User behaviors in a community-based multifunctional urban park with dog friendly area.

Urban parks are essential for community revitalization; for example, they are places to walk dogs and interact with other dog keepers. This study focused on an urban park with a dog-friendly area to be used by both dog keepers and other users as an alternative to off-leash dog parks that completely separate them and clarified aspects of park use through behavioral observation. The behaviors of 7122 visitors in 14 areas in the park and 294 pairs of dogs and their keepers in the dog-friendly area were observed. The results showed that the visitors' age groups and use behaviors differed by area. The dog-friendly area was in constant demand among dog keepers as a place where they could stay and interact with others and as a destination or relay point when walking their dogs. Visitors used the park in accordance with rules and morals, and the park was well managed. As it can be comfortably used by everyone (with or without dogs), this park can serve as a model for the development and maintenance of community-based multifunctional parks in urban areas.

BaitBuster-Bangla: A comprehensive dataset for clickbait detection in Bangla with multi-feature and multi-modal analysis.

This study presents a large multi-modal Bangla YouTube clickbait dataset consisting of 253,070 data points collected through an automated process using the YouTube API and Python web automation frameworks. The dataset contains 18 diverse features categorized into metadata, primary content, engagement statistics, and labels for individual videos from 58 Bangla YouTube channels. A rigorous preprocessing step has been applied to denoise, deduplicate, and remove bias from the features, ensuring unbiased and reliable analysis. As the largest and most robust clickbait corpus in Bangla to date, this dataset provides significant value for natural language processing and data science researchers seeking to advance modeling of clickbait phenomena in low-resource languages. Its multi-modal nature allows for comprehensive analyses of clickbait across content, user interactions, and linguistic dimensions to develop more sophisticated detection methods with cross-linguistic applications.

Comprehensive Analysis and Evaluation of Anomalous User Activity in Web Server Logs.

In this study, we present a novel machine learning framework for web server anomaly detection that uniquely combines the Isolation Forest algorithm with expert evaluation, focusing on individual user activities within NGINX server logs. Our approach addresses the limitations of traditional methods by effectively isolating and analyzing subtle anomalies in vast datasets. Initially, the Isolation Forest algorithm was applied to extensive NGINX server logs, successfully identifying outlier user behaviors that conventional methods often overlook. We then employed DBSCAN for detailed clustering of these anomalies, categorizing them based on user request times and types. A key innovation of our methodology is the incorporation of post-clustering expert analysis. Cybersecurity professionals evaluated the identified clusters, adding a crucial layer of qualitative assessment. This enabled the accurate distinction between benign and potentially harmful activities, leading to targeted responses such as access restrictions or web server configuration adjustments. Our approach demonstrates a significant advancement in network security, offering a more refined understanding of user behavior. By integrating algorithmic precision with expert insights, we provide a comprehensive and nuanced strategy for enhancing cybersecurity measures. This study not only advances anomaly detection techniques but also emphasizes the critical need for a multifaceted approach in protecting web server infrastructures.

Application of Deep Learning and Intelligent Sensing Analysis in Smart Home.

Deep learning technology can improve sensing efficiency and has the ability to discover potential patterns in data; the efficiency of user behavior recognition in the field of smart homes has been further improved, making the recognition process more intelligent and humanized. This paper analyzes the optical sensors commonly used in smart homes and their working principles through case studies and explores the technical framework of user behavior recognition based on optical sensors. At the same time, CiteSpace (Basic version 6.2.R6) software is used to visualize and analyze the related literature, elaborate the main research hotspots and evolutionary changes of optical sensor-based smart home user behavior recognition, and summarize the future research trends. Finally, fully utilizing the advantages of cloud computing technology, such as scalability and on-demand services, combining typical life situations and the requirements of smart home users, a smart home data collection and processing technology framework based on elderly fall monitoring scenarios is designed. Based on the comprehensive research results, the application and positive impact of optical sensors in smart home user behavior recognition were analyzed, and inspiration was provided for future smart home user experience research.

Analysis of the evolving factors of social media users' emotions and behaviors: a longitudinal study from China's COVID-19 opening policy period.

