Context Of Social Networks Research Articles

Performance and sustainability of BERT derivatives in dyadic data

In recent years, the Natural Language Processing (NLP) field has experienced a revolution, where numerous models –based on the Transformer architecture– have emerged to process the ever-growing volume of online text-generated data. This architecture has been the basis for the rise of Large Language Models (LLMs). Enabling their application to many diverse tasks in which they excel with just a fine-tuning process that comes right after a vast pre-training phase. However, their sustainability can often be overlooked, especially regarding computational and environmental costs. Our research aims to compare various BERT derivatives in the context of a dyadic data task while also drawing attention to the growing need for sustainable AI solutions. To this end, we utilize a selection of transformer models in an explainable recommendation setting, modeled as a multi-label classification task originating from a social network context, where users, restaurants, and reviews interact.

Dynamic clustering-based consensus model for large-scale group decision-making considering overlapping communities

Consensus-reaching strategy is crucial in large-scale group decision-making (LSGDM) as it serves as an effective approach to reducing group conflicts. Meanwhile, the common social network relationships in large groups can affect information exchange, thereby influencing the consensus-reaching process (CRP) and decision results. Therefore, how to leverage social network information in LSGDM to obtain an agreed solution has received widespread attention. However, most existing research assumes relative independence between communities in the dimension reduction process of LSGDM and neglects the possibility of different overlaps between them. Moreover, the impact of overlapping communities on CRP has not been adequately explored. Besides, the dynamic variations in clusters and their weights caused by evaluation updates need to be further studied. To address these issues, this paper proposes a dynamic clustering-based consensus-reaching method for LSGDM considering the impact of overlapping communities. First, the LINE-based label propagation algorithm is designed to cluster decision makers (DMs) and detect overlapping communities with social network information. An overlapping community-driven feedback mechanism is then developed to enhance group consensus by utilizing the bridging role of overlapping DMs. During CRP, clusters and their weights are dynamically updated with trust evolution due to the evaluation iteration. Finally, a case study using the Film Trust dataset is conducted to verify the effectiveness of the proposed method. Simulation experiments and comparative analysis demonstrate the capability of our method in modeling practical scenarios and addressing LSGDM problems under social network contexts.

Alternative Modes of Delivery in Pulmonary Rehabilitation: A Critical Appraisal of the Literature.

This review presents an overview of the safety and efficacy of alternative modes of pulmonary rehabilitation (PR) in people with chronic obstructive pulmonary disease (COPD). We identified recently published systematic reviews, meta-analyses, and guidelines, as well as relevant studies, exploring the safety and effectiveness of community-based PR, home-based PR, telerehabilitation, and web-based rehabilitation in people with COPD. A narrative summary of the main findings is presented. Although evidence suggests that community-based PR, home-based PR, telerehabilitation, and web-based rehabilitation are effective alternatives to center-based PR, it requires a careful interpretation as several of these programs do not comply with PR definition and have been compared with center-based PR programs that do not reach the minimal clinically important differences. Moreover, there is a huge heterogeneity among programs, and the confidence and quality of the evidence is mostly low. Hence, these novel modes of PR and center-based PR are not interchangeable. Instead, these are alternative modes aiming to increase access to PR. Questions remain regarding the most efficient way of implementing each PR mode, level of access, reimbursement policies, and data privacy in the use of technology. Standard protocols on how to set up each alternative PR mode need to be developed. Future research needs to explore how to use the treatable traits approach in combination with individual preferences and needs, program availability, safety, social support network, digital literacy, and health system context to identify the optimal PR program for each patient.

Examining peer review network dynamics in higher education visual communication courses using ERGM

