The impact of Internet-based specific activities on the perceptions of Internet addiction, quality of life, and excessive usage: a cross-sectional study

Introduction: Recent research has examined the context in which preference for specific online activities arises, leading researchers to suggest that excessive Internet users are engaged in specific activities rather than ‘generalized’ Internet use. The present study aimed to partially replicate and expand these findings by addressing four research questions regarding (i) participants' preferred online activities, (ii) possible expected changes in online behavior in light of hypothetical scenarios, (iii) perceived quality of life when access to Internet was not possible, and (iv) how participants with self-diagnosed Internet addiction relate to intensity and frequency of Internet use. Methods: A cross-sectional design was adopted using convenience and snowball sampling to recruit participants. A total of 1057 Internet users with ages ranging from 16 to 70 years (M age = 30 years, SD = 10.84) were recruited online via several English-speaking online forums. Results: Most participants indicated that their preferred activities were (i) accessing general information and news, (ii) social networking, and (iii) using e-mail and/or online chatting. Participants also reported that there would be a significant decrease of their Internet use if access to their preferred activities was restricted. The study also found that 51% of the total sample perceived themselves as being addicted to the Internet, while 14.1% reported that without the Internet their life would be improved. Conclusions: The context in which the Internet is used appears to determine the intensity and the lengths that individuals will go to use this tool. The implications of these findings are further discussed

Rethinking Privacy and Security Mechanisms in Online Social Networks

With billions of users, Online Social Networks(OSNs) are amongst the largest scale communication applications on the Internet. OSNs enable users to easily access news from local and worldwide, as well as share information publicly and interact with friends. On the negative side, OSNs are also abused by spammers to distribute ads or malicious information, such as scams, fraud, and even manipulate public political opinions. Having achieved significant commercial success with large amount of user information, OSNs do treat the security and privacy of their users seriously and provide several mechanisms to reinforce their account security and information privacy. However, the efficacy of those measures is either not thoroughly validated or in need to be improved. In sight of cyber criminals and potential privacy threats on OSNs, we focus on the evaluations and improvements of OSN user privacy configurations, account security protection mechanisms, and trending topic security in this dissertation. We first examine the effectiveness of OSN privacy settings on protecting user privacy. Given each privacy configuration, we propose a corresponding scheme to reveal the target user\u27s basic profile and connection information starting from some leaked connections on the user\u27s homepage. Based on the dataset we collected on Facebook, we calculate the privacy exposure in each privacy setting type and measure the accuracy of our privacy inference schemes with different amount of public information. The evaluation results show that (1) a user\u27s private basic profile can be inferred with high accuracy and (2) connections can be revealed in a significant portion based on even a small number of directly leaked connections. Secondly, we propose a behavioral-profile-based method to detect OSN user account compromisation in a timely manner. Specifically, we propose eight behavioral features to portray a user\u27s social behavior. A user\u27s statistical distributions of those feature values comprise its behavioral profile. Based on the sample data we collected from Facebook, we observe that each user\u27s activities are highly likely to conform to its behavioral profile while two different user\u27s profile tend to diverge from each other, which can be employed for compromisation detection. The evaluation result shows that the more complete and accurate a user\u27s behavioral profile can be built the more accurately compromisation can be detected. Finally, we investigate the manipulation of OSN trending topics. Based on the dataset we collected from Twitter, we manifest the manipulation of trending and a suspect spamming infrastructure. We then measure how accurately the five factors (popularity, coverage, transmission, potential coverage, and reputation) can predict trending using an SVM classifier. We further study the interaction patterns between authenticated accounts and malicious accounts in trending. at last we demonstrate the threats of compromised accounts and sybil accounts to trending through simulation and discuss countermeasures against trending manipulation

A systematic review and meta-analysis of the effect of digital game-based or influencer food and non-alcoholic beverage marketing on children and adolescents: Exploring hierarchy of effects outcomes.

