Social Network Profiles Research Articles

Data on librarians' perceptions of participation in a citizen science project in a network of public libraries

Citizen Science (CS) is an approach to scientific work and part of the Open Science movement. This study aims to analyse the perception of the librarians about their participation in the aBEIRAr project, which is a CS partnership for the valorisation of the territory developed in the Intermunicipal Network of Libraries of Beiras and Serra da Estrela (RIBBSE) in Portugal. The methodology comprised a literature review, and the case study includes an interview and a survey. Of the results obtained, the following stand out: the libraries are the driving forces behind the aBEIRAr project; they choose the themes, organise and dynamize the activities in their local communities, and establish various partnerships with the mediation of the project's scientific coordination; the level of satisfaction of the librarians in this project is very satisfactory; in the libraries, after carrying out the aBEIRAr project, the number of participants in other face-to-face activities and the interaction on their social network profiles increased; librarians consider that CS can bring to public libraries and their users participative scientific knowledge. The data provides valuable insights into the possibilities and challenges associated with executing CS projects in collaboration with public libraries. These findings contribute to the ongoing discussion about the role of libraries as essential community centers.

Smile variation leaks personality and increases the accuracy of interpersonal judgments.

People ubiquitously smile during brief interactions and first encounters, and when posing for photos used for virtual dating, social networking, and professional profiles. Yet not all smiles are the same: subtle individual differences emerge in how people display this nonverbal facial expression. We hypothesized that idiosyncrasies in people's smiles can reveal aspects of their personality and guide the personality judgments made by observers, thus enabling a smiling face to serve as a valuable tool in making more precise inferences about an individual's personality. Study 1 (N = 303) supported the hypothesis that smile variation reveals personality, and identified the facial-muscle activations responsible for this leakage. Study 2 (N = 987) found that observers use the subtle distinctions in smiles to guide their personality judgments, consequently forming slightly more accurate judgments of smiling faces than neutral ones. Smiles thus encode traces of personality traits, which perceivers utilize as valid cues of those traits.

Hybrid Filtering and Probabilistic Techniques for Privacy-Preserving Community Detection in OSNs

Online Social Networks (OSNs) face the major challenge of protecting participant's privacy, due to the high dimensionality and volume of the data. In real-time social networks, where hundreds of personal details of people are shared every day, there remains a significant threat to privacy. Privacy preservation is challenging for a community detection problem due to the high computational complexity and memory requirements, especially in larger real-world OSN graphs. Although weighted nodes provide better results, as they allow capturing the frequencies of the values, the privacy preservation of sensitive attributes such as specific profiles becomes harder compared to these models. This problem can result in queries and subsequent learnings from social network profiles of specific individuals, which may be of personal, political or otherwise concern. Online social networks (OSNs) grapple with significant privacy challenges due to the extensive dimensions and vast quantities of data involved. This research fills the void in current privacy-preserving community detection methodologies, which face problems in computational complexity and memory usage in large-scale OSNs. The proposed framework seeks to bolster privacy preservation through a comprehensive multi-step process. Data filtering uses a blended data filter system to remove outliers and irrelevant data, thus enhancing the quality of the input data. Density-Oriented Clustering phase employs a density-oriented clustering model to identify communities, with each cluster representing a distinct community. Privacy Preservation component introduces a new privacy preservation technique for sensitive OSN attributes, surpassing existing k-anonymization methods. The developed density-based social network community detection model and its novel privacy-preserving scheme are evaluated using the datasets Yelp, Football, Zachary and Dolphin from the SNAP dataset. Experimental results on these datasets embed a comprehensive evaluation based on the order of each node and the graph-based networks, where each node is laden with weights as proximity values, indicating the semantic proximity between communities and individuals. The proposed framework employs the normalized mutual information (NMI), modularity (Q), Rand index and runtime measurements to demonstrate widespread advantages in the multi-dimensional functional space, including greater accuracy, cluster compatibility, and computational tractability over the existing prominent traditional models for OSNs.