The outbreak of the COVID-19 pandemic has triggered citizen panic and social crises worldwide. The Chinese government was the first to implement strict prevention and control policies. However, in December 2022, the Chinese government suddenly changed its prevention and control policies and completely opened up. This led to a large-scale infection of the epidemic in a short period of time, which will cause unknown social impacts. This study collected 500+ epidemic-related hotspots and 200,000+ data from November 1, 2022, to March 1, 2023. Using a sentiment classification method based on pre-trained neural network models, we conducted inductive analysis and a summary of high-frequency words of various emotions. This study focuses on the inflection point of the emotional evolution of social media users and the evolution of "hot topic searches" events and emotional behavioral factors after the sudden open policy. Our research results show that, first of all, the positive emotions of social media users are divided into 4 inflection points and 5 time periods, and the negative emotions are divided into 3 inflection points and 4 time periods. Behavioral factors are different at each stage of each emotion. And the evolution patterns of positive emotions and negative emotions are also different. Secondly, the evolution of behavioral elements deserves more attention. Continue to pay attention: The treatment of diseases, the recovery of personal health, the promotion of festive atmosphere, and the reduction of publicity on the harm of "new crown sequelae and second infections" are the behavioral concerns that affect users' emotional changes. Finally, it is necessary to change the "hot topic searches" event by guiding the user's behavioral focus to control the inflection point of the user's emotion. This study helps governments and institutions understand the dynamic impact of epidemic policy changes on social media users, thereby promoting policy formulation and better coping with social crises.

Factors Influencing the Answerability and Popularity of a Health-Related Post in the Question-and-Answer Community: Infodemiology Study of Metafilter.

BACKGROUND: The web-based health question-and-answer (Q&A) community has become the primary and handy way for people to access health information and knowledge directly. OBJECTIVE: The objective of our study is to investigate how content-related, context-related, and user-related variables influence the answerability and popularity of health-related posts based on a user-dynamic, social network, and topic-dynamic semantic network, respectively. METHODS: Full-scale data on health consultations were acquired from the Metafilter Q&A community. These variables were designed in terms of context, content, and contributors. Negative binomial regression models were used to examine the influence of these variables on the favorite and comment counts of a health-related post. RESULTS: A total of 18,099 post records were collected from a well-known Q&A community. The findings of this study include the following. Content-related variables have a strong impact on both the answerability and popularity of posts. Notably, sentiment values were positively related to favorite counts and negatively associated with comment counts. User-related variables significantly affected the answerability and popularity of posts. Specifically, participation intensity was positively related to comment count and negatively associated with favorite count. Sociability breadth only had a significant impact on comment count. Context-related variables have a more substantial influence on the popularity of posts than on their answerability. The topic diversity variable exhibits an inverse correlation with the comment count while manifesting a positive correlation with the favorite count. Nevertheless, topic intensity has a significant effect only on favorite count. CONCLUSIONS: The research results not only reveal the factors influencing the answerability and popularity of health-related posts, which can help them obtain high-quality answers more efficiently, but also provide a theoretical basis for platform operators to enhance user engagement within health Q&A communities.

The effect of airtightness required in building energy conservation regulations on indoor and outdoor originated pollutants.

The contradiction of indoor air quality (IAQ) and energy conservation by isolating the indoor environment from the outdoor through airtightness is one of the challenges of the building sector. The key issue is, what are the optimum airtightness limits that can ensure IAQ in naturally ventilated buildings, taking into account the paradoxical effect of house leakages on the infiltration of outdoor pollutants and accumulation of indoor-generated pollutants? For this purpose, the effect of different levels of airtightness required in energy-compliant, low-energy, and very low-energy buildings on the concentration of two pollutants with outdoor and indoor origin, PM2.5 and formaldehyde, respectively, were studied. This study used a multizone model, CONTAM(W), which was validated using measured data to study the distribution of selected pollutants in a typical relatively old dwelling, to investigate the situation in Iran. Subsequently, we conducted simulations based on different combinations of scenarios for airtightness, user behavior, source strength, and meteorological parameters. The results showed that increasing the airtightness from the baseline scenario (ACH50 = 11.11/h) to 3, 1.5, and 0.75 in closed window conditions reduced the PM2.5 by 15%, 38%, and 58%, respectively, and elevated formaldehyde by 23%, 77%, and 169%, correspondingly. Under normal outdoor PM2.5 pollution, indoor formaldehyde levels exceeded the permissible limit only in closed window conditions, and IAQ remained acceptable in other scenarios. However, there is no indication that IAQ can be ensured by any degree of airtightness under severe outdoor air pollution, demanding specific solutions, such as those proposed in this work.

