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Accessibility research has matured over the last three decades and developed a better understanding of accessibility technologies, design and evaluation methods, systems and tools as well as empirical studies in accessibility. We envision how progress in new contexts over the next decade can be made to develop stronger links to other areas in Human-Centered Computing and address the research communities. A human-centered perspective on disability needs to develop from a medical model to a social model. New methods will utilize generative AI in design and development processes that address accessibility from the start of system design. We build on AI embedded into future design processes to address participation of small numbers of users better, and new technologies to allow for personalization of multi-modal interaction to improve verbal and non-verbal communication, making body-centric computing and natural interaction truly accessible. [ABSTRACT FROM AUTHOR]

The correct organization of transfers in the public transport system allows to reduce the congestion of the route network while maintaining a sufficient level of service quality for users of transport services. When modelling the probability of transfer, it is advisable to separate the probability of a public transport user choosing a trip with a transfer and the probability of making a transfer at a certain stop of the public transport system. The first probability largely depends on the socio-economic characteristics of the passenger and the time parameters of the trip. The probability of transfer at a certain stop is affected by the information provision of the stop and the number of routes. The general configuration of the route network determines the "basic need for a transfer in the network" - the ratio of the number of pairs of transport zones between which there is no direct public transport route to the total number of pairs of transport zones. The simulation carried out in the PTV Visum software environment for Lviv city made it possible to assess the impact of the tariff system on the change in the number of transfers and to determine critical stops and routes of the network. The inclusion of data on the structure of the city's population in the model makes it possible to use mathematical functions of the probability of transfer on the socio-economic characteristics of the passenger (age, gender, income level) when calculating the forecast number of transfers on the network. [ABSTRACT FROM AUTHOR]

Chatbots possess great potential benefits, yet concerns persist regarding users adopting inappropriate, offensive language. This research delved into the influence of user characteristics on verbally aggressive behaviours towards social chatbots. Employing a mixed-method study, we examined individual characteristics such as personal dispositions, offensive language patterns, academic majors, and prior experiences with conversational agents. Findings from a ten-day field experiment involving 33 participants using a real-world Telegram-based chatbot app unveiled that users' anthropomorphism, computer-related major, and gender significantly impact their moral emotions and evaluations of the chatbot's capabilities. Moreover, employing offensive language towards the chatbot detrimentally impacted users' perceptions of its abilities, helpfulness, and likability. The research findings advocate for ongoing monitoring and effective resolution of users' behaviours regarding the use of offensive language in their interactions with a chatbot. Additionally, the results underscore the importance of incorporating diverse perspectives into chatbot design to address biases and offensive utterances. [ABSTRACT FROM AUTHOR]

While AI technology is becoming increasingly prevalent in our daily lives, the comprehension of machine learning (ML) among non-experts remains limited. Interactive machine learning (IML) has the potential to serve as a tool for end users, but many existing IML systems are designed for users with a certain level of expertise. Consequently, it remains unclear whether IML experiences can enhance the comprehension of ordinary users. In this study, we conducted a public event using an IML system to assess whether participants could gain technical comprehension through hands-on IML experiences. We implemented an interactive sound classification system featuring visualization of internal feature representation and invited visitors at a science museum to freely interact with it. By analyzing user behavior and questionnaire responses, we discuss the potential and limitations of IML systems as a tool for promoting technical comprehension among non-experts. [ABSTRACT FROM AUTHOR]

