Vulnerable Users Research Articles

Burnt out and still single: Susceptibility to dating app burnout over time

Despite the ubiquity of dating apps, there is little longitudinal research examining the mental health and well-being of dating app users. To fill this void, this study takes a social compensation approach to exploring dating app users’ burnout experiences (i.e., emotional exhaustion, inefficacy, and depersonalization) over time. Four hundred ninety-three active single dating app users were surveyed over four waves across 12 weeks, with a final sample of 190. Multilevel growth curve models showed that dating app users experienced increased emotional exhaustion and inefficacy over time. Furthermore, depression, anxiety, and loneliness predicted more emotional exhaustion and inefficacy, while problematic dating app use predicted more emotional exhaustion and less inefficacy. The results suggest that dating apps may exacerbate existing difficulties with relationship initiation among vulnerable users by increasing their risk of burnout. Dating app burnout is a potential intervention target to improve users’ dating success.

Stochastic bilevel interdiction for fake news control in online social networks

Social media platforms attempt to mitigate and control fake news, using interventions such as flagging posts or adjusting newsfeed algorithms, to protect vulnerable individuals. In this paper, we consider performing intervention actions on specific source nodes or user–user edges in social networks, under uncertain effectiveness of different intervention strategies. We model misinformation from malicious users to vulnerable communities using stochastic network interdiction formulations. Specifically, we minimize the expected number of reachable vulnerable users via stochastic maximum flow, and develop an alternative formulation for handling large-scale social networks based on their topological structures. We derive theoretical results for path-based networks and develop an approximate algorithm for single-edge removal on paths. We test instances of a social network with 23,505 nodes, based on the IMDb actors dataset, to demonstrate the scalability of the approach and its effectiveness. Via numerical studies, we find that characteristics of removed edges change when intervention effectiveness is stochastic. Our results suggest that intervention should target on (i) a smaller set of centrally located edges with nodes that represent communities where regulatory actions are more effective, and (ii) dispersed edges with nodes where intervention has a high chance of failure.

The Efficacy Potential of Cyber Security Advice as Presented in News Articles

Abstract Cyber security advice is a broad church: it is thematically expansive, comprising expert texts, user-generated data consumed by individual users via informal learning and much in-between. While there is evidence that cyber security news articles play a role in disseminating cyber security advice, the nature and extent of that role are not clear. We present a corpus of cyber security advice generated from mainstream news articles. The work was driven by two research objectives. The first objective was to ascertain what kind of actionable advice is being disseminated; the second was to explore ways of determining the efficacy potential of news-mediated security advice. The results show an increase in the generation of cyber security news articles, together with increases in vocabulary complexity and reading difficulty. We argue that these could present challenges for vulnerable users. We believe that this corpus and the accompanying analysis have the potential to inform future efforts to quantify and improve the efficacy potential of security advice dissemination.

Feasibility and Affordability of Low-Cost Air Sensors with Internet of Things for Indoor Air Quality Monitoring in Residential Buildings: Systematic Review on Sensor Information and Residential Applications, with Experience-Based Discussions

In residential buildings that are private, autonomous, and occupied spaces for most of the time, it is necessary to maintain good indoor air quality (IAQ), especially when there are children, elderly, or other vulnerable users. Within the development of sensors, their low-cost features with adequate accuracy and reliability, as well as Internet of Things applications, make them affordable, flexible, and feasible even for ordinary occupants to guarantee IAQ monitoring in their homes. This systematic review searched papers based on Scopus and Web of Science databases about the Low-Cost Sensors (LCS) and IoT applications in residential IAQ research, and 23 studies were included with targeted research contents. The review highlights several aspects of the active monitoring strategies in residential buildings, including the following: (1) Applying existing appropriate sensors and their target pollutants; (2) Applying micro-controller unit selection; (3) Sensors and devices’ costs and their monitoring applications; (4) Data collection and storage methods; (5) LCS calibration methods in applications. In addition, the review also discussed some possible solutions and limitations of LCS applications in residential buildings based on the applications from the included works and past device development experiences.