Videogame livestreaming platforms are an emerging form of digital media, popular with young people, where users watch gaming influencers play videogames. Food and non-alcoholic beverage (hereafter: food) brands have a substantial presence on these platforms, yet no studies have examined the impact of this food marketing on young people. This systematic review and meta-analysis examined the evidence (quantitative or mixed-method) for a relationship between exposure to digital game-based or influencer food marketing, and food-related (brand awareness, attitudes, preferences, purchase, and consumption), and post-consumption (weight, body mass index [BMI], and dental caries) outcomes in young people (≤18 years). Twenty-three databases were searched in March 2021. Twenty-two studies met the inclusion criteria, of which 20 were included in the quantitative synthesis. Meta-analyses indicated food marketing was associated with more positive attitudes and greater preferences (OR = 1.74, p < 0.001 [95%CI: 1.355, 2.232]), and increased consumption (SMD = 0.37, p < 0.001 [95%CI: 0.219, 0.529]). Narrative synthesis indicated that food marketing may increase brand awareness but not pester intent, although data were limited. Evidence suggests that there is a relationship between exposure to food marketing via influencers and digital gaming media, and several food-related outcomes. This is the first quantitative synthesis to demonstrate these relationships; this work has implications for food marketing policy

A Human-Centric Metaverse Enabled by Brain-Computer Interface: A Survey

The growing interest in the Metaverse has generated momentum for members of academia and industry to innovate toward realizing the Metaverse world. The Metaverse is a unique, continuous, and shared virtual world where humans embody a digital form within an online platform. Through a digital avatar, Metaverse users should have a perceptual presence within the environment and can interact and control the virtual world around them. Thus, a human-centric design is a crucial element of the Metaverse. The human users are not only the central entity but also the source of multi-sensory data that can be used to enrich the Metaverse ecosystem. In this survey, we study the potential applications of Brain-Computer Interface (BCI) technologies that can enhance the experience of Metaverse users. By directly communicating with the human brain, the most complex organ in the human body, BCI technologies hold the potential for the most intuitive human-machine system operating at the speed of thought. BCI technologies can enable various innovative applications for the Metaverse through this neural pathway, such as user cognitive state monitoring, digital avatar control, virtual interactions, and imagined speech communications. This survey first outlines the fundamental background of the Metaverse and BCI technologies. We then discuss the current challenges of the Metaverse that can potentially be addressed by BCI, such as motion sickness when users experience virtual environments or the negative emotional states of users in immersive virtual applications. After that, we propose and discuss a new research direction called Human Digital Twin, in which digital twins can create an intelligent and interactable avatar from the user's brain signals. We also present the challenges and potential solutions in synchronizing and communicating between virtual and physical entities in the Metaverse

Can Upward Brand Extensions be an Opportunity for Marketing Managers During the Covid-19 Pandemic and Beyond?

Early COVID-19 research has guided current managerial practice by introducing more products across different product categories as consumers tried to avoid perceived health risks from food shortages, i.e. horizontal brand extensions. For example, Leon, a fast-food restaurant in the UK, introduced a new range of ready meal products. However, when the food supply stabilised, availability may no longer be a concern for consumers. Instead, job losses could be a driver of higher perceived financial risks. Meanwhile, it remains unknown whether the perceived health or financial risks play a more significant role on consumers’ consumptions. Our preliminary survey shows perceived health risks outperform perceived financial risks to positively influence purchase intention during COVID-19. We suggest such a result indicates an opportunity for marketers to consider introducing premium priced products, i.e. upward brand extensions. The risk-as�feelings and signalling theories were used to explain consumer choice under risk may adopt affective heuristic processing, using minimal cognitive efforts to evaluate products. Based on this, consumers are likely to be affected by the salient high-quality and reliable product cue of upward extension signalled by its premium price level, which may attract consumers to purchase when they have high perceived health risks associated with COVID-19. Addressing this, a series of experimental studies confirm that upward brand extensions (versus normal new product introductions) can positively moderate the positive effect between perceived health risks associated with COVID-19 and purchase intention. Such an effect can be mediated by affective heuristic information processing. The results contribute to emergent COVID-19 literature and managerial practice during the pandemic but could also inform post-pandemic thinking around vertical brand extensions

Understanding personal and contextual factors to increase motivation in gamified systems