When searching and browsing the web, more and more of the information we encounter is generated or mediated through large language models (LLMs). This can be looking for a recipe, getting help on an essay, or looking for relationship advice. Yet, there is limited understanding of how individuals perceive advice provided by these LLMs. In this paper, we explore people's perception of LLM-generated advice, and what role diverse user characteristics (i.e., personality and technology readiness) play in shaping their perception. Further, as LLM-generated advice can be difficult to distinguish from human advice, we assess the perceived creepiness of such advice. To investigate this, we run an exploratory study (N = 91), where participants rate advice in different styles (generated by GPT-3.5 Turbo). Notably, our findings suggest that individuals who identify as more agreeable tend to like the advice more and find it more useful. Further, individuals with higher technological insecurity are more likely to follow and find the advice more useful, and deem it more likely that a friend could have given the advice. Lastly, we see that advice given in a ‘skeptical’ style was rated most unpredictable, and advice given in a ‘whimsical’ style was rated least malicious—indicating that LLM advice styles influence user perceptions. Our results also provide an overview of people's considerations on likelihood, receptiveness, and what advice they are likely to seek from these digital assistants. Based on our results, we provide design takeaways for LLM-generated advice and outline future research directions to further inform the design of LLM-generated advice for support applications targeting people with diverse expectations and needs.

Abstract With the rise of artificial intelligence and the development of social media, people's communication is more convenient and convenient. However, in the Internet environment, the untrue dissemination of news data leads to a large number of problems. Efficient and automatic detection of rumors in social platforms hence has become an important research direction in recent years. This paper leverages deep learning methods to mine the changing trend of user features related to rumor events, and designs a rumor detection model called Time Based User Feature Capture Model(TBUFCM). To obtain a new feature vector representing the user's comprehensive features under the current event, the proposed model first recomputes the user feature vector by using feature enhancement function. Then it utilizes GRU(Gate Recurrent Unit, GRU) and CNN(Convolutional Neural Networks, CNN) models to learn the global and local changes of user features, respectively. Finally, the hidden rumor features in the process of rumor propagation can be discovered by user and time information. The experimental results show that TBUFCM outperforms the baseline model, and when there are only 20 forwarded posts, it can also reach an accuracy of 92%. The proposed method can effectively solve the security problem of news data dissemination in the Internet environment.

Extremists are increasingly using social media to recruit and radicalize other users and increase their money. Terrorists can use popular social networks accounts and perform their activities in a hidden way. So, it is crucial to create a fruitful mechanism for controlling the spread of misinformation. Otherwise, a large number of people can mislead by this terrorist activity by joining them. Here, we propose malicious news spreading model incorporating hidden attackers of a social network. A threshold is defined for deciding the extinction of malicious news from a social network. Here, we show the importance of network alertness and activity of cybersecurity agencies in the modified model. Moreover, we obtained the optimal values of the control parameters for emergencies.

With the rise of artificial intelligence and the development of social media, people's communication is more convenient and convenient. However, in the Internet environment, the untrue dissemination of news data leads to a large number of problems. Efficient and automatic detection of rumors in social platforms hence has become an important research direction in recent years. This paper leverages deep learning methods to mine the changing trend of user features related to rumor events, and designs a rumor detection model called Time Based User Feature Capture Model(TBUFCM). To obtain a new feature vector representing the user's comprehensive features under the current event, the proposed model first recomputes the user feature vector by using feature enhancement function. Then it utilizes GRU(Gate Recurrent Unit, GRU) and CNN(Convolutional Neural Networks, CNN) models to learn the global and local changes of user features, respectively. Finally, the hidden rumor features in the process of rumor propagation can be discovered by user and time information. The experimental results show that TBUFCM outperforms the baseline model, and when there are only 20 forwarded posts, it can also reach an accuracy of 92%. The proposed method can effectively solve the security problem of news data dissemination in the Internet environment. [ABSTRACT FROM AUTHOR]