Blockchain Enabled Social Network for Detecting Fake Accounts

Front end technology with permanent accounts is employed in online social networks to help people get to know one another. In an effort to maintain connection with everyone, Facebook and Twitter are changing along with the users. OSNs are used by them for planning events, news exchange, interpersonal communication, and even running their own online enterprises. Because of OSNs' quick growth and the abundance of personal data that its users have shared, attackers and imposters have been lured to them with the intention of stealing personal information, spreading disruptive activities, and publishing false information. A further rationale for creating fictitious profiles for hateful accounts. In an attempt to harm the reputation of the person they detest, these users make profiles using their usernames and publish photographs and articles that aren't related to them. to use blockchain technology, Aadhar number verification, and SVM profile classification to create a reliable solution for detecting phoney social network profiles. By analysing several aspects of social media profiles, the SVM classification uses machine learning to discern between real and fraudulent accounts. By verifying Aadhar numbers through legitimate channels, Aadhar number verification is integrated into the registration process, guaranteeing the authenticity of user identities. A decentralised and secure identity management system is made possible by the use of blockchain technology, with smart contracts providing immutable storage for verified user data.

Promotion of freelance services in the digital environment

The article analyses the concept of freelance (temporary, project employment) within the framework of new approaches to employment and changes in the perception of remote and hybrid forms of employment by society and business after the COVID-19 pandemic. The following segments of the labour market where freelancing is the most common element are considered: copywriting, law, software development, design of buildings and structures, creation of graphic, audio and video content, consulting, provision of expertise, tutoring. The portrait of a freelancer is studied in detail according to such characteristics as the region of residence, sex, average age, the share of “real” freelancers and the share of part-timers, experience of interaction with freelance platforms, tax status, single and team players, the reasons for choosing freelancing as a form of employment, the most valuable component of freelancing, the disadvantages of freelancing as a form of employment and motivation of freelancers. The main methods of promoting freelance services in the digital environment are identified: a portfolio site, active maintenance of a professional profile in social networks and its advertising, registration on aggregator sites, freelance and human-cloud platforms. The main element of the portfolio site is highlighted – the works performed by a freelancer (texts, photographs, graphics and similar materials) – along with the key factors that a freelancer needs to consider when creating and maintaining a portfolio site and the most common mistakes

Childlessness, Social Network Profiles in Midlife and Late Adulthood, and Their Implications for Subjective Well-Being.

Despite the rising prevalence of individuals reaching advanced age without children, little is known about the diversity of support networks within childless populations. We examine the network profiles of childless adults aged 50+ in Singapore, which observes high childlessness rates despite societal emphasis on familism. We employ latent class analysis to derive network typology based on a 2022 nationwide survey in Singapore. Additionally, we use logistic regression analyses to investigate the sociodemographic correlates of childless individuals' network types and the associations between these network types and subjective well-being. Childless Singaporeans form a heterogeneous group characterized by different support networks. Evidence suggests the centrality of parents in the childless' social networks and the continuity of parent-child support exchanges that extend into the child's midlife and late adulthood. When parents are absent, siblings/extended kin serve as their support sources. Age, sibship size, and socioeconomic status are key correlates of network types. Membership in diverse networks is beneficial to the subjective well-being of childless individuals. Although one-fifth of childless individuals in restricted networks demonstrate significantly poorer well-being, the remaining four-fifths show comparable, if not better, well-being than the non-childless. Results underscore the importance of differentiating network types among the childless, particularly when assessing their well-being. Contrary to the notion of associating later-life childlessness with social isolation and vulnerabilities, many childless Singaporeans manage to construct non-child-based networks equipped with various supportive relations that cater to their needs. Nevertheless, persistent vulnerabilities among restricted network members deserve policymakers' attention.