Peer review plays a pivotal role in shaping collaborative learning environments and evaluating student engagement, but there is a lack of research on the relational dynamics that underlie the interactions between students during the peer review process, which is critical for understanding the effectiveness of peer review for students’ learning outcomes. Using explanatory sequential study design, this study’s objectives are to explore the dynamics of peer review as a feedback mechanism in higher education within a social network context, focusing on student interactions during the feedback process. Specifically, we applied dyadic-independent Exponential Random Graph Modeling (ERGM), along with mixed-methods design to analyze peer review interactions within a visual communication course at a large state university. We collected and analyzed two samples: a quantitative sample of 167 students for network analysis, drawn from 3082 peer reviews and rubric scores, and a qualitative sample of 136 students from post-course surveys that provided feedback on the peer review process and course methodologies. Our investigation revealed the inherently social network nature of peer review, shedding light on its significance in engaging students and assessing student engagement. Specifically, we examined the role of students’ performance levels, edge-based attributes derived from the visual peer review rubric, and language used in the feedback on the likelihood of forming peer review connections in the network. Our findings indicate that attributes derived from the visual peer review rubric significantly influence the likelihood of peer connections. On the other hand, students’ performance levels do not have any impact on the formation of peer connections. The findings highlight the importance of visual peer review activities in shaping peer connections in the classroom. Future studies will incorporate higher-order structures, such as transitivity and triadic closure, into our ERGM model to further enhance our understanding of peer review dynamics in educational settings.

Large Group Failure Mode and Effects Analysis Method Considering Individual Concerns and Integrated Consensus Mechanism in Social Network Context: An Application to Mobile Medical Privacy Risk Evaluation

Large Group Failure Mode and Effects Analysis Method Considering Individual Concerns and Integrated Consensus Mechanism in Social Network Context: An Application to Mobile Medical Privacy Risk Evaluation

The social force model: a behavioral modeling approach for information propagation during significant events

Information diffusion otherwise known as the propagation, spread or dissemination of information occurs when a piece of information flows from a particular individual/community to another in a social network. Studies related to information propagation involve problems regarding the factors that affect the information propagation, how the information is disseminated, the speed of propagation, etc. Researchers have proposed information propagation models to understand the phenomenon and to answer these questions. These models have been effectively used in applications such as behavior analysis, public health care, etc. Although several studies are carried out in this field, the literature demands identifying the most influential factors of propagation in real time in cases of sudden unexpected significant disasters/epidemics/pandemics, since the existing propagation models seem unfit during such circumstances. In this paper, a novel information propagation model which predicts the top propagators of information related to a particular context is proposed. This model utilizes the past few weeks' data during a sudden outbreak of a disaster and identifies the most influential attributes of a user profile to predict the top propagators of the future. The proposed Social Force Model is inspired by a model used in studying the fear propagation pattern in pedestrian dynamics in real-life situation [Cornes FE, Frank GA, Dorso CO. Fear propagation and the evacuation dynamics. Simul Model Pract Theory. 2019;95:112–133.]. We have effectively mapped the various forces which constitute the Social Force Model such as the Desired Force, the Social Force and the Granular Force with respect to the online social network context in order to discover the key spreaders of information during a specific context. Apart from identifying the propagators, the proposed model discovers the key attributes by analyzing the behavior of users based on their past activities in the online social network.

Neural network approaches for rumor stance detection: Simulating complex rumor propagation systems

SummaryThis research introduces a comprehensive suite of neural network models designed to tackle the challenging task of rumor stance detection within the framework of simulating complex rumor propagation systems. Our objective centers on accurately modeling the intricate structures of rumor dialogues and propagation patterns to identify user stances—whether they are in support, denial, questioning, or commenting on rumors. Unlike conventional methods that rely on simplistic keyword targeting and fail in the nuanced context of social networks, our models delve into the complexities of dialogue and propagation structures, offering a more precise and insightful analysis of rumor dynamics. In addressing the simulation and modeling of complex systems, our approach specifically focuses on the elaborate interaction networks that underpin rumor spread and reception. While our methodology does not directly engage with brain‐like computing paradigms, it reflects a similar level of sophistication in handling layered and complex information flows, analogous to cognitive processes in understanding and interpreting human communications. Employing a hierarchical attention mechanism, our models adeptly parse through multitiered dialogue sequences, effectively distinguishing between various indicators of user stances. This allows for a nuanced and detailed representation of the rumor ecosystem, significantly enhancing the accuracy of stance detection. Through rigorous testing on diverse datasets, our approach has demonstrated superior performance over existing models, thereby establishing a new benchmark in the field.

Dual-promotion: Bragging better by promoting peers.