Gamification, the use of game elements in non-game contexts, has been shown to help people reaching their goals, affect people's behavior and enhance the users' experience within interactive systems. However, past research has shown that gamification is not always successful. In fact, literature reviews revealed that almost half of the interventions were only partially successful or even unsuccessful. Therefore, understanding the factors that have an influence on psychological measures and behavioral outcomes of gamified systems is much in need. In this thesis, we contribute to this by considering the context in which gamified systems are applied and by understanding personal factors of users interacting with the system. Guided by Self-Determination Theory, a major theory on human motivation, we investigate gamification and its effects on motivation and behavior in behavior change contexts, provide insights on contextual factors, contribute knowledge on the effect of personal factors on both the perception and effectiveness of gamification elements and lay out ways of utilizing this knowledge to implement personalized gamified systems. Our contribution is manifold: We show that gamification affects motivation through need satisfaction and by evoking positive affective experiences, ultimately leading to changes in people's behavior. Moreover, we show that age, the intention to change behavior, and Hexad user types play an important role in explaining interpersonal differences in the perception of gamification elements and that tailoring gamified systems based on these personal factors has beneficial effects on both psychological and behavioral outcomes. Lastly, we show that Hexad user types can be partially predicted by smartphone data and interaction behavior in gamified systems and that they can be assessed in a gameful way, allowing to utilize our findings in gamification practice. Finally, we propose a conceptual framework to increase motivation in gamified systems, which builds upon our findings and outlines the importance of considering both contextual and personal factors. Based on these contributions, this thesis advances the field of gamification by contributing knowledge to the open questions of how and why gamification works and which factors play a role in this regard.Gamification, die Nutzung von Spielelementen in spielfremden Kontexten, kann nachweislich Menschen helfen, ihre Ziele zu erreichen, das Verhalten von Menschen zu beeinflussen und die Erfahrung der User in interaktiven Systemen zu verbessern. Allerdings hat die bisherige Forschung gezeigt, dass Gamification nicht immer erfolgreich ist. So haben Literaturübersichten ergeben, dass fast die Hälfte der Interventionen nur teilweise erfolgreich oder sogar erfolglos waren. Daher besteht ein großer Bedarf, die Faktoren zu verstehen, die einen Einfluss auf psychologische Maße sowie auf das Verhalten von Usern in gamifizierten Systemen haben. In dieser Arbeit tragen wir dazu bei, indem wir den Kontext, in dem gamifizierte Systeme eingesetzt werden, betrachten und persönliche Faktoren von Usern, die mit dem System interagieren, verstehen. Geleitet von der Selbstbestimmungstheorie, einer der wichtigsten Theorien zur menschlichen Motivation, untersuchen wir Gamification und dessen Auswirkungen auf Motivation und Verhalten in Kontexten zur Verhaltensänderung. Wir liefern Erkenntnisse über kontextuelle Faktoren, tragen Wissen über den Einfluss persönlicher Faktoren auf die Wahrnehmung und Effektivität von Gamification-Elementen bei und bieten Möglichkeiten, dieses Wissen für die Implementierung personalisierter gamifizierter Systeme zu nutzen. Unser Beitrag ist mannigfaltig: Wir zeigen, dass Gamification die Motivation durch Bedürfnisbefriedigung und durch das Hervorrufen positiver affektiver Erfahrungen beeinflusst, was letztlich zu Verhaltensänderungen führen kann. Darüber hinaus zeigen wir, dass das Alter, die Absicht, das Verhalten zu ändern, und Hexad-Usertypen eine wichtige Rolle bei der Erklärung von interpersonellen Unterschieden in der Wahrnehmung von Gamification-Elementen spielen. Ebenso zeigen unsere Resultate dass die Anpassung von gamifizierten Systemen auf Basis dieser persönlichen Faktoren positive Auswirkungen auf psychologische und verhaltensbezogene Ergebnisse hat. Letztlich zeigen wir, dass Hexad-Usertypen teilweise durch Smartphone-Daten und Interaktionsverhalten in gamifizierten Systemen vorhergesagt werden können und dass sie auf spielerische Art und Weise erhoben werden können. Dies ermöglicht, unsere Erkenntnisse in der Gamification-Praxis zu nutzen. Auf Basis dieser Ergebnisse schlagen wir ein konzeptuelles Framework zur Steigerung der Motivation in gamifizierten Systemen vor, das die Wichtigkeit der Berücksichtigung sowohl kontextueller als auch persönlicher Faktoren hervorhebt. Diese Erkenntnisse bereichern das Forschungsfeld Gamification, indem sie Wissen zu den offenen Fragen, wie und warum Gamification funktioniert und welche Faktoren in diesem Zusammenhang eine Rolle spielen, beitragen