Online mental health self-help services are of societal importance and increasingly popular. Therefore, we have developed an online platform offering free self-help to the Turkish public with modules based on Cognitive Behavioral Therapy (CBT) targeting depression, anxiety, and stress respectively. The main purpose of this study is to describe the user profile of this platform. A pre-intervention self-report assessment including general demographic information and the Brief Symptom Inventory questionnaire during October 2020 until September 2022. 8331 participants completed the assessment and created an account out of the 11.228 users who registered during a two-year period, of which 8.331 (74%) completed the assessment and created an account. The majority of these users were female (76.17%), highly educated (82%), single (68%) and actively studying or working (84%). Slightly more than half (57%) of the platform user had not received psychological assistance before, while those who did receive previous assistance indicate to have benefitted from that (74%). The psychological symptoms of users are widely distributed, encompassing a broad range of user profiles. Approximately half of all users actively used the platform, while the other half did not complete any module. Among active users, the course "coping with depressive mood" was the most popular (41.45%), followed by "coping with anxiety" (37.25%) and "coping with stress" (21.30%). Offering a free online CBT self-help platform to the Turkish public seems feasible, with strong uptake among both man and woman struggling with a variety of psychological symptoms. Further research is needed to assess user satisfaction and change in symptoms over time during platform use by means of a feasibility trial. [ABSTRACT FROM AUTHOR]

[Purpose/Significance] With the popularity and development of social media, Weibo has become an important information transmission platform. Understanding the law of emergency information transmission based on the characteristics of Weibo users is of great significance for grasping the development trend of events, evaluating the influence of information, and formulating effective response strategies. [Methods/Process] Selecting typical cases of four types of emergencies (natural disasters, accidents, public health events, and social security events) as the research objects, this paper crawled the user data of the information transmission on the Weibo platform and analyzed the distributions of user gender, user geographical distribution, user attention and fan number and user type of the information transmission for these events, in order to reveal the differences and similarities of the user characteristics and transmission laws for the four types of emergencies. [Results/Conclusions] As for the similarities of the user characteristics and transmission laws, first, the audiences of the four types of emergencies have obvious regional characteristics, and the users in economically developed provinces generally have a higher attention rate. Second, the transmission users of all types of emergencies are generally concentrated on active users and grassroot users with strong and weak propagation power respectively, accompanied by a small number of authoritative users with strong influence. However, there are differences in the broadness and gender distribution of the audiences for different types of emergencies. First, natural disasters have a wider spread of the audiences and relatively higher attentiveness; accidents and public health events tend to be local events with relatively low attentiveness, whose audiences may be more concentrated in and around the affected areas; the audience spread of social security events is scattered depending on the nature of the event, the scope of influence as well as the communication channels. Second, female users pay more attention to natural disasters, public health events and social security events, while male users pay more attention to accidents, which can be attributed to the different emotional orientations and psychological characteristics associated with users of different genders. These results provide insights for the formulation of targeted guidance strategies for information dissemination. In future studies, we will collect data on other social media platforms, obtain more information on user characteristics through different channels, and introduce more in-depth analysis methods and indicators to comprehensively reveal the dynamic mechanisms of emergency information dissemination, thus improving the accuracy and effectiveness of the research.