Roundabout Safety Benefits, Design Considerations and Safety Evaluation Techniques

Roundabouts are increasingly replacing conventional signalized intersections due to their potential to enhance traffic safety and operational efficiency. This paper examines and reviews the safety benefits, design considerations, and evaluation methods for roundabouts. Studies consistently show significant reductions in crashes at roundabouts relative to other at-grade intersection control systems, particularly severe injury crashes, with reductions ranging from 37% to 88%. Roundabouts reduce conflict points, lower vehicle speeds, and improve safety for both motorized and vulnerable road users. The methodology for this paper involved a comprehensive review of literature on roundabout design and safety evaluation techniques. Data from global studies were analyzed to identify key design elements such as geometric layout, signage, and pedestrian provisions, which contribute to roundabout safety. Various safety evaluation methods, including crash data analysis, traffic conflict techniques, and surrogate safety measures, were also assessed for their effectiveness in evaluating roundabout performance. Findings indicate that proper geometric design, clear signage, and the use of innovative features like right-turn bypass lanes and roundabout metering systems can enhance safety and traffic flow. Based on these findings, the paper recommends integrating these design elements, prioritizing the safety of vulnerable users, and utilizing advanced safety evaluation techniques, including simulation modelling and intelligent transportation systems (ITS) to further optimize roundabout performance.

Road’s mortality prediction for the most vulnerable users in the context of the COVID-19 pandemic in Santiago de Cali, Colombia

The purpose of this study is to predict the frequency of mortality from urban traffic injuries for the most vulnerable road users before, during and after the confinement caused by COVID-19 in Santiago de Cali, Colombia. Descriptive statistical methods were applied to the frequency of traffic crash frequency to identify vulnerable road users. Spatial georeferencing was carried out to analyze the distribution of road crashes in the three moments, before, during, and after confinement, subsequently, the behavior of the most vulnerable road users at those three moments was predicted within the framework of the probabilistic random walk. The statistical results showed that the most vulnerable road user was the cyclist, followed by motorcyclist, motorcycle passenger, and pedestrian. Spatial georeferencing between the years 2019 and 2020 showed a change in the behavior of the crash density, while in 2021 a trend like the distribution of 2019 was observed. The predictions of the daily crash frequencies of these road users in the three moments were very close to the reported crash frequency. The predictions were strengthened by considering a descriptive analysis of a range of values that may indicate the possibility of underreporting in cases registered in the city’s official agency. These results provide new elements for policy makers to develop and implement preventive measures, allocate emergency resources, analyze the establishment of policies, plans and strategies aimed at the prevention and control of crashes due to traffic injuries in the face of extraordinary situations such as the COVID-19 pandemic or other similar events.

Early morning hour and evening usage habits increase misinformation-spread

Social media manipulation poses a significant threat to cognitive autonomy and unbiased opinion formation. Prior literature explored the relationship between online activity and emotional state, cognitive resources, sunlight and weather. However, a limited understanding exists regarding the role of time of day in content spread and the impact of user activity patterns on susceptibility to mis- and disinformation. This work uncovers a strong correlation between user activity time patterns and the tendency to spread potentially disinformative content. Through quantitative analysis of Twitter (now X) data, we examine how user activity throughout the day aligns with diurnal behavioural archetypes. Evening types exhibit a significantly higher inclination towards spreading potentially disinformative content, which is more likely at night-time. This knowledge can become crucial for developing targeted interventions and strategies that mitigate misinformation spread by addressing vulnerable periods and user groups more susceptible to manipulation.

Deprescribing NSAIDs: The Potential Role of Community Pharmacists.

Non-steroidal anti-inflammatory drugs (NSAIDs) are largely used for controlling various pain conditions and are widely available in community pharmacies, with and without prescription. Despite their effectiveness, NSAIDs can pose significant risks due to potential side effects and drug interactions, particularly in polypharmacy and comorbidity contexts and for vulnerable users. This study investigated whether and how NSAIDs deprescribing can be conducted at the community pharmacy level by assessing pharmacists' confidence, attitudes, and potential barriers and facilitators. Additionally, we aimed to identify any deprescribing guidelines that pharmacists could use. A literature search and a cross-sectional digital questionnaire targeting community pharmacists in Norway were conducted. Results showed that study participants (N = 73) feel confident in identifying needs for deprescribing NSAIDs but barriers such as time constraints, lack of financial compensation, and communication challenges were noted. Participants reported positive attitudes toward deprescribing but highlighted a need for better guidelines and training. This study highlights a gap in specific guidelines for deprescribing NSAIDs and a potential for enhancing pharmacists' roles in the deprescribing process, for example, through training and improved financial incentives. Further research is encouraged to develop concrete strategies for an effective implementation where community pharmacists can be involved in the deprescribing of NSAIDs.