Unmasking Deceptive Profile in Social Network using Machine Learning

Social media platforms have become a regular aspect of our lives, acting as mediums for networking, information sharing, and communication. However, the proliferation of deceptive profiles poses a significant challenge to the authenticity and integrity of these platforms. Deceptive profiles can be utilized for various malicious purposes such as spreading misinformation, engaging in cyberbullying, or conducting fraudulent schemes. In this research paper, we propose a comprehensive approach for distinguishing between genuine and fake anonymous profiles on social media platforms using machine learning (ML) and natural language processing (NLP). We investigate a range of features and methodologies, including behavioral patterns, sentiment analysis, and profile completeness, also use ML algorithms to effectively discern between genuine and fake profiles. Experimental results obtained from real-world datasets demonstrate the efficacy and scalability of our proposed approach. Moreover, we discuss the implications of fake profiles on social media and propose strategies for mitigation and prevention. Key Words: Deceptive Profile Identification, Machine Learning, Natural Language Processing, Behavioral Patterns, Sentiment Analysis, Profile Completeness.

Projeto Jovem Doutor Quixadá: O protagonismo dos estudantes de medicina durante a extensão promotora de saúde

Introduction: Adolescence is a phase of life full of emotional, cognitive, social and hormonal transformations, as well as changes in affective relationships between young people and their experience of sexuality. In view of this, connecting education and health is essential for sharing information, identifying users' health needs and fostering individuals' autonomy, so that their choices can be more appropriate, as well as reducing the distance between health professionals and the population. Methodology: This is a descriptive experience report from an extension group, whose main audience was students at a full-time secondary school in the municipality of Quixadá, Ceará. The interventions took place from August 2023 to October 2023. The actions involved an educational blitz, lectures and active methodologies on various topics. In addition, an Instagram social network profile was created to publicize the project's actions and educational-formative content, which enabled a greater reach. The actions were divided into five themes: Educational blitz on the culture of peace; Bullying and Fake news; Negative impacts of alcohol and drug use in adolescence; Mental health; STIs in adolescence. Result: The actions on bullying and the Yellow September action began with a brief explanation of the subject, which helped the students to learn about the topic and those who already knew about it to reaffirm the concept. Later, at the end of the explanation, the students were able to ask questions and share experiences, which made the action more interactive, enriched the debate and enabled a mutual exchange of knowledge. On the other hand, the educational blitz on the culture of peace in schools took a different approach, using pamphlets with the numbers of violence among the public and its impact on the future, so that the young people approached were impacted and recognized the importance of implementing a culture of peace in schools. Finally, the project helped to strengthen the school-university relationship by improving the quality of life of young people, breaking down barriers in the classroom, especially when it came to mental health.

Older adults’ mental health during the COVID-19 pandemic: The association with social networks

This study examined the impact of different social networks on the mental health outcomes of older adults during the COVID-19 pandemic. Participants were 25,534 older adults from the Survey of Health, Ageing and Retirement in Europe (SHARE). The study identified five social network profiles (family, friends, spouse, diverse, others) and a “no network” group. Findings showed that, compared to the no network profile, those with spouse and family profiles are more protected against depression and loneliness during the COVID-19. In turn, individuals within friends and diverse profiles had a similar likelihood of feeling depressed, anxious, and lonely. Friends and diverse profiles had higher likelihood of suffering more anxious or lonelier than before the COVID-19 compared to the no network profile. The study further discusses possible explanations about why these profiles, which typically served as mental health protectors, were significantly affected by the unique circumstances of the COVID-19 pandemic.