A Mental Health and Well-Being Chatbot: User Event Log Analysis.

BACKGROUND: Conversational user interfaces, or chatbots, are becoming more popular in the realm of digital health and well-being. While many studies focus on measuring the cause or effect of a digital intervention on people's health and well-being (outcomes), there is a need to understand how users really engage and use a digital intervention in the real world. OBJECTIVE: In this study, we examine the user logs of a mental well-being chatbot called ChatPal, which is based on the concept of positive psychology. The aim of this research is to analyze the log data from the chatbot to provide insight into usage patterns, the different types of users using clustering, and associations between the usage of the app's features. METHODS: Log data from ChatPal was analyzed to explore usage. A number of user characteristics including user tenure, unique days, mood logs recorded, conversations accessed, and total number of interactions were used with k-means clustering to identify user archetypes. Association rule mining was used to explore links between conversations. RESULTS: ChatPal log data revealed 579 individuals older than 18 years used the app with most users being female (n=387, 67%). User interactions peaked around breakfast, lunchtime, and early evening. Clustering revealed 3 groups including "abandoning users" (n=473), "sporadic users" (n=93), and "frequent transient users" (n=13). Each cluster had distinct usage characteristics, and the features were significantly different (P<.001) across each group. While all conversations within the chatbot were accessed at least once by users, the "treat yourself like a friend" conversation was the most popular, which was accessed by 29% (n=168) of users. However, only 11.7% (n=68) of users repeated this exercise more than once. Analysis of transitions between conversations revealed strong links between "treat yourself like a friend," "soothing touch," and "thoughts diary" among others. Association rule mining confirmed these 3 conversations as having the strongest linkages and suggested other associations between the co-use of chatbot features. CONCLUSIONS: This study has provided insight into the types of people using the ChatPal chatbot, patterns of use, and associations between the usage of the app's features, which can be used to further develop the app by considering the features most accessed by users.

UsbVisdaNet: User Behavior Visual Distillation and Attention Network for Multimodal Sentiment Classification.

In sentiment analysis, biased user reviews can have a detrimental impact on a company's evaluation. Therefore, identifying such users can be highly beneficial as their reviews are not based on reality but on their characteristics rooted in their psychology. Furthermore, biased users may be seen as instigators of other prejudiced information on social media. Thus, proposing a method to help detect polarized opinions in product reviews would offer significant advantages. This paper proposes a new method for sentiment classification of multimodal data, which is called UsbVisdaNet (User Behavior Visual Distillation and Attention Network). The method aims to identify biased user reviews by analyzing their psychological behaviors. It can identify both positive and negative users and improves sentiment classification results that may be skewed due to subjective biases in user opinions by leveraging user behavior information. Through ablation and comparison experiments, the effectiveness of UsbVisdaNet is demonstrated, achieving superior sentiment classification performance on the Yelp multimodal dataset. Our research pioneers the integration of user behavior features, text features, and image features at multiple hierarchical levels within this domain.

Developing personas for live streaming commerce platforms with user survey data.

