User Profiles Research Articles

Who Finds Pornography Stressful? A Latent Profile Analysis.

This study explores the connection between pornography use, sexual functioning, and mental health using latent profile analysis (LPA) to identify distinct profiles among pornography users and assess variations in mental health, including anxiety, depression, and emotion regulation. The aim of this study was to identify profiles among pornography users, specifically distinguishing those with sexual distress and sexual function problems, characterize these profiles, and assess variations in mental health. Data were collected from 463 participants through an anonymous online survey. Three distinct pornography user profiles were identified based on their consumption and sexual problems: high-frequency problematic pornography use (PPU, which is defined as uncontrollable patterns of pornography use resulting in adverse consequences and significant distress) with sexual problems, high-frequency non-PPU without/with low sexual problems, and no/low-frequency non-PPU without sexual problems. Differences among the profiles emerged in anxiety and depressive symptoms as well as emotion regulation capabilities. The high-frequency PPU with sexual problems group exhibited lower emotion regulation capabilities, while the no/low-frequency non-PPU without sexual problems group reported significantly lower depressive symptoms. This study emphasizes the multifaceted nature of pornography use, revealing that certain patterns may be more problematic. The findings highlight the interplay between emotional dysregulation, anxiety, depression, problematic pornography use, and sexual problems and distress. Recognizing these distinct profiles is crucial for understanding the relationship between pornography use and sexual and mental well-being, ultimately contributing to more targeted interventions and improved outcomes for all populations.

An Intelligent Multi-Agent System using XML for Adaptive Employment Agency Management

In recent years, e-commerce has become a significant social and cultural phenomenon. Many organizations now offer their services online. Within this context, online recruitment services play an important role in supporting individuals in job searches and assisting companies in finding personnel. Conversely, the potential that AI forecasting systems offer in distinct application fields is well known using increasingly high-performance algorithms that are particularly promising and certainly used successfully in job searches. In this field, in general, companies insert their job offers (job proposals) into a database; individuals are supported in their search for a job offer using a search engine based on classic Information Retrieval (IR) techniques. Regarding this, it is presumable that the Information Retrieval techniques currently used by recruitment services, which do not involve the use of rich user profiles, can provide a single individual with a large number of job offers, many of which are of little interest for him. This result could cause strong dissatisfaction for the user, and his renunciation of the use of such services. The Geomatics Laboratory, in the context of forecasting studies in the territorial and environmental fields, is experimenting with forecasting systems also in the business and economics fields to create a customized search engine, proposing a Recommender System developed using intelligent agents and based on XML. This system leverages detailed user profiles to assist users in personalized job offer searches, which combines classic Information Retrieval techniques with User Modeling techniques and artificial intelligence techniques. As a result, in generic domains, our system quickly achieves satisfactory results; however, it struggles to maintain this performance after many sessions. Conversely, in specialized domains, while our system requires many sessions to reach satisfactory performance initially, it consistently delivers outstanding performance once this phase is complete.

Analysis of Profiles of Supporters of Conspiracy Narratives about Vaccination Against COVID-19 on a Social Network

The article analyzes the socio-demographic characteristics and value-worldview self-positioning of the distributors of conspiracy narratives (“conspiracy theories”) about vaccination against COVID-19 on the Russian social network “VK”. The research material consists of 33,674 profiles of VK users who published at least two messages during the pandemic that explicate the stated narrative. The collection and analysis of the material was carried out using big data analysis technologies. The purpose of the study is to determine whether it is possible to identify typical socio-demographic and ideological traits of anti-vaccer conspiracy theorists, which to a certain extent explain their penchant for conspiracy thinking. The results obtained refute the marginal dominance of conspiracy theorists in a pandemic situation. Versions popular in the scientific literature about the predominance of insufficiently educated people among “conspiracy theorists”, or the elderly as more gullible, or women as more suggestible, or residents of villages and small provincial towns as more conservative, etc. are not confirmed by the large-scale empirical studies of large user data. The results of the study may be useful for developing countermeasures and strategies to address public mistrust of institutions and official trends in public health, in particular vaccination policies. The traditional focus on information and educational measures (explanations about the dangers of the disease and the benefits of the vaccine) seems to be ineffective in the context of the emergence of value systems, cognitive distortions, general socio-political tension and other reasons that are still awaiting serious study.