Background: Digital mental health interventions (DMHIs) may reduce treatment access issues for those experiencing depressive and/or anxiety symptoms. DMHIs that incorporate relational agents may offer unique ways to engage and respond to users and to potentially help reduce provider burden. This study tested Woebot for Mood & Anxiety (W-MA-02), a DMHI that employs Woebot, a relational agent that incorporates elements of several evidence-based psychotherapies, among those with baseline clinical levels of depressive or anxiety symptoms. Changes in self-reported depressive and anxiety symptoms over 8 weeks were measured, along with the association between each of these outcomes and demographic and clinical characteristics. Methods: This exploratory, single-arm, 8-week study of 256 adults yielded non-mutually exclusive subsamples with either clinical levels of depressive or anxiety symptoms at baseline. Week 8 Patient Health Questionnaire-8 (PHQ-8) changes were measured in the depressive subsample (PHQ-8 ≥ 10). Week 8 Generalized Anxiety Disorder-7 (GAD-7) changes were measured in the anxiety subsample (GAD-7 ≥ 10). Demographic and clinical characteristics were examined in association with symptom changes via bivariate and multiple regression models adjusted for W-MA-02 utilization. Characteristics included age, sex at birth, race/ethnicity, marital status, education, sexual orientation, employment status, health insurance, baseline levels of depressive and anxiety symptoms, and concurrent psychotherapeutic or psychotropic medication treatments during the study. Results: Both the depressive and anxiety subsamples were predominantly female, educated, non-Hispanic white, and averaged 38 and 37 years of age, respectively. The depressive subsample had significant reductions in depressive symptoms at Week 8 (mean change =—7.28, SD = 5.91, Cohen's d = -1.23, p < 0.01); the anxiety subsample had significant reductions in anxiety symptoms at Week 8 (mean change = -7.45, SD = 5.99, Cohen's d = -1.24, p < 0.01). No significant associations were found between sex at birth, age, employment status, educational background and Week 8 symptom changes. Significant associations between depressive and anxiety symptom outcomes and sexual orientation, marital status, concurrent mental health treatment, and baseline symptom severity were found. Conclusions: The present study suggests early promise for W-MA-02 as an intervention for depression and/or anxiety symptoms. Although exploratory in nature, this study revealed potential user characteristics associated with outcomes that can be investigated in future studies. Trial Registration: This study was retrospectively registered on ClinicalTrials.gov (#NCT05672745) on January 5th, 2023. [ABSTRACT FROM AUTHOR]

In this study, the authors aimed to enhance supply chain efficiency at Company A, a key player in China's manufacturing sector, by focusing on user-centric system design. The approach involved analyzing departmental-level user characteristics to inform the development of an optimized human-machine interface. To achieve this, they conducted focus groups and card sorting exercises, identifying specific user needs across departments like planning, purchasing, receiving, warehousing, production, and quality control. The application of these insights into system design led to significant operational improvements. Notably, time savings were observed in departments such as receiving (74.44%) and quality control (41.86%). This tailored approach underscored the importance of understanding and integrating user characteristics in supply chain systems, leading to enhanced productivity and a competitive edge. The study demonstrates the impact of user-focused system design in improving both efficiency and user satisfaction in supply chain management. [ABSTRACT FROM AUTHOR]

Short-form videos are popular worldwide as a thriving form of entertainment. Its fragmentation pattern, which presents users with intensive and engaging information, might lead to addiction and adverse effects. This study aims to investigate the effect of addiction to short-form videos on users' attention, including attention while watching videos and the ability of attentional concentration after watching time. Users addicted or non-addicted to short-form videos were screened to participate in a short-form video watching task and a Stroop task based on eye-tracking technology. The results showed that addicted users reported less interest, centration, and more distractions and exhibited more fixation counts and shorter average fixation duration during watching short-form videos than non-addicted users. In the Stroop task, addicted users achieved longer response time and less accuracy and showed longer average fixation duration, more fixation counts, and saccades between the targets and the distractors than non-addicted users. The results suggest that addicted users might suffer more difficulties maintaining attention, have more attention deficits while watching short-form videos, and have impaired attentional concentration for processing interference. The findings contribute to understanding the effect of addiction to short-form videos and provide helpful insight into using it healthily and preventing addiction. [ABSTRACT FROM AUTHOR]