Конфігурації особистого, публічного та професійного в персональних профілях користувачів фейсбуку: контент-аналіз

Background. Social media have become powerful platforms for self-representation and self-presentation. Researchers note that social networks blur the conventional boundaries between personal and public issues and spheres of life. Empirical analysis of personal profiles will help understand what issues (personal, public, or professional) and in what configurations users post on their social networks. The study aims to analyze the posts' meta-themes, types, and general patterns of configurations in 15 personal profiles of the Ukrainian Facebook space. Methods. Content analysis of 50 consecutive posts from 15 profiles of users of different ages, professions, and publicity was applied. Posts have been categorized by meta-themes (personal, professional, public) and types defined in the process of analysis (including dissemination of information about one's project, posts for memorable dates, diary accounting, diary reflection, meme, quote, opinion writing, etc). Results. Differences between the strategies of personal profile usage by public and non-public individuals were found. In particular, non-public users more often focus on personal topics, while public ones focus on professional ones. Popular types of posts were identified, such as diary accounting, diary reflecting, mapping of one's network, and project announcements. It was found that promoting one's own projects, journalism, and essay writing, as well as crowdfunding campaigns, are more common among public figures, while diary reflection, sharing photos, and memes are more prevalent among non-public users. Personal profiles in social networks perform a number of functions, such as being counterparts of live communication and self-presentation, spaces for opinion leadership, and involvement in collective events. Despite the proven diversity of profile management, each user demonstrates integrity and consistency in their approach. Conclusions. Unlike institutions and spaces of modernity, contemporary social networks combine different spheres of life (personal, professional, and public) in one space – in a single personal profile and even a single post. This creates both new opportunities for self-(re)presentation and challenges. Further empirical research involving qualitative methods is relevant for a deeper understanding motivation of users and their perception of the profiles.

Turiyam Based Four Way Unknown Profile Characterization on Social Networks

Recently Turiyam set and its algebra is introduced for dealing with the data containing human quantum cognition. This paper tries to extend the applications of Turiyam set while profile characterization at Social Network. To achieve this goal, a method is proposed for characterization of social network profiles in true regions (t), false region (f), Neutro region (i) and Liberal or Turiyam region (l), independently. The proposed method also provides implementation to understand the concept of Turiyam logic. In addition, many real life examples for data with Turiyam attributes is given with its four way characterization.

Avatar legal protection as an atypical copyright object

The legal protection of an avatar as an atypical copyright object has been investigated. The originality and objective form of an avatar have been established and characterised, which will allow correlating an avatar with atypical copyright objects, and the atypicality of an avatar as an object of copyright has been emphasised.
 It has been determined that the phenomenon of an avatar as a legal category is that it is a complex category which has a different legal regime. It has been noted that it is most appropriate to relate an avatar to an object of rights, in particular, in the field of personal non-property rights and intellectual property rights. Creating objects with the help of a neural network, in particular, an avatar in the form of a computer copy of a person, is a complex work of different people. At the same time, this work has signs of intellectual, scientific and creative activity. Due to the complexity of such activity, it may fall under different legal regimes of intellectual property rights, in particular copyright and patent law. An avatar as a game character can be considered as an object of copyright. If an avatar is created using digital tools offered by the developers of a computer game, the copyright belongs to the respective developers.
 It has been proved that an avatar is an intangible benefit that can include such virtual objects as: a game character tied to a specific user; a social network profile of a person; a computer copy of a person with unique identification features, originality and objective form. It has the following characteristics: the presence of a digital objective form that makes it possible to perceive the avatar in the digital environment, originality, uniqueness, and the availability of legal access to use the avatar. The legal protection of an avatar within the framework of copyright may be carried out under two legal regimes: copyright protection; and the institute of rights of a special kind (sui generis). It would be appropriate to supplement the Law of Ukraine “On Copyright and Related Rights” with a definition of an avatar and rights to it.

QST-ClustNet: Quantum Inspired Sooty Tern Optimization for Clustering User Profiles in Social Network

QST-ClustNet: Quantum Inspired Sooty Tern Optimization for Clustering User Profiles in Social Network

Marijuana Use and Perceptions of Employment Suitability

Abstract: The present study extends research on marijuana social acceptance and employment suitability in the United States by examining how hiring managers view substance use-related content in social networking profiles. Specifically, this research focused on the weight hiring managers placed on social networking posts containing recreational marijuana content, medicinal marijuana content, and alcohol content when assessing potential candidates. With a sample of 405 hiring managers who evaluated experimentally manipulated social networking profiles, the results demonstrated a modest negative hiring bias against recreational marijuana content, with stronger negative effects for alcohol content. At the same time, social networking content related to medicinal marijuana content did not have a significant effect on perceptions of employment suitability. These findings highlight the nuanced nature of substance use stigma in today’s evolving society.