To create favorable impressions and receive credit, individuals need to share information about their past accomplishments. Broadcasting one's past accomplishments or claiming credit to demonstrate competence, however, can harm perceptions of warmth and likability. In fact, prior work has conceptualized self-promotion as a hydraulic challenge: tactics that boost perceptions along one dimension (e.g., competence) harm perceptions along other dimensions (e.g., warmth). In this work, we identify a novel approach to self-promotion: We show that by combining self-promotion with other-promotion (complimenting or giving credit to others), which we term "dual-promotion," individuals can project both warmth and competence to make better impressions on observers than they do by only self-promoting. In seven preregistered studies, including analyses of annual reports from members of Congress and experiments using social network, workplace, and political contexts (total N = 1,448), we show that individuals who engage in dual-promotion create more favorable impressions of warmth and competence than those who only engage in self-promotion. The beneficial effects of dual-promotion are robust to both competitive and noncompetitive contexts and extend to behavioral intentions. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

The Influence of Self-Efficacy of College Students in Privacy Protection and Behavior in Social Networking Context

In the era of mobile social media, users who show themselves on social platforms will face the risk of privacy breaches. Frequent data leaks and increasingly complex social media privacy protection terms intensify users’ privacy concerns, along with mandatory privacy exposure, contributing to privacy fatigue among users. This study examines the changes in privacy protection behavior among college students under the influences of privacy concerns and privacy fatigue from the perspective of self-efficacy in privacy protection. Data is collected through a questionnaire survey, and a structural equation model is established to provide insights into protecting user privacy. The results of this research indicate that college students’ internal self-efficacy in privacy protection has a negative impact on privacy fatigue. College students’ privacy concerns are significantly negatively correlated with their willingness to disclose information, while privacy fatigue is significantly positively correlated with their willingness to disclose information. Privacy fatigue among college students has a significant positive influence on both their disclosure intention and the behavior of disengaging from privacy protection.

Achieving social influence across gender and time: Are dominance and prestige equally viable for men and women?

The dual framework of social rank allocation discusses dominance and prestige as two viable routes to status or social influence. In doing so, this literature has largely neglected findings demonstrating backlash against men and women for behaving in gender-incongruent ways. Likewise, it remains unclear if dominance and prestige continue to be effective means to status over time. This study investigates the viability of dominance or prestige in contributing to an individual's social influence, conditional on their gender and across time. Using a stereotype-neutral context of an online social network, I unobtrusively tracked individuals' changes in social influence among their network members on Twitter. By analyzing almost 230,000 tweets, it was found that men's influence increased with greater dominance, whereas women's decreased. At the same time, women's influence increased with greater prestige, whereas men's decreased. Network centrality (in-degree centrality) explained this differential interaction pattern. Additionally, longitudinal analysis provided a more nuanced understanding. Over time, role incongruence effects dampened, dominance became less effective, even for men, and prestige became viable for both men and women. Thus, by jointly considering the role of gender and time, this research offers key theoretical caveats to the dual rank framework. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Exploring the Components of Digital Identity on Social Networks Sites: Identifier, Post, Profile Photo, and Selfie

The digital age has ushered in a paradigm shift in self-expression and identity construction, with social network sites (SNSs) serving as vibrant canvases for individuals to shape and project their digital personality. This paper focuses on delving into the intricate components of digital identity on SNS, focusing on four key elements: identifiers, posts, profile photos, and selfies. Based on a descriptive and analytical method, this article attempts to draw up a detailed and nuanced analysis of the multiple facets of digital identity, thus providing a solid basis for in-depth understanding of this complex phenomenon in the specific context of social networks which technically regulate the representation of self. The results obtained show that the construction of a digital identity is framed by the architecture of SNSs, which incite users to manifest their selves, thus producing unlimited digital traces that reflect their personality traits. The results also provide an understanding of the structure of SNSs that aim to raise awareness of presenting a positive self-image in the digital world.

Hate Speech Recognition: The Role of Empathy and Awareness of Social Media Influence

RICONOSCIMENTO DELL’HATE SPEECH: IL RUOLO DELL’EMPATIA E DELLA CONSAPEVOLEZZA DELL’INFLUENZA DEI SOCIAL MEDIA Abstract Hate speech occurs within democratic societies that embrace freedom of expression and is made tangible in the social network context. It is characterized by a specific form of discrimination based on the use of verbal expressions or other media content and, usually, directed at minority groups. Although there is a lack of consensus about a unique and shared definition of hate speech, its social and personal consequences are particularly relevant for the whole society. For these reasons, it seems of crucial importance to identify hate speech recognition antecedents. The present study aimed at analyzing the relationship between hate speech recognition and specific psychological constructs, namely, empathy and awareness of social media influence. More in details, we hypothesized the association between empathy and hate speech recognition was mediated by awareness of social media influence. Data obtained from 146 participants revealed that empathy positively predicted hate speech recognition, and such relationship was mediated by awareness. Implication of such findings are discussed.