Live streaming commerce has emerged as a novel form of online marketing that offers live streaming commerce platforms a means of meeting different user groups' needs. The objective of this article is to examine the effects of age and gender on live streaming commerce platform usage and investigate user characteristics of these platforms in China. This study adopted a data-driven persona construction method combining quantitative and qualitative methods through the use of survey and interview. The survey involved 506 participants (age range = 19-70), and the interview involved 12 participants. The survey findings showed that age significantly affected users' livestream platform usage, while gender did not. Younger users had higher device proficiency and operation numbers. With more trust and device use, older users used the platforms later in the day than younger users. Interview findings revealed that gender affected users' motivations and value focus. Women tended to use the platforms as a means of entertainment. Women valued service quality and enjoyment more, while men focused on the accuracy of product information more. Four personas with significant differences were then constructed: Dedicated, Dependent, Active and Lurker. Their various needs, motivations and behavior patterns can be considered by designers to elevate the interaction of live streaming commerce platforms.

User experience of a family health history chatbot: A quantitative analysis.

Objective: Family health history (FHx) is an important tool in assessing one's risk towards specific health conditions. However, user experience of FHx collection tools is rarely studied. ItRunsInMyFamily.com (ItRuns) was developed to assess FHx and hereditary cancer risk. This study reports a quantitative user experience analysis of ItRuns. Methods: We conducted a public health campaign in November 2019 to promote FHx collection using ItRuns. We used software telemetry to quantify abandonment and time spent on ItRuns to identify user behaviors and potential areas of improvement. Results: Of 11065 users who started the ItRuns assessment, 4305 (38.91%) reached the final step to receive recommendations about hereditary cancer risk. Highest abandonment rates were during Introduction (32.82%), Invite Friends (29.03%), and Family Cancer History (12.03%) subflows. Median time to complete the assessment was 636 seconds. Users spent the highest median time on Proband Cancer History (124.00 seconds) and Family Cancer History (119.00 seconds) subflows. Search list questions took the longest to complete (median 19.50 seconds), followed by free text email input (15.00 seconds). Conclusion: Knowledge of objective user behaviors at a large scale and factors impacting optimal user experience will help enhance the ItRuns workflow and improve future FHx collection.

Psychological antecedents of telehealth acceptance: A technology readiness perspective.

The ongoing coronavirus pandemic has been threatening the healthcare system. In this context, telehealth is a potential solution to deliver effective and safe health care to the public. To facilitate the application and acceptance of telehealth, a good understanding of psychological determinants is of great importance. Therefore, this study aims to examine the public's positive and negative mindsets towards telehealth. A theoretical model was established by employing the technology readiness model and perceived value theory. To empirically test the relationships between constructs, a total of 500 responses from residents in Singapore were collected; thereafter, structural equation modeling was performed. The results indicate that discomfort negatively impacts perceived value whereas optimism and innovativeness positively impact users' perceived value. Further, perceived value positively impacts the acceptance of telehealth via attitude. Demographic factors (i.e. internet literacy, age, education) can also influence certain aspects of technology readiness (e.g. innovativeness, optimism). Moreover, social influence is an important moderator between perceived value and the acceptance of telehealth. The empirical findings enhance the understanding of users' psychology concerning telehealth and provide policy recommendations regarding the development of telehealth to improve public health.

Generic User Behavior: A User Behavior Similarity-Based Recommendation Method.

Recommender system (RS) plays an important role in Big Data research. Its main idea is to handle huge amounts of data to accurately recommend items to users. The recommendation method is the core research content of the whole RS. However, the existing recommendation methods still have the following two shortcomings: (1) Most recommendation methods use only one kind of information about the user's interaction with items (such as Browse or Purchase), which makes it difficult to model complete user preference. (2) Most mainstream recommendation methods only consider the final consistency of recommendation (e.g., user preferences) but ignore the process consistency (e.g., user behavior), which leads to the biased final result. In this article, we propose a recommendation method based on the Entity Interaction Knowledge Graph (EIKG), which draws on the idea of collaborative filtering and innovatively uses the similarity of user behaviors to recommend items. The method first extracts fact triples containing interaction relations from relevant data sets to generate the EIKG; then embeds the entities and relations in the EIKG; finally, uses link prediction techniques to recommend items for users. The proposed method is compared with other recommendation methods on two publicly available data sets, Scholat and Lizhi, and the experimental result shows that it exceeds the state of the art in most metrics, verifying the effectiveness of the proposed method.