AnxietyNet: social media trends with fuzzy logic trees

In the digital age, social media platforms have become integral parts of daily life, facilitating communication, information sharing, and community building on a global scale. However, alongside the benefits of connectivity, concerns regarding public anxiety within these virtual communities have emerged. This research paper delves into the phenomenon of public anxiety on social media and proposes a novel approach utilizing fuzzy tree logic to analyze and address this issue. Through a comprehensive review of literature, theoretical frameworks, and practical applications, this paper aims to provide insights into the complex dynamics of public anxiety and offer potential solutions leveraging machine learning techniques. The proposed methodology involves the development of a cascading model to dynamically compute individual anxiety scores within social media networks. By analyzing structural influences and user interactions, this model aims to provide a nuanced understanding of anxiety dynamics within online communities. Additionally, a probabilistic model is designed to measure anxiety scores of social network messages, utilizing a generalized user profile. The integration of fuzzy tree logic enables the construction of a tree structure to effectively compute anxiety scores, offering a more flexible and human-like decision-making process. Preliminary experiments demonstrate the effectiveness of the proposed approach in capturing and analyzing public anxiety on social media platforms. The fuzzy tree model shows promise in accurately assessing anxiety levels and identifying key factors contributing to social anxiety within online communities. Moreover, the integration of machine learning techniques enhances the scalability and adaptability of the model, allowing for real-time analysis and proactive intervention strategies. This research underscores the importance of addressing public anxiety on social media through innovative analytical frameworks. By leveraging fuzzy tree logic and machine learning techniques, we can gain deeper insights into the dynamics of anxiety within online communities and develop targeted interventions to support individuals experiencing distress. Collaborative efforts in this endeavor hold the potential to foster societal well-being and resilience in the digital age.

Enhancing Service Efficiency and Accessibility for Persons with Disabilities: An Information Management System with SMS Notification in Libertad, Antique

This research evaluates the effectiveness of a Persons with Disabilities (PWD) Information Management System with SMS notification implemented in Libertad, Antique, Philippines. The system aims to improve service accessibility and efficiency for PWD by providing a centralized platform for information dissemination and service delivery. The study utilizes the ISO/IEC 25010 Software Quality Model and McCall's Software Quality Model to assess the system's user experience and technical aspects, respectively. The findings reveal a generally positive assessment of the system's quality, with high ratings for functional suitability, performance efficiency, compatibility, usability, and reliability. However, areas for improvement include security, maintainability, and portability. The paper concludes by discussing the benefits of the system for both the PWD population and the PWD office, highlighting the need for further development to enhance user profiling, real-time support, and data-driven enhancements. The study demonstrates the transformative potential of technology in promoting inclusion and improving the lives of individuals with disabilities.

How generative artificial intelligence portrays science: Interviewing ChatGPT from the perspective of different audience segments.

Generative artificial intelligence in general and ChatGPT in particular have risen in importance. ChatGPT is widely known and used increasingly as an information source for different topics, including science. It is therefore relevant to examine how ChatGPT portrays science and science-related issues. Research on this question is lacking, however. Hence, we simulate "interviews" with ChatGPT and reconstruct how it presents science, science communication, scientific misbehavior, and controversial scientific issues. Combining qualitative and quantitative content analysis, we find that, generally, ChatGPT portrays science largely as the STEM disciplines, in a positivist-empiricist way and a positive light. When comparing ChatGPT's responses to different simulated user profiles and responses from the GPT-3.5 and GPT-4 versions, we find similarities in that the scientific consensus on questions such as climate change, COVID-19 vaccinations, or astrology is consistently conveyed across them. Beyond these similarities in substance, however, pronounced differences are found in the personalization of responses to different user profiles and between GPT-3.5 and GPT-4.

