horizon in Special Education

Technology has always represented the key to human progress. It is believed that the use of supportive technological mediators can facilitate teaching/learning processes and enable everyone to learn how to critically manage technology without being its slave or passive user while contributing to the collective well-being. Educational robotics is a new frontier for learning that can offer numerous benefits to students. The use of robots can offer the possibility of creating inclusive educational settings in which all students, regardless of their abilities or disabilities, can participate meaningfully. The article proposes an analysis of the evidence obtained from a systematic literature review with reference to general educational robotics and social robotics for emotion recognition. Finally, as a practical implementation of an educational robotic intervention on emotion recognition, the “Emorobot Project” as part of the EU-funded “Ecosystem of Innovation—Technopole of Rome” Project in NextGenerationEU will be presented. The project’s aim is to foster the development of social skills in children with autism spectrum disorders through the creation of an open-source social robot that can recognize emotions. The project is intended to provide teachers with a supportive tool that allows them to design individual activities and later extend the activity to classmates. An educational robot can be used as a social mediator, a playmate during the learning phase that can help students develop social skills, build peer connection, reduce social isolation—one of the main difficulties of this disorder—and foster motivation and the acquisition of interpersonal skills through interaction and imitation. This can help ensure that all students have access to quality education and that no one is left behind.