A new technique for influence maximization on social networks using a moth-flame optimization algorithm

In our modern digital era, social networks have seamlessly integrated into the fabric of our daily lives. These digital platforms serve as vital channels for communication, exchanging information, and cultivating valuable connections. The propagation of information within these social networks has emerged as a central focus for numerous sectors, including politics, marketing, research, education, and finance. Diverse models have been employed to depict the dynamics of information dissemination across these networks. Nevertheless, the notion of influence holds profound significance for both businesses and individuals. Influence maximization, particularly within the context of social networks, has garnered considerable attention owing to its potential to reach and impact a broad audience. This intricate challenge is commonly referred to as the “influence maximization problem,” a problem well-known for its NP-hard complexity. This paper proposes a cutting-edge technique that leverages the Moth-Flame Optimization Algorithm to enhance influence maximization. Influence maximization is an important issue in network analysis, which widely occurs in social networks. Influence can be seen as a cascading effect, where the actions of a few trigger a chain reaction, ultimately reaching a large portion of the network. Identifying these “influencers” is crucial for efficient resource allocation and information dissemination. One of the important issues in finding the maximum influence is choosing the best vertex among all the vertices in the graph. This research presents a new method to find the maximum influence in social networks based on the Moth-Flame Algorithm (MFA). The proposed method aims to find the maximum influence in the social network graph that has a good fitness degree. The algorithm can identify potential influencers. Our simulations across multiple networks have unequivocally showcased the superiority of this algorithm as the preeminent and scalable solution to the influence maximization problem. The experimental outcomes clearly delineate that the employment of the MFA (Maximal First Activation) approach effectively diminishes the execution time required to approximate the maximum influence. The proposed technique improved the accuracy and excucation time by 3.140 % and 12.2 % compared to other methods.

Understanding online reviews adoption in social network communities: an extension of the information adoption model

PurposeOnline reviews derived from peer communications have been increasingly viewed as an important approach for consumers to gather pre-purchase information. This study aims to examine factors affecting online reviews adoption in social network communities and then indicates the underlying mechanism of this process based on an extended information adoption model (IAM).Design/methodology/approachUsing the data collected from 242 users of a social network community via an online survey, the proposed model is empirically assessed by partial least squares-based structural equation model (PLS-SEM).FindingsThe results show that both perceived diagnosticity and perceived serendipity are drivers of online reviews adoption in social network communities. Meanwhile, community identification is not only an antecedent of diagnosticity and serendipity perceived by community members, but also motivates source credibility which, in turn, positively influences argument quality. Finally, the importance of argument quality and source credibility in reviews adoption process is also presented.Originality/valueThis study extends the IAM and enriches the literature regarding online reviews adoption. It deepens the understanding of serendipitous experiences and community identification in social networking context by addressing their important roles in the authors' extended IAM.

Sentiment Analysis of Social Network Text Based on HDBSCAN and SO-PMI

With the rapid development of the Internet, the length and themes of text content in social network environments are becoming more and more abundant. The probability and degree of semantic similarity between different words in different texts are also increasing. It is difficult to effectively identify text topics and their sentiment tendencies. This paper takes the text data of the Ford Forum of the PCauto as the research object, builds a word vector model based on Word2vec, uses HDBSCAN to classify social network context, and calculates the sentiment value of different topic through SO-PMI. It aims to explore the complexity of the semantic evolution and feature extraction process and its generating text topics, based on which we further analyze the variability of sentiment tendencies between words and topics and then summarize their characteristics.