The Landscape of User-centered Misinformation Interventions - A Systematic Literature Review

Misinformation is one of the key challenges facing society today. User-centered misinformation interventions as digital countermeasures that exert a direct influence on users represent a promising means to deal with the large amounts of information available. While an extensive body of research on this topic exists, researchers are confronted with a diverse research landscape spanning multiple disciplines. This review systematizes the landscape of user-centered misinformation interventions to facilitate knowledge transfer, identify trends, and enable informed decision-making. Over 6,000 scholarly publications were screened, and a systematic literature review ( N=172 ) was conducted. A taxonomy was derived regarding intervention design (e.g., labels, showing indicators of misinformation, corrections, removal, or visibility reduction of content), user interaction (active or passive), and timing (e.g., pre or post exposure to misinformation or on request of the user). We provide a structured overview of approaches across multiple disciplines and derive six overarching challenges for future research regarding transferability of approaches to (1) novel platforms and (2) emerging video- and image-based misinformation, the sensible combination of automated mechanisms with (3) human experts and (4) user-centered feedback to facilitate comprehensibility, (5) encouraging media literacy without misinformation exposure, and (6) adequately addressing particularly vulnerable users such as older people or adolescents.

Knowledge retrieval and diagnostics in cloud services with large language models

Efficient customer support is the foundation for any service provider trying to improve customer relationships. An important measure of successful support is the mean time to resolve issues. The complexity and large scale of modern cloud environments make it unrealistic to reduce the resolution time without deploying intelligent solutions. The latest also provides an exceptional opportunity to leverage cross-customer product usage data for proactive solutions when the troubles of some users can be analyzed in advance to prevent similar issues of other users. We build a recommender system that matches customer support requests to other resolved support requests or knowledge base articles that contain valuable information for problem remediation. This system can be used by customers or support teams to quickly find problem-resolution tips or detect trending issues to warn vulnerable users. We utilize large language models, fine-tune for better performance, and discuss capabilities and possible improvements. During our research, we highlighted several evaluation metrics such as mean time to resolve issues and the accuracy of recommendations. However, estimating accuracy is challenging due to insufficient datasets with precise and comprehensive recommendations. Despite this, our support managers provided some estimates regarding the remediation durations. Typically, identifying and resolving an issue takes several days or weeks. With appropriate recommendations, this time can be significantly reduced to several hours and, in some simple cases, even lead to self-service capabilities.

Cyberaddiction to TikTok during the COVID-19 pandemic

Purpose The purpose of this study is to shed light on the determinants of TikTok cyberaddiction during the COVID-19 pandemic, as well as the resulting attitudinal and intentional reactions of users. Design/methodology/approach An online survey of 305 young TikTok users was conducted to test the research hypotheses. Data was analyzed through a partial least sqaures approach. Findings The results highlight the mediating role of cyberaddiction to TikTok in the relationship between psychological disorders (anxiety and loneliness) and attitude toward TikTok. Indeed, it was found that anxiety and loneliness contribute to TikTok cyberaddiction. The latter would have a positive effect on attitude toward TikTok, which decreases in the presence of fear of missing out (FOMO). It was also proven that attitude toward TikTok has a positive impact on attitude toward the brands promoted on this platform, resulting in an increase in purchasing intentions. Research limitations/implications This study was conducted during the COVID-19 pandemic. It is therefore interesting to replicate it to see if the research model can be still validated in the absence of an emergency situation. Testing the model on a larger target, including older TikTok users, could also lead to interesting findings. Practical implications Knowledge of the psychological factors behind cyberaddiction should not be seen as an opportunity to take advantage from addicted shoppers’ vulnerability. Managers should rather limit communication campaigns on TikTok during crisis periods, undertake retargeting actions by sparing vulnerable TikTok users and avoid stimulating anxiety and loneliness which can lead to cyberaddiction and compulsive purchases. Originality/value This study sheds light on the central role of cyberaddiction to TikTok in the relationship between psychological disorders and users’ reactions, during a pandemic. It focused on the moderating role of FOMO on the relationship between determinants of cyberaddiction and attitude toward TikTok. Escape theory and attachment theory were used and adapted to the context of social media.