User profile visualisation for privacy awareness on Geo-Social Networks

ABSTRACT Geo-Social Networks (GeoSNs) enable user interaction by sharing personal location and contextual information, leading to the accumulation of large databases of users’ physical presence and experiences in geographic places. These data form the basis of building (geo)graphic profiles for users on GeoSNs. Users’ awareness of the data they share is limited. Without active reminders of the contents of their geoprofiles, users may fail to recognise possible privacy risks associated with their location sharing behaviour. In this work, we argue that users’ awareness on GeoSNs should consider the spatial, temporal, and social dimensions of the data, as well as their combinations. We propose a basic strategy for representing and visualizing geoprofiles that aggregates the interaction of users in space over time to summarise the degree of relatedness of users to place and to other users. We conducted a user study with 26 participants to assess the perception of their geoprofiles on a typical GeoSN against the proposed approach. Results demonstrate the potential value of the proposal for improving user awareness of their data and privacy implications, compared to basic search and retrieval functions offered by typical GeoSNs. This work highlights the need for considering and improving user awareness on these platforms.

Flavored Tobacco User Characteristics in U.S. young Adults: Wave 5 of the Population Assessment of Tobacco and Health Study

Introduction: Flavorings are one of the major appealing factors of current tobacco products, especially for young adults. They constantly evolve, and new flavorings are introduced to the market every year, but little is known about user profiles of flavored tobacco products (FTPs). Methods: We analyzed young adult (ages 18–24) tobacco product users (N = 4,707) from Wave 5 of the Population Assessment of Tobacco and Health Study. We applied bivariate analyses to compare individuals who use FTP and non-FTPs by different flavor types (i.e., menthol/mint, fruit, candy/sweets, and tobacco). Parameter estimates were weighted by applying the sample and replicate weights for the complex study design to produce population-representative estimates. Results: Sixty percent of the current combustible tobacco users and 90% of electronic nicotine delivery systems (ENDS) users were FTP users. FTP users were more likely to be multiple product users than non-FTP users. Being non-Hispanic White, drinking alcohol in the past year, experiencing internalizing problems in the past month, and having tobacco dependence were associated with FTP use. Flavored ENDS users had different sociodemographic characteristics by flavor types but did not differ significantly in vaping-related behaviors (e.g., frequency). Also, preferred flavors differed among tobacco user groups (i.e., ENDS, hookah, and cigars). Conclusions: FTPs appear to be gaining popularity among young adults and tend to show co-use with multiple tobacco products and other substances. Future studies are warranted to monitor the risk factors of young adult FTP use, especially in the context of flavor ban on ENDS products.

Rancang Bangun E-Library Berbasis Web pada SMP Negeri 2 Wewewa Tengah

At every educational institution there is a library for students to use at that institution. Likewise with SMP Negeri 2 Wewewa Tengah. At the school there is a library but it still uses a bookkeeping management system or is still written manually, while the number of active borrowers is almost all students carrying out the borrowing process at the school. The process of borrowing books in the library begins when students come to the library and tell the library staff that they want to borrow books. Then students select the books they want to borrow on the shelves provided. Next, the library staff records the borrower's data, such as full name, student identification number, and class, as well as data on books borrowed, such as book title, author, and inventory number in the ledger used as a borrowing register.). The school library has a strategic role and function in developing the potential of students from the entire academic community in the school environment. The library is part of the learning resources that every school has, a library that is still manual is something that needs to be created a system that can help with administrative activities. With the e-library, users can access it together with other users without having to wait in line. The search process is easier and faster because it is assisted by an information search engine that uses a database. This system presents a variant of the digital library, including a user page section that presents books available at the Central Wewewa State library

User experience of the Written Exam Question Quality tool to inform the writing of new written-exam questions

Background: Creating new written-exam questions is a burdensome task for faculty members. While several guidelines exist, there had not been a previous attempt to streamline them in a user-friendly tool. We created the Written Exam Question Quality tool (WEQQ) and explored potential users’ perception of this tool when writing their exam questions. Methods: We conducted a descriptive study to explore how four Canadian faculty members used the WEQQ. We conducted structured interviews that were analyzed within and across participants to understand the latter’s perceived usefulness and acceptability of the WEQQ. Quantitative data from a short questionnaire on creating exam questions and their psychometric properties were also collected. Results and conclusion: Participants’ perception of the WEQQ was positive, and they were favorable to its use. The WEQQ seemed to represent a user-friendly, easy way to help faculty members in creating multiple-choice or short-answer questions. Time on task remained the same when using the WEQQ. We were able to identify two user profiles, passive and active, which indicated how faculty members use the WEQQ to create exam questions. Future steps would be to further investigate if the WEQQ can increase the quality of written-exam questions and to understand how to promote an active use of the WEQQ when implementing this tool.