Exploring changes in guest preferences for Airbnb accommodation with different levels of sharing and prices: Using structural topic model.

Introduction: Online reviews have become an important source of information for investigating customers' consumption experiences in academic studies. In the context of sharing economy-based accommodation, various studies have been conducted to investigate the user experience of Airbnb by analyzing online reviews; however, most previous Airbnb studies had focused on analyzing the user experience of Airbnb at a holistic level without distinguishing the accommodation attributes of Airbnb. Therefore, this article aimed to investigate how the preferences revealed by Airbnb users in online reviews vary across Airbnb listings with different levels of sharing and price ranges. Methods: This study analyzed 181,190 online reviews under Airbnb listings in Kuala Lumpur, Malaysia, using the structural topic model (STM). Results: This study identified 21 topics related to Airbnb service and product attributes. Discussion: The findings show that Airbnb users who stay at entire property are more concerned with the hedonic value of their stay, while those who stay at shared property are more concerned with the utilitarian value. The purposes of the host-guest interaction were also found to differ between these two types of Airbnb accommodations. Regarding the effect of listing prices on users' preferences, findings reveal that those staying at lower-priced rooms were more concerned about the convenience of exploring the surrounding area, while those who stayed at higher-priced rooms were more concerned about the surrounding environment and the interior facilities of the property.

Research on the Factors Affecting the Adoption of Smart Aged-Care Products by the Aged in China: Extension Based on UTAUT Model.

With the rapid development of network technology and smart technology, smart aged-care products are becoming increasingly valued for their ability to help the aged actively cope with the challenges of aging. However, seniors face challenges in using smart aged-care products for many reasons, which reduces their willingness to adopt them. As a result, the sustainable development of smart aged-care products is constrained. This study combined the unified theory of technology acceptance and use, perceived risk theory and perceived cost theory, and reconstructed a research model that investigated the adoption of smart aged-care products by the elderly in China. Questionnaires were given to older Chinese adults in this study, and 386 valuable responses were received. The findings of the structural equation model (SEM) analysis are as follows: (1) performance expectancy, effort expectancy, and social influence were positively related to the behavioral intention of seniors to use smart aged-care products; (2) perceived cost and perceived risk were negatively related to the behavioral intention of seniors to use smart aged-care products; (3) perceived risk indirectly affected use behavior through behavioral intentions; (4) facilitating conditions did not have a significant impact on the use behavior of seniors in adopting smart aged-care products. Based on the empirical results, this study sought to improve the use behavior of the aged in relation to the adoption of smart aged-care products, and provided suggestions to improve the overall service quality and sustainability of those products.

Acceptance and use of virtual reality games: an extension of HMSAM.

Virtual reality (VR) is considered as one of the technological megatrends of 2020s, and today, VR systems are used in various settings, digital gaming being among the most popular ones. However, there has been a dearth of understanding regarding the central factors behind VR gaming acceptance and use. The present study therefore aimed to explain the factors that drive the use and acceptance of VR games. We extended the hedonic-motivation system acceptance model with utilitarian and inconvenience factors to capture the pertinent features of VR systems more holistically. We proposed a theoretical model and analyzed it through covariance-based structural equation modeling using an online survey sample of 473 VR gamers. Our findings help explain the role of different antecedents behind VR gaming acceptance and demonstrate that VR gaming is driven more by the hedonic gaming aspects than by the utilitarian health and well-being aspects of VR games, enjoyment being the strongest driver behind VR gaming intention and immersion. Moreover, findings also suggested that use intentions and immersion levels are not significantly diminished by physical discomfort and VR sickness. The findings, which potentially extend to other VR systems as well, also pose important implications for the providers of VR games. As the main contribution, based on our empirical findings, we provide a greater theoretical understanding on VR gaming acceptance and use.

Public Use of the "Your COVID Recovery" Website Designed to Help Individuals Manage Their COVID-19 Recovery: Observational Study.