Digital Phenotyping of Geriatric Depression Using a Community-Based Digital Mental Health Monitoring Platform for Socially Vulnerable Older Adults and Their Community Caregivers: 6-Week Living Lab Single-Arm Pilot Study.

Despite the increasing need for digital services to support geriatric mental health, the development and implementation of digital mental health care systems for older adults have been hindered by a lack of studies involving socially vulnerable older adult users and their caregivers in natural living environments. This study aims to determine whether digital sensing data on heart rate variability, sleep quality, and physical activity can predict same-day or next-day depressive symptoms among socially vulnerable older adults in their everyday living environments. In addition, this study tested the feasibility of a digital mental health monitoring platform designed to inform older adult users and their community caregivers about day-to-day changes in the health status of older adults. A single-arm, nonrandomized living lab pilot study was conducted with socially vulnerable older adults (n=25), their community caregivers (n=16), and a managerial social worker over a 6-week period during and after the COVID-19 pandemic. Depressive symptoms were assessed daily using the 9-item Patient Health Questionnaire via scripted verbal conversations with a mobile chatbot. Digital biomarkers for depression, including heart rate variability, sleep, and physical activity, were measured using a wearable sensor (Fitbit Sense) that was worn continuously, except during charging times. Daily individualized feedback, using traffic signal signs, on the health status of older adult users regarding stress, sleep, physical activity, and health emergency status was displayed on a mobile app for the users and on a web application for their community caregivers. Multilevel modeling was used to examine whether the digital biomarkers predicted same-day or next-day depressive symptoms. Study staff conducted pre- and postsurveys in person at the homes of older adult users to monitor changes in depressive symptoms, sleep quality, and system usability. Among the 31 older adult participants, 25 provided data for the living lab and 24 provided data for the pre-post test analysis. The multilevel modeling results showed that increases in daily sleep fragmentation (P=.003) and sleep efficiency (P=.001) compared with one's average were associated with an increased risk of daily depressive symptoms in older adults. The pre-post test results indicated improvements in depressive symptoms (P=.048) and sleep quality (P=.02), but not in the system usability (P=.18). The findings suggest that wearable sensors assessing sleep quality may be utilized to predict daily fluctuations in depressive symptoms among socially vulnerable older adults. The results also imply that receiving individualized health feedback and sharing it with community caregivers may help improve the mental health of older adults. However, additional in-person training may be necessary to enhance usability. ClinicalTrials.gov NCT06270121; https://clinicaltrials.gov/study/NCT06270121.

Application of Hybrid Deep Reinforcement Learning for Managing Connected Cars at Pedestrian Crossings: Challenges and Research Directions

The autonomous vehicle is an innovative field for the application of machine learning algorithms. Controlling an agent designed to drive safely in traffic is very complex as human behavior is difficult to predict. An individual’s actions depend on a large number of factors that cannot be acquired directly by visualization. The size of the vehicle, its vulnerability, its perception of the environment and weather conditions, among others, are all parameters that profoundly modify the actions that the optimized model should take. The agent must therefore have a great capacity for adaptation and anticipation in order to drive while ensuring the safety of users, especially pedestrians, who remain the most vulnerable users on the road. Deep reinforcement learning (DRL), a sub-field that is supported by the community for its real-time learning capability and the long-term temporal aspect of its objectives looks promising for AV control. In a previous article, we were able to show the strong capabilities of a DRL model with a continuous action space to manage the speed of a vehicle when approaching a pedestrian crossing. One of the points that remains to be addressed is the notion of discrete decision-making intrinsically linked to speed control. In this paper, we will present the problems of AV control during a pedestrian crossing, starting with a modelization and a DRL model with hybrid action space adapted to the scalability of a vehicle-to-pedestrian (V2P) encounter. We will also present the difficulties raised by the scalability and the curriculum-based method.