Automatic Identification of Fake Profiles in Online Social Networks

In the modern era, the social life of individuals has shifted towards becoming connected with online informal communities. These communities have revolutionized the way we pursue our social life, making it easier to connect with them and keep up with their updates. However, with the rapid growth of these communities, there has been a rise in issues such as false profiles and online bullying. There is currently no practical solution to address these issues. To address this issue, we have developed a system with which the programmed identification of false profiles is feasible and efficient. This system uses order methods such as support vector machine, nave bayes and decision trees to classify the profiles into false or true classes. As this is a software-based recognition method, it can be easily connected to online informal organizations that have a large number of profiles whose profiles cannot be physically inspected. In today’s world, users are bombarded with unnecessary information during surfing, which are posted by false users. Research has shown that 20% to 40% of profiles in social networks like Facebook are fake profiles. This detection of fake profiles leads to solution using frameworks. Keywords: Online Social Network, Fake Profiles, Classification, Natural Language Processing.

Digital reality in Compulsary Secondary Education: uses, purposes and profiles in social networks

ABSTRACT Daily lives of adolescents have changed significantly with the incorporation of social networks. Therefore, this quantitative study aims to analyze which social networks they use, to find out the predominant type of use they make of them and to explore their connectivity profile. To carry out the study, a sample of 850 Spanish students in Compulsory Secondary Education was analyzed. The results show that adolescents spend an excessive number of hours on social networks, with little parental supervision. They mainly use WhatsApp, Instagram and YouTube for social purposes. It is concluded that adolescents in Compulsory Secondary Education, outside school hours, and with their smartphones, spend a large part of their day on social networks, suppressing other kind of activity.

An Enhanced Scammer Detection Model for Online Social Network Frauds Using Machine Learning

The prevalence of online social networking increase in the risk of social network scams or fraud. Scammers often create fake profiles to trick unsuspecting users into fraudulent activities. Therefore, it is important to be able to identify these scammer profiles and prevent fraud such as dating scams, compromised accounts, and fake profiles. This study proposes an enhanced scammer detection model that utilizes user profile attributes and images to identify scammer profiles in online social networks. The approach involves preprocessing user profile data, extracting features, and machine learning algorithms for classification. The system was tested on a dataset created specifically for this study and was found to have an accuracy rate of 94.50% with low false-positive rates. The proposed approach aims to detect scammer profiles early on to prevent online social network fraud and ensure a safer environment for society and women’s safety.