[Purpose/Significance] With the popularity and development of social media, Weibo has become an important information transmission platform. Understanding the law of emergency information transmission based on the characteristics of Weibo users is of great significance for grasping the development trend of events, evaluating the influence of information, and formulating effective response strategies. [Methods/Process] Selecting typical cases of four types of emergencies (natural disasters, accidents, public health events, and social security events) as the research objects, this paper crawled the user data of the information transmission on the Weibo platform and analyzed the distributions of user gender, user geographical distribution, user attention and fan number and user type of the information transmission for these events, in order to reveal the differences and similarities of the user characteristics and transmission laws for the four types of emergencies. [Results/Conclusions] As for the similarities of the user characteristics and transmission laws, first, the audiences of the four types of emergencies have obvious regional characteristics, and the users in economically developed provinces generally have a higher attention rate. Second, the transmission users of all types of emergencies are generally concentrated on active users and grassroot users with strong and weak propagation power respectively, accompanied by a small number of authoritative users with strong influence. However, there are differences in the broadness and gender distribution of the audiences for different types of emergencies. First, natural disasters have a wider spread of the audiences and relatively higher attentiveness; accidents and public health events tend to be local events with relatively low attentiveness, whose audiences may be more concentrated in and around the affected areas; the audience spread of social security events is scattered depending on the nature of the event, the scope of influence as well as the communication channels. Second, female users pay more attention to natural disasters, public health events and social security events, while male users pay more attention to accidents, which can be attributed to the different emotional orientations and psychological characteristics associated with users of different genders. These results provide insights for the formulation of targeted guidance strategies for information dissemination. In future studies, we will collect data on other social media platforms, obtain more information on user characteristics through different channels, and introduce more in-depth analysis methods and indicators to comprehensively reveal the dynamic mechanisms of emergency information dissemination, thus improving the accuracy and effectiveness of the research. [ABSTRACT FROM AUTHOR]

Disinformation in today's high-choice media environment, particularly regarding environment- and science-related issues, poses threats to individuals and society. It is argued that online users turn primarily to attitude-confirming content (selective exposure effects) which amplifies existing opinions and societal polarization. However, with the increasing prevalence of online disinformation and fake news, it is also likely that people will be exposed to disinformation that contradicts their current beliefs. Regarding the controversial issue of anthropogenic climate change, we elucidate how users react when they are (incidentally) exposed to counter-attitudinal anthropogenity-denying fake news by investigating their search and selection behavior after the exposure. The innovative research design of our case study includes pre- and post-surveys, stimulus exposure, and content analyses of web activities (n = 39). Importantly, the exposure to climate-change-denying fake news decreased acceptance of climate change and its human causes. Selection behavior was mainly attitude-confirming, while search behavior was more diverse. Journalistic sources were used most often and for longest. Individual characteristics affected searches and selections. KEY POLICY HIGHLIGHTS In the information-saturated online environment comprising scientifically correct and incorrect information, users are frequently exposed to counter-attitudinal fake news, e.g. videos denying anthropogenic climate change. After exposure to an attitude-opposing fake news video, participants' climate change acceptance decreased. In a free internet search after stimulus exposure, attitude-confirming anthropogenity-accepting positions and journalistic content dominated users' selection but not search behavior. Future climate change-related communication activities should note that participants who stated they knew little about climate change knowledge were mainly interested in solution-oriented websites. The higher participants' willingness to take personal responsibility or acknowledge others' responsibility, the more they searched for anthropogenity-opposing content. [ABSTRACT FROM AUTHOR]

Through big data analysis of borrowing information, it is possible to find the characteristics of different users. This paper applied K-means clustering to classify users. User feature vectors were extracted, and several algorithms were utilized to recommend books to different users. The hybrid algorithm achieved a precision of 96.29%, a recall rate of 92.13%, and an score of 94.16%, significantly higher than other algorithms. The findings prove that it is feasible to discover the characteristics of different users through big data analysis for book recommendation, and the hybrid algorithm performs the best. [ABSTRACT FROM AUTHOR]

Conversational AI, like Amazon's Alexa, are often marketed as tools assisting owners, but humans anthropomorphize computers, suggesting that they bond with their devices beyond an owner-tool relationship. Little empirical research has studied human-AI relationships besides relational proxies such as trust. We explored the relationships people form with conversational AI based on the Relational Models Theory (RMT, Fiske, 1992). Results of the factor analyses among frequent users (Ntotal = 729) suggest that they perceive the relationship more as a master-assistant relationship (i.e., authority ranking) and an exchange relationship (i.e., market pricing) than as a companion-like relationship (i.e., peer bonding). The correlational analysis showed that authority ranking barely correlates with system perception or user characteristics, whereas market pricing and peer bonding do. The relationship perception proved to be independent of demographic factors and label of the digital device. Our research enriches the traditional dichotomous approach. The extent to which users see their conversational AI as exchange partners or peer-like has a stronger predictive value regarding human-like system perception of conversational AI than the perception of it as servants. [ABSTRACT FROM AUTHOR]