Robust Image Forgery Detection Over Online Social Network Shared Images

In servers and mobile users, an image is sent over the social network or exchanged. Due to the fact that it contains delicate personal information, the privacy of that data is crucial. A hacker may use social information about a person to discredit them if their image is hacked. Text-based encryption can be used in mobile cloud computing under the current architecture. There are various ways to store data securely utilizing mobile computing, including end-to-end encryption of data transmission and dynamic credential generation that only generates text. We'll be creating a brand-new wavelet watermarking technique called the discrete wavelet transform for use in real-time social network applications like Facebook, and this study suggests an efficient image forgery detection method that recognizes a manipulated foreground or background. Using this technique, images can be used and safely kept on servers. We categorize the image as either common or delicate, and we also enhance the project to include copy right implementation. Run copyright algorithms referred to as wavelet transform algorithms when employing sensitive techniques. After that, provide the receiver secure access to download the pictures. Using C#.NET as the front end and SQL SERVER as the back end, experimental results show the efficiency of current algorithms in real-time social network contexts and a comparison of their privacy rates

Corruption as state -corporate crime: the example of the health sector in Greece

The study analyses the relations and processes underpinning grand corruption in the health sector in Greece. Viewed from a systemic-structural perspective, corruption is examined as a phenomenon emerging from the interactions and interconnections between the organized structures of venture capital and the state apparatus. Applying an integrated theoretical model of state-corporate crime and drawing on extensive empirical qualitative research, this paper traces the processes of normalization and institutionalization of corruption within the context of social networks and organizations. It is argued that corruption is not a deviant response to a normal system but a normal response to a system fostering illicit practice in accordance with capitalist ethos, profiteering, and the distribution of economic resources. In this wide political economy context, existing power relations and structures within the health sector are being reproduced.

Saxon Transylvania, An Identity Potpourri and a Cultural Catalysis in the Social Network Context of the Last Millennium

Saxons from Transylvania are a strong ethnic group. The present study brings to light an internal point of view of the Saxon identity. In the Transylvanian space, the Saxon identity is shaped by the interaction between the inhabitant ethnic groups present concomitantly in the same space. Saxons, Romanians, Hungarians and Rroma coexist and interact, developing cultural exchange

Directional user similarity model for personalized recommendation in online social networks

With the huge amount of information available in online social networks and the increasing spread of user generated data in different forms, personal recommendation systems represent ideal solutions for optimising users’ efforts and advocating the right information that suits the user’s context. This research proposes a user-based collaborative filtering recommendation technique for social networks that exploits the context and characteristics of social networks in order to recommend personalised content to users. The originality of the approach is to streamline contextual features and social network data to reduce data dimensionality and use learning techniques to uncover contextual information that is not normally included as a typical interest to rank tweets for users. The proposed approach uses a hybrid directional relation user similarity model (DRUSM) that exploits subtle users’ actions and directional interactions’ diversity to match users’ contexts. DRUSM includes two similarity algorithms – author directional similarity (AUDS) and action directional similarity (ACDS) – to account for the abovementioned actions and interactions. It employs a learning algorithm to rank tweets according to a user’s interests. DRUSM was tested and compared with AUDS, ACDS and other algorithms, such as the new heuristic similarity model, content relevance and publisher authority. Experiments show that DRUSM outperformed all the other algorithms. It achieves higher accuracy in recommending tweets due to its suitability to appropriately handle fine details of the user’s context.

University life has gone digital: influences of institutional mobile social network use during the COVID-19 emergency

PurposeMany universities implemented institutional social networking apps as an alternative to in-person social experiences during the COVID-19 pandemic. Therefore, this study aims to explore previously identified factors that influenced intentions to form collective actions, also known as we-intentions, on such social networking apps and their influence on student satisfaction with the app artifact.Design/methodology/approachStudents from across a large university were invited to participate in a survey. Responses from 915 students who reported using the app were analyzed using a maximum likelihood covariance-based structural equation model. Analysis was conducted using the R programming language's psych, lavaan, and semTools packages.FindingsThe authors found that we-intentions are positively associated with recent app use and with student satisfaction with the app. Group norms were found to significantly influence the formation of we-intentions, while social identity is positively associated with both we-intentions and satisfaction.Originality/valueThe paper provides evidence that past research generalizes to the context of university mobile social networks and identifies a relationship between we-intentions and satisfaction in this context. It also provides practical insight into factors that influence we-intentions, and subsequently students' online education experience, in the context of a university's institutional mobile social network.