Investigating Older Adults' Use of a Socially Assistive Robot via Time Series Clustering and User Profiling: Descriptive Analysis Study.

The aging population and the shortage of geriatric care workers are major global concerns. Socially assistive robots (SARs) have the potential to address these issues, but developing SARs for various types of users is still in its infancy. This study aims to examine the characteristics and use patterns of SARs. This study analyzed log data from 64 older adults who used a SAR called Hyodol for 60 days to understand use patterns and their relationship with user characteristics. Data on user interactions, robot-assisted content use, demographics, physical and mental health, and lifestyle were collected. Time series clustering was used to group users based on use patterns, followed by profiling analysis to relate these patterns to user characteristics. Overall, 4 time series clusters were created based on use patterns: helpers, friends, short-term users, and long-term users. Time series and profiling analyses revealed distinct patterns for each group. We found that older adults use SARs differently based on factors beyond demographics and health. This study demonstrates a data-driven approach to understanding user needs, and the findings can help tailor SAR interventions for specific user groups. This study extends our understanding of the factors associated with the long-term use of SARs for geriatric care and makes methodological contributions.

Recommendation systems with user and item profiles based on symbolic modal data

Recommendation systems with user and item profiles based on symbolic modal data

Patterns of Use and Withdrawal Syndrome in Dual Cannabis and Tobacco Users (DuCATA_GAM-CAT): Protocol for a Mixed Methods Study.

Approximately 1 in 6 cannabis users develop a cannabis use disorder (CUD) and the odds increase to 1 in 2 for daily users. The Dual use of Cannabis and Tobacco Monitoreing through a Gamified Web app (DuCATA_GAM-CaT) project aims to identify cannabis-tobacco patterns of use and withdrawal symptoms among individuals with CUD who are attending substance abuse programs. The project uses a mixed methods approach consisting of 3 studies. First, a participatory qualitative study involves focus groups comprising individuals with CUD, clinicians, project researchers, and an expert gamification company to co-design a gamified web app. Second, a longitudinal prospective study to follow up individuals over 6 weeks with CUD attending substance abuse programs . Participants report their cannabis-tobacco usage patterns, type and frequency of tobacco use, nicotine dependence, withdrawal symptoms, psychoemotional factors, and motivation to quit both substances. Predictive analysis techniques are used to analyze clinical, demographic, psychological, and environmental data to predict the probability of achieving abstinence. Third, homogeneous focus groups to explore participants' experiences during their CUD treatment. By June 2024, the project had completed the first study, defining eligible cannabis user profiles, developed the initial web app prototype, and initiated recruitment across 10 centers, with 74 participants enrolled, aiming to reach 150 participants in total. All participants are required to provide informed consent, and their information is kept confidential and anonymized following confidentiality rules. The research team is committed to disseminating the results obtained to professional and patient groups, as well as informing public health agents, to positively influence political and social decision makers and design programmers. Additionally, we aim to prioritize the publication of the results in high-impact journals specialized in drug abuse, public health, and health care services research. ClinicalTrials.gov NCT05512091; https://clinicaltrials.gov/study/NCT05512091. DERR1-10.2196/58335.

Considerations on sentiment of social network posts as a feature of destructive impacts

Nowadays, people spend a lot of time in the information space, communicating within various social platforms. Content of those platforms can influence people’s feelings and personalities, which is especially relevant for young people. In this research, we made an attempt to prove this hypothesis. For the experiment, we selected the VKontakte social network and analysed users profiles together with the results of the psychological tests passed by them. The goal of the experiment was to find correlations between the information provided within the social network communities and the users’ personalities. Moreover, in this paper, we made an attempt to enhance the results of the classifier accuracy using the sentiment analysis. The experiments were conducted to test the sentiment analysis models, to analyse the proposed feature based on posts’ sentiment, and test the classifier for the detection of the potentially destructive impacts. The analysis of the correlation of the proposed feature with the communities that have potentially destructive impacts on anxiety is conducted. The analysis of the obtained results is provided. During the experiments, the authors found out that consideration of the posts’ sentiment allows increasing accuracy of the classifier for anxiety destructive impacts on 12.24 %. Additionally, we analysed the relationship between the user sentiments metric and destructiveness. We confirmed that the assessment of the user’s posts’ sentiment can be used to compile his psychological characteristics and determine possibility of destructiveness.