Cybersickness is one of the greatest barriers to the adoption of virtual reality. A growing body of research has focused on identifying the characteristics of cybersickness and finding ways to mitigate it through the utilization of data from VR content, physiological signals, and body movement, along with artificial intelligence techniques. In this work, we extend prior research on cybersickness prediction by considering the role of different data modalities. We propose a novel deep learning model named multimodal, attention-based cybersickness (MAC), which learns temporal sequences and characteristics of video flows, eye movement, head movement, and electrodermal activity. Based on data collected from 27 participants, we demonstrate the effectiveness of MAC, showing an F1-score of 0.87. Our experimental results further show not only the influences of gender and prior VR experience but also the effectiveness of the attention mechanism on model performance, emphasizing the importance of considering the characteristics of data types and users in cybersickness modeling. [ABSTRACT FROM AUTHOR]

Conversational AI, like Amazon’s Alexa, are often marketed as tools assisting owners, but humans anthropomorphize computers, suggesting that they bond with their devices beyond an owner-tool relationship. Little empirical research has studied human-AI relationships besides relational proxies such as trust. We explored the relationships people form with conversational AI based on the Relational Models Theory (RMT, Fiske, 1992). Results of the factor analyses among frequent users (Ntotal = 729) suggest that they perceive the relationship more as a master-assistant relationship (i.e., authority ranking) and an exchange relationship (i.e., market pricing) than as a companion-like relationship (i.e., peer bonding). The correlational analysis showed that authority ranking barely correlates with system perception or user characteristics, whereas market pricing and peer bonding do. The relationship perception proved to be independent of demographic factors and label of the digital device. Our research enriches the traditional dichotomous approach. The extent to which users see their conversational AI as exchange partners or peer-like has a stronger predictive value regarding human-like system perception of conversational AI than the perception of it as servants.

Understanding users' individual differences may provide clues to help identify computer users who are prone to act insecurely. We examine factors that impact home users' reported computer security behaviour. We conducted two online surveys with a total of 650 participants to investigate the relationship between self-reported security behaviour and users' knowledge, motivation, confidence, risk propensity and sex-typed characteristics. We found that all of these factors impacted security behaviour, with knowledge as the most important predictor. We further show that a user's affinity to feminine or masculine characteristics is a better determinant of security behaviour than using binary male/female descriptors. Our study enabled us to confirm earlier results in the literature in a non-organisational setting, and to extend the literature by studying additional factors and by comparing the relative importance of each factor as a predictor of security behaviour. [ABSTRACT FROM AUTHOR]

Objective/Significance: To measure the international communication capacity of China’s English academic journal papers based on social media platforms.Method/Process: Social network analysis, content analysis and emotion analysis were used to describe the diffusion structure of China’s English academic journal papers on Twitter from the aspects of network structure and identify important node users, explore the identity characteristics and emotion characteristics of users as well as diffusion capability of journals of different disciplines.Conclusion/Discovery: Journal articles in the fields of natural sciences and engineering as well as humanities and social sciences exhibit a strong positive correlation between their diffusion structure and network size. Networks with a mixed diffusion structure typically have larger network sizes. The users occupying key positions in the diffusion network typically have profile information that is academically relevant. The retweets and comments are mainly statements without emotional inclination, and the comments are mainly summary descriptions of the papers. The distribution of diffusion width, diffusion strength and diffusion speed of each discipline is significantly different. There is a significant positive correlation between the diffusion structure of papers and their diffusion capabilities.Innovation/Value: To reveal the diffusion structure and characteristics of China’s English academic journal papers on social media platforms, so as to provide reference for expanding the visibility of China’s English academic journals and improving the international dissemination capacity of China’s English journals. [ABSTRACT FROM AUTHOR]