Challenges and barriers for gender-inclusive public transport policies and practice in Delhi, India

The importance of gender mainstreaming in transport services has been brought to focus by leading organisations such as the World Bank and Asian Development Bank. Commonly, women in South and Southeast Asian countries struggle with a public transport system that is not designed for them. National policy makers from these countries typically pick and choose when to follow international guidelines. The present study explores the challenges and barriers for implementation of international guidelines and standards on gender when designing inclusive public transport systems. Semi-structured interviews were undertaken with leading national policymakers and transport practitioners to gain an in-depth understanding of current practices in Delhi, India. Discussions with policymakers revealed a significant lack of willingness to consider women as a distinct user group, largely due to a male-dominated sector. Women, when rarely considered, are often categorized within the vulnerable user group. This makes any gender-specific travel requirements absent from policies and guidelines for transport practitioners to implement. Without any adequate gender-inclusive policies and government guidelines, practitioners are hamstringed into designing and implementing services which negates women’s travel needs. Interviews with transport practitioners also revealed major barriers to include funding constraints and poor national data resources. The study concludes with critical gaps in the pipeline from international to national to transport practitioners which prevents gender-specific travel needs from being met for women riding public transport.

How Platform Exchange and Safeguards Matter: The Case of Sexual Risk in Airbnb and Couchsurfing

Recent work in CHI and CSCW has devoted increasing attention to how the design of network hospitality platforms shapes user experiences and relational outcomes. In this article, I interrogate how different risk factors emerge based on the type of exchanges these platforms facilitate. To do so, I juxtapose two prominent network hospitality platforms-one facilitating negotiated exchange (i.e., Airbnb) with another facilitating reciprocal exchange (i.e., Couchsurfing). Homing in on sexual risk, an underexplored form of platform danger, and drawing on interviews with 40 female dual-platform users, I argue that Airbnb's provision of binding negotiated exchange and institutional safeguards reduces risk through three mechanisms: casting initial guest-host relation into a buyer-seller arrangement, stabilizing interactional scripts, and formalizing sexual violence recourse. Conversely, Couchsurfing's focus on reciprocal exchange and lack of safeguards increase sexual precarity for users both on- and off-platform. This study demonstrates how platforms with strong prosocial motivations can jeopardize sociality and concludes with design implications for protecting vulnerable user populations.

Urban Mobility and Road Safety: Approach to the Safety of Users of Soft Modes of Transport in Grand Lomé

Road insecurity is a major challenge for the international community. The aim of this article is to take stock of the safety of users of soft modes of transport in Grand Lomé. The methodology used in this research is based, on the one hand, on the literature review in order to examine the different theories that govern the analysis of road safety issues and particularly on the safety of vulnerable users of soft modes of transport, and on the other hand, on the analysis of the realities on the ground through surveys and interviews with stakeholders. The results reveal real difficulties in getting around users of active modes of transport on the roads of Grand Lomé. The main roads do not have sidewalks and pedestrian crossings. The sidewalks that exist on some of the new roads are, for the most part, narrow and illegally occupied by businesses, which prevent users of soft modes from benefiting from them. Bike lanes are almost non-existent on the developed lanes. Following this logic, the street belongs first and foremost to motorists. Pedestrians and cyclists are guests who must negotiate their passage on the roads of Grand Lomé.

Brain-computer interfaces and human factors: the role of language and cultural differences-Still a missing gap?

Brain-computer interfaces (BCIs) aim at the non-invasive investigation of brain activity for supporting communication and interaction of the users with their environment by means of brain-machine assisted technologies. Despite technological progress and promising research aimed at understanding the influence of human factors on BCI effectiveness, some topics still remain unexplored. The aim of this article is to discuss why it is important to consider the language of the user, its embodied grounding in perception, action and emotions, and its interaction with cultural differences in information processing in future BCI research. Based on evidence from recent studies, it is proposed that detection of language abilities and language training are two main topics of enquiry of future BCI studies to extend communication among vulnerable and healthy BCI users from bench to bedside and real world applications. In addition, cultural differences shape perception, actions, cognition, language and emotions subjectively, behaviorally as well as neuronally. Therefore, BCI applications should consider cultural differences in information processing to develop culture- and language-sensitive BCI applications for different user groups and BCIs, and investigate the linguistic and cultural contexts in which the BCI will be used.