User profiles in digitalized healthcare: active, potential, and rejecting — a cross-sectional study using latent class analysis

BackgroundThere is evidence of different use by different groups of people for general health-related applications. Yet, these findings are lacking for digitalized healthcare services. It is also unclear whether typical use patterns can be found and how user types can be characterized.MethodsThe analyses are based on data from 1 821 respondents to the Health Related Beliefs and Health Care Experiences in Germany panel (HeReCa). Digitalized healthcare services, that were used to determine the user types, include for example sick notes before/after examination and disease related training. User types were determined by latent class analysis. Individual groups were characterized using multinomial logistic regressions, taking into account socioeconomic and demographic factors as well as individual attitudes towards digitalization in the healthcare system.ResultsThree types were identified: rejecting (27.9%), potential (53.8%) and active (18.3%). Active participants were less likely to be employed, less likely to be highly educated and less skeptical of digital technologies. Potential users were the youngest, most highly-educated and most frequently employed group, with less skepticism than those who rejected. Rejecters were the oldest group, more likely to be female and of higher socio-economic status.ConclusionsSocio-demographic and socio-economic differences were identified among three user types. It can therefore be assumed that not all population groups will benefit from the trend towards digitalization in healthcare. Steps should be taken to enhance access to innovations and ensure that everyone benefits from them.

Use and transformation of beaches as a tourism resource by promoters and managers in oceanic islands. A conflict for geoheritage conservation and social preferences in the canary islands

In the case of the Canary Islands (Spain), human occupation on beaches has generated environmental and landscape transformations and a loss of their geoheritage values. In this sense, this archipelago could be a good example to demonstrate the paradigm at the regional scale at first and island scale at second, that the colour and composition of the beaches are determinant for urban-tourist development. The beaches with the colour and composition that managers and promoters prefer most are those that have withstood the greatest anthropogenic pressure, and may lead to socio-environmental conflicts. For this, the aim of this work is to characterize and identify beach typologies according to their sedimentological characteristics, the degree of urban-tourist occupation, and the environmental changes and artificialisation that have taken place over the last sixty years in the beaches of La Palma, Tenerife and Fuerteventura. For this purpose, the General Catalogue of Beaches and Maritime Bathing Areas (the latter were excluded from the study) issued by the Canary Islands Government was used. This source was spatially and statistically analysed using a Geographic Information System (GIS) and related to socio-environmental variables extracted from geo-referenced sources. Orthophotos from 1957 and more recent years were also used for the historical characterization. Finally, this information was contrasted with social preferences, obtained from online surveys, about the physical configuration of Canarian beaches. Results show that the quantity and distribution of beach types (grain size and beach colour) differ for each island, and that beach use depends on its typology, with sandy beaches being the most used as an urban-tourist resource and pebble-cobble beaches the most transformed to respond to sand beach user demand. These and other factors that have affected the evolution of these beaches and the degree of conservation of their natural functions and geoheritage values are discussed. The responses to the survey show significant patterns related to user profile and preferences regarding the beach environment and recreational services. The importance of social preferences and beach transformation is discussed to understand the evolution of beach management in the Canary Islands and the identified conflicts.

GPT Alumni AI Pesquisa: A Practical Tutorial for the Adoption and Ethical Use of AI in Scientific Research

Objective: This tutorial introduces the use of Alumni AI Pesquisa, a chatbot based on GPT-4, publicly available on the OpenAI platform, designed to support the ethical and transparent use of Artificial Intelligence (AI) in academic research. The aim is to guide authors, editors, and reviewers in the responsible application of AI, ensuring scientific integrity throughout the editorial process. Method: The tutorial provides a step-by-step guide on how to access and utilize Alumni AI Pesquisa. The GPT-4 offers guidance on the ethical use of AI in academic writing, the oversight of editorial decisions assisted by AI, as well as compliance with editorial guidelines and manuscript formatting. Correct usage of AI is promoted through practical examples and references to global best practices. Results: Alumni AI Pesquisa provides immediate support tailored to different user profiles (authors, editors, and reviewers), encouraging transparent AI use in manuscripts. The tool ensures that AI-assisted decisions are validated by human supervisors, guaranteeing adherence to ethical and technical standards. Conclusions: Publicly available on the OpenAI platform, Alumni AI Pesquisa, powered by GPT-4, significantly contributes to the promotion of scientific integrity by facilitating responsible AI use in the academic environment. It is recommended that the use of the tool be cited in publications, explicitly mentioning the version (GPT-4).