The paradigm of developing mobile apps has shifted from native apps that store data on mobile devices to mobile cloud computing (MCC) apps that send data to the cloud. Transferring users' data to the cloud provides several benefits, such as larger storage capacity and simultaneous access by multiple devices and users. However, storing data in the cloud also raises privacy concerns as users do not have direct control over their data. This study reports a privacy cost–benefit analysis including the moderating effects of dispositional traits (i.e. two personality meta-traits: stability and plasticity) and the behaviour-based trait (i.e. use experience) to understand information disclosure behaviour. The empirical study is based on a scenario-based survey (n = 807) from a diverse sample of MCC apps users. The results support the moderating effects of personality meta-traits; stability and plasticity differentially moderate the effects of perceived privacy risk and perceived value of data transfer to the cloud on information disclosure behaviour. Contradictory to prior research, prior use experience does not moderate the effects of the cost–benefit perceptions. The implications of research and practice are discussed. [ABSTRACT FROM AUTHOR]

The fundamental function of a natural gas pipeline network is to transport enough natural gas to users. Therefore, user characteristics should be considered in the formulation of the flow allocation plan of the pipeline network under accident conditions. However, user characteristics have usually not been considered in previous flow allocation models. In this study, a mixed integer linear programming model is developed to determine the optimal flow allocation plan of a large-scale and complex natural gas pipeline network under accident conditions, and the user characteristics are considered as well. The optimization objective is to maximize the weighted sum of the amount of natural gas transported to the consumers under accident conditions, and the weights of the natural gas users are obtained by user characteristics analysis. The model constraints include flow constraints, gas source supply capacity constraints, user demand constraints, pipeline transmission capacity constraints, pressure constraints, and pipeline hydraulic constraint. For the sake of model simplification, the hydraulic constraints are treated piecewise linearly. Furthermore, the model is set into a real-world situation, which is the natural gas pipeline network located in China, and the user characteristics are considered in the optimal flow allocation plan under accident condition. The impact of user characteristics is further investigated by calculating and comparing the flow allocation plan when considering and ignoring user characteristics. The study indicates that when user characteristics are considered, the natural gas pipeline network will tend to give higher priority to those crucial users. [ABSTRACT FROM AUTHOR]

Social networking platforms allow people to connect and socialize online, but the extant research suggests that increased social media (SM) use also leads to fatigue, affecting individual well-being. During the coronavirus (COVID-19) pandemic when millions of people were confined to their homes, SM use surged, posing questions about changes in individual SM use behaviors and effects. Guided by the stressor–strain–outcome framework and SM use research, this study examined the relationship among personal factors (gender, caregiving, income), two stressors (SM use intensity and risk concern about COVID-19), and the strain of SM fatigue. Survey data of 192 U.S. adult SM users were collected in late March of 2020. Our quantitative data analysis shows that SM fatigue increased significantly as individual concern about COVID-19 increased. Meanwhile, gender and caregiving responsibilities significantly impacted SM use intensity. Surprisingly, the predicted effect of SM use intensity on SM fatigue was not supported. Additional factor analysis revealed three motives of SM use (entertainment, networking, and collaboration) and quantitative analysis revealed that only the networking use of SM increased SM fatigue significantly. The results highlight the importance of considering individual risk concern and SM use motives when studying individual SM fatigue during crises.