BACKGROUND: At the start of the COVID-19 pandemic, unprecedented pressure was placed on health care services globally. An opportunity to alleviate this pressure was to introduce a digital health platform that provided COVID-19-related advice and helped individuals understand and manage their COVID-19 symptoms. Therefore, in July 2020, the Your COVID Recovery website was launched by the National Health Service of England with the aim of creating a practical tool that provides advice and support to individuals recovering from COVID-19. The website includes information on many of the key COVID-19 symptoms. To date, public use of the Your COVID Recovery website and user behavior remain unknown. However, this information is likely to afford insight into the impact of the website and most commonly experienced COVID-19 symptoms. OBJECTIVE: This study aimed to evaluate public use of the Your COVID Recovery website, a digital health platform that provides support to individuals recovering from COVID-19, and determine user behavior during its first year of operation. METHODS: Google Analytics software that was integrated into the Your COVID Recovery website was used to assess website use and user behavior between July 31, 2020, and July 31, 2021. Variables that were tracked included the number of users, user country of residence, traffic source, number of page views, number of session views, and mean session duration. User data were compared to COVID-19 case data downloaded from the UK government's website. RESULTS: During the study period, 2,062,394 users accessed the Your COVID Recovery website. The majority of users were located in the United Kingdom (1,265,061/2,062,394, 61.30%) and accessed the website via a search engine (1,443,057/2,062,394, 69.97%). The number of daily website users (n=15,298) peaked on January 18, 2021, during the second wave of COVID-19 in the United Kingdom. The most frequently visited pages after the home page were for the following COVID-19 symptoms: Cough (n=550,190, 12.17%), Fatigue (n=432,421, 9.56%), Musculoskeletal pain (n=406,859, 9.00%), Taste and smell (n=270,599, 5.98%), and Breathlessness (n=203,136, 4.49%). The average session duration was 1 minute 13 seconds. CONCLUSIONS: A large cohort of individuals actively sought help with their COVID-19 recovery from the website, championing the potential of this tool to target an unmet health care need. User behavior demonstrated that individuals were primarily seeking advice on how to relieve and manage COVID-19 symptoms, especially symptoms of cough, fatigue, and musculoskeletal pain. COVID-19 rehabilitation programs should use the results of this study to ensure that the program content meets the needs of the post-COVID-19 population.

Measurement data on the window opening behavior and climate in a strongly daylit office building.

The long-term measurement data presented in this article result were collected in a strongly daylit office building under real working conditions and include temperature and wind speed of the outdoor situation as well as climatic variables of the indoor space, such as temperature and relative humidity. In addition to the measurement of environmental variables, the window opening behavior was also logged. The entire data acquisition was implemented via the building control system and was performed with a one-minute resolution. An exception to this is the recording of the window openings, which were logged on change of state. The measurement data obtained can be combined with other measurement data to provide an improved data basis for energy building simulations, prediction models and energy potential assessments.

Adaptive and personalized user behavior modeling in complex event processing platforms for remote health monitoring systems.

Taking care of people who need constant care is essential and its cost is rising every day. Many intelligent remote health monitoring systems have been developed from the past till now. Intelligent systems explainability has become a necessity after the worldwide adoption of such systems, especially in the health domain to explain and justify decisions made by intelligent systems. Rule-based techniques are among the best in terms of explainability. However, there are several challenges associated with remote health monitoring systems in general and rule-based techniques, specifically. In this research, an adaptive platform based on Complex Event Processing (CEP) has been proposed for user behavior modeling to provide adaptive and personalized remote health monitoring. This system can manage a massive amount of data in real-time utilizing the CEP engine. It can also avoid human errors in setting rules thresholds by extracting thresholds from previous data using JRip rule-based classifier. Moreover, a feature selection method is proposed to decrease the high number of features while maintaining accuracy. Additionally, a rule adaption method has been proposed to cope with changes over time. Additionally, a personalized rule adaption method is proposed to address the need for responsiveness of the system to the special requirements of each user. The experimental results on both hospital and activity data sets showed that the proposed rule adaption method improves the accuracy by about 15 % compared to non-adaptive systems. Additionally, the proposed personalized rule adaption method has an accuracy improvement of about 3 % to 6 % on both mentioned datasets.