FP5.8 - Bridging the Trust Divide: How Medical AI Outperforms yet Battles to Win Public Confidence - A Study Conducted Among 74 UK Residents

Abstract Background AI is being incorporated into healthcare by major tech companies, but public acceptance remains challenging. The study aims to understand resistance to AI despite its increasing accuracy and potential to improve patient waiting times. Method In partnership with the University of Hull and the Academic Health Science Network (AHSN) in the UK, a study was conducted that involved 111 adult patients or carers. 9 did not respond while 28 demonstrated poor digital literacy and therefore excluded. An interactive user page was designed for the remaining users (n=74) to engage with medical chatbot. The participants were asked whether participants would use an AI-powered medical chatbot or nurse for triage at their nearest A&E. Unstructured open-ended interviews were conducted to understand the participants' reasoning behind their answers. Finding Participants ranged in age from 21 to 74, with a slight female majority (40 female vs 34 male). The majority, 79.8% (n=59) respondents, expressed their comfort with a nurse-led care while a mere 20.2% (n=15) showed readiness to interact with a medical chatbot. Notably, the age group of 20-40 was most open to the idea of consulting a chatbot. The four primary objections were: 90.5% (n = 67) believed that the chatbot fails to justify its decisions, 59.5% (n = 44) doubt its accuracy, 78.4% (n = 58) felt that the chatbot was inflexible, and 79.7% (n = 59) found it unemotional and detached. Conclusion Effective treatment relies on trust, respect and understanding, crucial elements needed when incorporating AI in medicine. It should be introduced progressively, considering patients' emotions and societal circumstances.

A novel probabilistic load shifting approach for demand side management of residential users

Demand side management is a beneficial set of techniques which leads improving consumption profile of residential users. Practice of dynamic pricing and appropriate shifting of electrical appliances at home are important tasks at that point. In this study, a novel probabilistic load shifting strategy is proposed and its beneficial effects on economic and technical parameters are demonstrated. For that purpose, a survey on energy consumption is applied for residential users and its results are used for creating a probabilistic model. 300 virtual residents are created in this way and a probabilistic simulation technique is developed. Next, the developed simulation technique is applied for creation of the monthly consumption data. Finally, it is demonstrated that electricity bills are decreased significantly when the proposed approach is applied. Besides that, peak-to-average ratio which is calculated by considering all residential users is also decreased as a result. In conclusion, when the results are compared with the ones of conventional constant tariff, time-of-use tariff with three time zones and a deterministic load shifting strategy, it is observed that the best results of economic and technical parameters in question are achieved by using the proposed probabilistic load shifting strategy together with dynamic pricing mechanism involved.

Themes and sentiments in conversations about food waste on Twitter: Proposal of a framework using neural topic modeling

There is growing research interest in the role of social media in the food industry, and its potential has yet to be fully exploited. This study extracted and analyzed data from Twitter, now X, (2018–2023) using relevant keywords related to food waste (FW), identified frequently discussed topics, and determined the sentiments involved in tweets. With the aid of neural topic modeling techniques, this study found that the main topics discussed in this social network include “Food and Garden Waste Composting and Recycling,” “Global Food Waste and Management,” “Reducing Food Waste & Saving Money,” “Single-Use Plastic and Waste Management,” and “Creative Uses of Leftovers in Meals”. Some of the subject categories often discussed are FW Environmental concerns, FW events, Leftovers management, and FW household management. Most subject categories are associated with positive sentiments, which are associated with topics like tips and recommendations for reducing FW. Negative sentiments are related to discussions around the environmental and social impacts of FW. Our study found a gap in knowledge and awareness of food recycling practices. Our study extends previous research because the analyzed messages are not restricted to specific geographical regions. Moreover, our study also used a more varied dataset in terms of user profiles and employed a search string that comprised a more varied set of terms, making our search more thorough. A comprehensive framework of discussed topics and associated sentiments is proposed. Our study has crucial implications for the design of FW reduction campaigns and interventions in social media.