Models and methods for analyzing complex networks and social network structures

Objectives. The study aimed to investigate contemporary models, methods, and tools used for analyzing complex social network structures, both on the basis of ready-made solutions in the form of services and software, as well as proprietary applications developed using the Python programming language. Such studies make it possible not only to predict the dynamics of social processes (changes in social attitudes), but also to identify trends in socioeconomic development by monitoring users’ opinions on important economic and social issues, both at the level of individual territorial entities (for example, districts, settlements of small towns, etc.) and wider regions.Methods. Dynamic models and stochastic dynamics analysis methods, which take into account the possibility of self-organization and the presence of memory, are used along with user deanonymization methods and recommendation systems, as well as statistical methods for analyzing profiles in social networks. Numerical modeling methods for analyzing complex networks and processes occurring in them are considered and described in detail. Special attention is paid to data processing in complex network structures using the Python language and its various available libraries.Results. The specifics of the tasks to be solved in the study of complex network structures and their interdisciplinarity associated with the use of methods of system analysis are described in terms of the theory of complex networks, text analytics, and computational linguistics. In particular, the dynamic models of processes observed in complex social network systems, as well as the structural characteristics of such networks and their relationship with the observed dynamic processes including using the theory of constructing dynamic graphs are studied. The use of neural networks to predict the evolution of dynamic processes and structure of complex social systems is investigated. When creating models describing the observed processes, attention is focused on the use of computational linguistics methods to extract knowledge from text messages of users of social networks.Conclusions. Network analysis can be used to structure models of interaction between social units: people, collectives, organizations, etc. Compared with other methods, the network approach has the undeniable advantage of operating with data at different levels of research to ensure its continuity. Since communication in social networks almost entirely consists of text messages and various publications, almost all relevant studies use textual analysis methods in conjunction with machine learning and artificial intelligence technologies. Of these, convolutional neural networks demonstrated the best results. However, the use of support vector and decision tree methods should also be mentioned, since these contributed considerably to accuracy. In addition, statistical methods are used to compile data samples and analyze obtained results.

"You don't believe in God? You ain't Black": Identifying as atheist elicits identity denial from Black ingroup members.

Objective: Anecdotal narratives and recent qualitative research with Black atheists document experiences of racial identity denial from the target's perspective. However, no research to date has examined whether Black perceivers perceive Black atheists as being weakly identified with their race. Because belief in God is often inextricably linked with Black racial identity in the Black community, we hypothesized that Black atheists would be perceived as less Black than nonatheists. Method: Black/African American adults (n = 343) were randomly assigned to view one of three Black individual's social networking profiles (i.e., a Christian, an atheist, and religion not explicitly mentioned). After, they reported their perceptions of the targets' perceived racial identity and trustworthiness. Results: Black participants, regardless of how strongly they identified racially, perceived a Black Atheist as less racially identified than a Black Christian or someone whose religious affiliation was unknown. Additionally, a Black atheist was perceived as less trustworthy than a Black Christian. Conclusions: Black atheists experience general anti-atheist bias (e.g., perceived as untrustworthy), as well as unique anti-atheist bias in the form of racial identity denial. These findings extend previous research on identity denial and intragroup dynamics and advance our understanding of the relationship between religious identification and racial identity denial within the Black community. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Prevention of crime in the field of information technologies in the conditions of martial law in Ukraine

The article examines the directions of crime prevention in the field of information technologies in the conditions of martial law. It is noted that in the process of scientific and technical progress and the active introduction of Internet technologies into all areas of society, crime is also developing.
 This issue is especially relevant today in Ukraine, because in the conditions of martial law, cybercriminals are actively involved in hacking government servers, disinforming the population, using fake profiles in social networks, etc. Today, cyber fraud is widespread, in which criminals intend to learn bank details of citizens under the guise of various payments in order to take possession of funds.
 It is noted that today the war in the information space causes no less damage than the war on the battlefield. In this regard, the process of improving the current criminal and criminal procedural legislation regarding the prosecution of cybercriminals has already begun in Ukraine. Thus, after the full-scale invasion of Russia on the territory of Ukraine, the number of criminal offenses in the field of information technology increased dramatically. The aggressor country uses Internet technologies for disinformation about the invasion of Ukraine, propaganda of hostile ideas, etc. In this regard, the Verkhovna Rada of Ukraine optimized the criminal and criminal procedural legislation, improving the grounds and procedural mechanisms for bringing criminals to criminal responsibility.
 It was determined that the legislative framework of Ukraine does not contain a separate special act that would regulate the issue of crime prevention in the field of information technologies. Instead, there are several legal acts in Ukrainian legislation that regulate this issue.
 It was concluded that in order to build an effective system of crime prevention in the field of information technology, it is advisable not only to strengthen responsibility for committed criminal offenses, but also to develop socio-economic, organizational-management and moral-psychological directions aimed at neutralizing the factors that lead to the commission of criminal offenses .