Protection of children’s personal data in social networks in the context of the right to be forgotten

The article is devoted to a legal analysis of the protection of the confidentiality of children’s personal data on social networks in the context of the right to be forgotten and the General Data Protection Regulation. It is established that most social networks have their own usage rules, but their priority is to attract more users and company profit rather than the security of users’ personal data. Legal mechanisms for the implementation and protection of human rights in the digital environment are in the process of formation and improvement. It is proven that the application of the right to be forgotten has become one of the forms of personal data protection in social networks and on the Internet in general. Although initially the right to erasure emerged only at the level of the judicial practice of the Court of Justice of the EU, an effective mechanism has now been created to protect the individual’s right to delete outdated information about themselves, taking into account the balance between the right to privacy and the right to freedom of expression. Due to psychological immaturity and limited life experience, children and adolescents may casually disclose sensitive personal information, increasing the likelihood of abuse by both social networks and third parties. Therefore, minors receive special protection as vulnerable user categories according to the General Data Protection Regulation. It is argued that the incorporation of the right to be forgotten into EU legislation helps eliminate undesirable consequences of oversharing and restores minors’ ability to control their digital footprint. The terms of service of most social networks ensure additional protection of minors’ data and provide several options for deleting already disseminated information. Obstacles to the application of the right to be forgotten in social networks are identified, such as the lack of a developed mechanism for deleting photos or videos with minors published without the consent of the child or their parents by another user, the limited ability of users under 13 years old to request deletion without intermediaries, and the limited liability of social networks for the loss of information, data, and direct or indirect damages from such actions.

Examining the impact of different street classification scenarios on active transportation equity. Insights from Athens, Greece

Transport equity is an essential characteristic of a just city and a concept related to social inclusion. This study examined how different street classification scenarios will affect active transportation equity. Four different scenarios with different planning rationales were developed that reformulate the street classification in a municipality within a metropolitan area, i.e., Kallithea in Athens, Greece. These scenarios give rise to new street categories that reallocate street space favoring active transport users. The evaluation of active transportation equity was carried out by utilizing spatial analysis and welfare economics through the lens of egalitarian and sufficientarian approaches. Based on the results, scenarios that propose new urban centers as well as a readable street classification with traffic calming zones determined by ring roads seem to be more efficient in accommodating vulnerable users in an equitable way. Hence, new planning schemes should rely on active modes, leaving automobiles behind. In general, measuring the impact of street classification scenarios through the lens of equity is critical for improving the quality of life in cities. Additionally, it is noteworthy knowledge for policy makers, planners, and local administrators. Finally, this research may inspire new studies, enriching the scientific debate.

Cartographic Resources for Equitable University–Community Interaction in Slum Areas

Cartographic resources play a crucial role in facilitating communication across various sectors, including research projects focused on low-income communities. Despite this, some researchers still adhere to colonialist and exploitative approaches. This study aims to promote equitable university–community interaction though cartographic resources, aid academic and vulnerable community users in choosing a better platform for their work, and provide insights to developers for improving the platforms to better serve the user profiles of community members. To achieve this, we examined the use of cartographic resources in five projects within low-income communities (commonly referred to as favelas or so-called “slums”) in three Brazilian cities, all guided by equitable principles. The study unfolds in four stages: (i) data collection from documents and interviews; (ii) systematization into seven analytical categories—cartographic resources, data, personnel, processes, equipment, general objectives, and specific objectives; (iii) analysis of eight cartographic resources; and (iv) a critical examination of the outcomes. The synthesis of the collected information identified 65 characteristics/demands, with 17 common to all projects, including vector feature creation, thematic map design, printed map usage, and satellite imagery. We also identified 53 geographic information system (GIS) functionalities required for the projects, predominantly related to vector data generation and editing. The outcomes demonstrate the benefits of project methodologies, contributing to a decolonial university–community praxis. Additionally, they underscore the potential of digital cartographic resources, functioning not solely as data collection tools but also as powerful instruments that empower slum residents to advocate for improvements and foster local development.