COVID-19 and Environment: Impacts of a Global Pandemic

This is a reprint of the MDPI IJERPH Special Issue entitled "COVID-19 and Environment: Impacts of a Global Pandemic". The reprint consists of 17 papers with different topics related to the COVID-19 pandemic and environmental impacts using data from different countries all over the globe

Privacy For Whom? A Multi-Stakeholder Exploration of Privacy Designs

Privacy is considered one of the fundamental human rights. Researchers have been investigating privacy issues in various domains, such as our physical privacy, data privacy, privacy as a legal right, and privacy designs. In the Human-Computer Interaction field, privacy researchers have been focusing on understanding people\u27s privacy concerns when they interact with computing systems, designing and building privacy-enhancing technologies to help people mitigate these concerns, and investigating how people\u27s privacy perceptions and the privacy designs influence people\u27s behaviors. Existing privacy research has been overwhelmingly focusing on the privacy needs of end-users, i.e., people who use a system or a product, such as Internet users and smartphone users. However, as our computing systems are becoming more and more complex, privacy issues within these systems have started to impact not only the end-users but also other stakeholders, and privacy-enhancing mechanisms designed for the end-users can also affect multiple stakeholders beyond the users. In this dissertation, I examine how different stakeholders perceive privacy-related issues and expect privacy designs to function across three application domains: online behavioral advertising, drones, and smart homes. I choose these three domains because they represent different multi-stakeholder environments with varying nature of complexity. In particular, these environments present the opportunities to study technology-mediated interpersonal relationships, i.e., the relationship between primary users (owners, end-users) and secondary users (bystanders), and to investigate how these relationships influence people\u27s privacy perceptions and their desired ways of privacy protection. Through a combination of qualitative, quantitative, and design methods, including interviews, surveys, participatory designs, and speculative designs, I present how multi-stakeholder considerations change our understandings of privacy and influence privacy designs. I draw design implications from the study results and guide future privacy designs to consider the needs of different stakeholders, e.g., cooperative mechanisms that aim to enhance the communication between primary and secondary users. In addition, this methodological approach allows researchers to directly and proactively engage with multiple stakeholders and explore their privacy perceptions and expected privacy designs. This is different from what has been commonly used in privacy literature and as such, points to a methodological contribution. Finally, this dissertation shows that when applying the theory of Contextual Integrity in a multi-stakeholder environment, there are hidden contextual factors that may alter the contextual informational norms. I present three examples from the study results and argue that it is necessary to carefully examine such factors in order to clearly identify the contextual norms. I propose a research agenda to explore best practices of applying the theory of Contextual Integrity in a multi-stakeholder environment