This work aims to increase the consumption of online tourism products and promote the development of the tourism economy. Based on this, it first analyses the Internet market under the guidance of consumer psychology. Then, the influencing factors of online product decision-making for the tourism economy are discussed. Finally, based on the above analysis, it discusses and evaluates the main factors affecting the consumption of online travel products. The research method of this work is set based on psychology so that it can analyze the psychological state of consumers more deeply and promote the development of the consumer market. The results show that the main factors affecting the consumption of online travel products include online travel platforms and user characteristics. Specifically, approximately 80% of users consume online travel products based on platform reviews, approximately 10% of users consume online travel products based on platform recommended content, and approximately 5% of users consume online travel products based on platform search content. Users vary mainly by age, gender, and region and have different preferences for different platforms. Among the four major platforms, Ctrip occupies the most consumers. The conclusion is that the main way to develop the tourism economy is to build a better online travel platform. At the same time, it is necessary to promote online tourism according to the characteristics of users and increase the marketing of online tourism products. This work not only provides a reference for promoting online tourism product marketing but also helps to promote the development of the tourism economy.

Social media has become one of the most powerful and ubiquitous means by which individuals curate and share their life stories with the world at large. Not surprisingly then, researchers have started to examine the reasons why individuals post personal memories on social media and said individuals' characteristics. Across two studies, we extended this line of research by further testing the Purposes of Online Memory Sharing Scale (POMSS) and its subscales: self, social, therapeutic and directive. Additionally, we examined which of these motives led college students (Study 1) and adults of a community sample (Study 2) to post personal memories on social media and whether said motives were associated with the individuals' psychological characteristics. Overall, the results revealed that emerging adults and older adults posted personal experiences on social media primarily for social reasons. We also found that extraversion, disclosure and social media usage predicted each of the motives for posting personal experiences on social media. In addition, individuals who were more lonely and who had lower self-esteem were more likely to post personal experiences on social media for therapeutic reasons. We discuss these results in terms of their implications towards understanding the mnemonic consequences associated with social media use. [ABSTRACT FROM AUTHOR]

This study explored care recipients’ characteristics in relation to Taiwan’s “Professional Services and Home-Based Reablement Under Long-Term Care 2.0” plan. Methods: Data were extracted from the care management information database of the long-term care system in Taiwan, and secondary data analysis was performed. Descriptive statistics and multiple logistic regression were used to analyze the factors affecting outcomes. Results: The study revealed the following two main results: (1) Factors significantly influencing care recipients’ approval of the relevant professional services were residential area, type of A care management unit, a younger age, a higher level of education, a lower level of caregiver burden, having a foreign care worker, and a higher dependency level. (2) The characteristics of care recipients who were likely to discontinue their participation in home-based reablement plans were age, living with family caregivers, not having a foreign live-in caregiver, having mild to moderate disabilities (long-term care CMS level), and greater long-term care resource availability. Conclusions: Policy makers can use the care recipient characteristics identified in this study as a reference for developing an effective intervention model for the long-term care of care recipient groups in the community to meet the needs of both care recipients and their caregivers. [ABSTRACT FROM AUTHOR]

Abstract Background Smartphone apps are increasingly used for health-related behaviour change and people discover apps through different sources. However, it is unclear whether users differ by mode of app discovery. Drink Less is an alcohol reduction app that received national media coverage in the UK caused by celebrity influence (a male TV and radio national broadcaster, aged 51). Our aim was to compare users who discovered the app before and after this coverage. Methods A natural experiment assessing the impact of media coverage of Drink Less on users’ socio-demographic and drinking characteristics, app engagement levels, and extent of alcohol reduction. The study period was from 17th May 2017 to 23rd January 2019, with media coverage starting on 21st August 2018. Users were 18 years or over, based in the UK and interested in drinking less. Interrupted time series analyses using Generalised Additive Mixed Models were conducted for each outcome variable aggregated at the weekly level. Results In 66 weeks prior to the media coverage, 8617 users downloaded the app and 18,959 in 23 weeks afterwards. There was a significant step-level increase in users’ mean age (B = 8.17, p