Real-time generation and adaptation of social companion robot behaviors

Social robots will be part of our future homes. They will assist us in everyday tasks, entertain us, and provide helpful advice. However, the technology still faces challenges that must be overcome to equip the machine with social competencies and make it a socially intelligent and accepted housemate. An essential skill of every social robot is verbal and non-verbal communication. In contrast to voice assistants, smartphones, and smart home technology, which are already part of many people's lives today, social robots have an embodiment that raises expectations towards the machine. Their anthropomorphic or zoomorphic appearance suggests they can communicate naturally with speech, gestures, or facial expressions and understand corresponding human behaviors. In addition, robots also need to consider individual users' preferences: everybody is shaped by their culture, social norms, and life experiences, resulting in different expectations towards communication with a robot. However, robots do not have human intuition - they must be equipped with the corresponding algorithmic solutions to these problems. This thesis investigates the use of reinforcement learning to adapt the robot's verbal and non-verbal communication to the user's needs and preferences. Such non-functional adaptation of the robot's behaviors primarily aims to improve the user experience and the robot's perceived social intelligence. The literature has not yet provided a holistic view of the overall challenge: real-time adaptation requires control over the robot's multimodal behavior generation, an understanding of human feedback, and an algorithmic basis for machine learning. Thus, this thesis develops a conceptual framework for designing real-time non-functional social robot behavior adaptation with reinforcement learning. It provides a higher-level view from the system designer's perspective and guidance from the start to the end. It illustrates the process of modeling, simulating, and evaluating such adaptation processes. Specifically, it guides the integration of human feedback and social signals to equip the machine with social awareness. The conceptual framework is put into practice for several use cases, resulting in technical proofs of concept and research prototypes. They are evaluated in the lab and in in-situ studies. These approaches address typical activities in domestic environments, focussing on the robot's expression of personality, persona, politeness, and humor. Within this scope, the robot adapts its spoken utterances, prosody, and animations based on human explicit or implicit feedback.Soziale Roboter werden Teil unseres zukünftigen Zuhauses sein. Sie werden uns bei alltäglichen Aufgaben unterstützen, uns unterhalten und uns mit hilfreichen Ratschlägen versorgen. Noch gibt es allerdings technische Herausforderungen, die zunächst überwunden werden müssen, um die Maschine mit sozialen Kompetenzen auszustatten und zu einem sozial intelligenten und akzeptierten Mitbewohner zu machen. Eine wesentliche Fähigkeit eines jeden sozialen Roboters ist die verbale und nonverbale Kommunikation. Im Gegensatz zu Sprachassistenten, Smartphones und Smart-Home-Technologien, die bereits heute Teil des Lebens vieler Menschen sind, haben soziale Roboter eine Verkörperung, die Erwartungen an die Maschine weckt. Ihr anthropomorphes oder zoomorphes Aussehen legt nahe, dass sie in der Lage sind, auf natürliche Weise mit Sprache, Gestik oder Mimik zu kommunizieren, aber auch entsprechende menschliche Kommunikation zu verstehen. Darüber hinaus müssen Roboter auch die individuellen Vorlieben der Benutzer berücksichtigen. So ist jeder Mensch von seiner Kultur, sozialen Normen und eigenen Lebenserfahrungen geprägt, was zu unterschiedlichen Erwartungen an die Kommunikation mit einem Roboter führt. Roboter haben jedoch keine menschliche Intuition - sie müssen mit entsprechenden Algorithmen für diese Probleme ausgestattet werden. In dieser Arbeit wird der Einsatz von bestärkendem Lernen untersucht, um die verbale und nonverbale Kommunikation des Roboters an die Bedürfnisse und Vorlieben des Benutzers anzupassen. Eine solche nicht-funktionale Anpassung des Roboterverhaltens zielt in erster Linie darauf ab, das Benutzererlebnis und die wahrgenommene soziale Intelligenz des Roboters zu verbessern. Die Literatur bietet bisher keine ganzheitliche Sicht auf diese Herausforderung: Echtzeitanpassung erfordert die Kontrolle über die multimodale Verhaltenserzeugung des Roboters, ein Verständnis des menschlichen Feedbacks und eine algorithmische Basis für maschinelles Lernen. Daher wird in dieser Arbeit ein konzeptioneller Rahmen für die Gestaltung von nicht-funktionaler Anpassung der Kommunikation sozialer Roboter mit bestärkendem Lernen entwickelt. Er bietet eine übergeordnete Sichtweise aus der Perspektive des Systemdesigners und eine Anleitung vom Anfang bis zum Ende. Er veranschaulicht den Prozess der Modellierung, Simulation und Evaluierung solcher Anpassungsprozesse. Insbesondere wird auf die Integration von menschlichem Feedback und sozialen Signalen eingegangen, um die Maschine mit sozialem Bewusstsein auszustatten. Der konzeptionelle Rahmen wird für mehrere Anwendungsfälle in die Praxis umgesetzt, was zu technischen Konzeptnachweisen und Forschungsprototypen führt, die in Labor- und In-situ-Studien evaluiert werden. Diese Ansätze befassen sich mit typischen Aktivitäten in häuslichen Umgebungen, wobei der Schwerpunkt auf dem Ausdruck der Persönlichkeit, dem Persona, der Höflichkeit und dem Humor des Roboters liegt. In diesem Rahmen passt der Roboter seine Sprache, Prosodie, und Animationen auf Basis expliziten oder impliziten menschlichen Feedbacks an