A review on trust propagation and opinion dynamics in social networks and group decision making frameworks

Novel approaches to recruiting clinical sites for embedded pragmatic clinical trials: Insights from the AIM-back trial

Background

Embedded pragmatic clinical trials (ePCTs) assess interventions in real-world settings. Best practices for recruiting clinical sites for ePCTs are unknown, especially for sites that aren't known to the study team or familiar with clinical research. We describe the site recruitment process for AIM-Back, an ePCT of two nonpharmacologic pathways for low back pain within the Veterans Health Administration (VA).

Methods

During the planning phase of the AIM-Back trial, we aimed to recruit 18-20 sites. Eligible sites required provider capacity, administrative support, and geographic separation to avoid contamination. Our three-step approach involved: (1) lead (VA personnel) identification through existing VA contacts, data repositories of VA clinicians, and promotional outreach at events and listservs; (2) lead engagement via tailored communications emphasizing participation benefits; and (3) virtual meetings with administrators and clinicians.

Results

We identified 184 leads across 53 VA healthcare systems. Leads from 40 systems responded to outreach, and recruitment meetings were conducted with 23 systems involving primary care, physical therapy, research staff, and leadership. We met our recruitment goal, securing participation agreements from 19 sites, with a median timeline from outreach to participation agreement of 3.7 months. Common reasons for non-participation included infrastructure and resource constraints, resistance to new clinical programs, and competing programs.

Conclusion

AIM-Back's recruitment highlights ePCT site recruitment complexities for trials engaging new clinical research sites. Our innovative three-step recruitment approach provides an example for similarly designed trials. Future ePCTs should consider comprehensive recruitment strategies to ensure clinician buy-in, study feasibility, and broaden existing networks for completing ePCTs.

Evolutionary dynamics of trust in hierarchical populations with varying investment strategies

Trust is one of the fundamental elements in the development of human societies, which can be modelled on the trust game. In the traditional trust game, investors decide whether to invest or not, and trustees choose whether to be trustworthy or not. In this study, we differentiate between investors and trustees and assume that strategy imitation only happens among individuals of the same class, in which their ratios remain constant. Trustees can choose to be either trustworthy or untrustworthy, while investors decide between an active and a conservative investment strategies based on environmental factors. Here, the environmental factor is closely related to the number of trustworthy trustees within the group. Applying evolutionary game theory, we investigate behavioural changes in the [Formula: see text]-player trust game when environmental factors are introduced. Our findings indicate that investors can form effective coalitions with trustworthy trustees, thereby excluding untrustworthy ones. Furthermore, we validate the robustness of our model and reveal that different investment behaviours have different advantages under specific environmental conditions. This study highlights the subtle interplay between trust and investment dynamics in different environments, providing new insights into the mechanisms of trust in socioeconomic systems, which has some practical significance.

Multiagent-System-Based Attention Mechanism for Predicting Product Popularity: Handling Positive-Negative Diffusion Over Social Networks

This brief is concerned with the prediction problem of product popularity under a social network (SN) with positive-negative diffusion (PND). First, a PND model is proposed to enable the simulation of product diffusion, and three user states are defined. Second, an optimal and precise feature vector of every user is extracted through a multi-agent-system-based attention mechanism (MASAM) that is devised. The weight matrix shared in the mechanism of all agents is learned using a distributed learning algorithm provided in MASAM. Third, an MAS model for product diffusion on SN is established based on the feature representations from MASAM. Rules for agent interaction during PND diffusion are suggested, which accelerate the simulation of information spread in SN. Finally, comprehensive experiments are conducted to verify the effectiveness and efficiency of the proposed models and algorithms in prediction and to compare their performance with baseline methods. Furthermore, a case study is provided to illustrate the applicability and extendibility of the developed algorithm.

Collective effect of self-learning and social learning on language dynamics: a naming game approach in social networks

Linguistic rules form the cornerstone of human communication, enabling people to understand and interact with one another effectively. However, there are always irregular exceptions to regular rules, with one of the most notable being the past tense of verbs in English. In this work, a naming game approach is developed to investigate the collective effect of social behaviours on language dynamics, which encompasses social learning, self-learning with preference and forgetting due to memory constraints. Two features that pertain to individuals' influential ability and affinity are introduced to assess an individual's role of social influence and discount the information they communicate in the Bayesian inference-based social learning model. Our findings suggest that network heterogeneity and community structure significantly impact language dynamics, as evidenced in synthetic and real-world networks. Furthermore, self-learning significantly enhances the process of language regularization, while forgetting has a relatively minor impact. The results highlight the substantial influence of network structure and social behaviours on the transition of opinions, from consensus to polarization, demonstrating its importance in language dynamics. This work sheds new light on how individual learners adopt language rules through the lenses of complexity science and decision science, advancing our understanding of language dynamics.

Three-Stage Cascade Information Attenuation for Opinion Dynamics in Social Networks

In social network analysis, entropy quantifies the uncertainty or diversity of opinions, reflecting the complexity of opinion dynamics. To enhance the understanding of how opinions evolve, this study introduces a novel approach to modeling opinion dynamics in social networks by incorporating three-stage cascade information attenuation. Traditional models have often neglected the influence of second- and third-order neighbors and the attenuation of information as it propagates through a network. To correct this oversight, we redefine the interaction weights between individuals, taking into account the distance of opining, bounded confidence, and information attenuation. We propose two models of opinion dynamics using a three-stage cascade mechanism for information transmission, designed for environments with either a single or two subgroups of opinion leaders. These models capture the shifts in opinion distribution and entropy as information propagates and attenuates through the network. Through simulation experiments, we examine the ingredients influencing opinion dynamics. The results demonstrate that an increased presence of opinion leaders, coupled with a higher level of trust from their followers, significantly amplifies their influence. Furthermore, comparative experiments highlight the advantages of our proposed models, including rapid convergence, effective leadership influence, and robustness across different network structures.

Wandering Drunkards Walk after Fibonacci Rabbits: How the Presence of Shared Market Opinions Modifies the Outcome of Uncertainty

Shared market opinions and beliefs by market participants generate a set of constraints that mediate information through a not-so-unstable system of expected target prices. Price trajectories, within these sets of constraints, confirm or disprove the likelihood of participant expectations and cannot, de facto, be considered permutable, as literature has shown, since their inner structure is dynamically affected by their own progress, suggesting per se the presence of both heat and cycles. This study described and discussed how trajectories are built using different alphabets and suggests that prices follow an ergodic course within structurally similar tessellation classes. It is reported that the courses of price moves are self-similar due to their a priori structure, and they do not need to be complete in order to create the conditions, in resembling conditions, for the appearance of the well-known and commonly used Fibonacci ratios between price trajectories. To date, financial models and engineering are mostly based on the mathematics of randomness. If these theoretical findings need empirical validation, such a potential infrastructure of ratios would suggest the possibility for a superstructure to exist, in other words, the emergence of exploitable patterns.

Neuronal activity induces symmetry breaking in neurodegenerative disease spreading

Dynamical systems on networks typically involve several dynamical processes evolving at different timescales. For instance, in Alzheimer's disease, the spread of toxic protein throughout the brain not only disrupts neuronal activity but is also influenced by neuronal activity itself, establishing a feedback loop between the fast neuronal activity and the slow protein spreading. Motivated by the case of Alzheimer's disease, we study the multiple-timescale dynamics of a heterodimer spreading process on an adaptive network of Kuramoto oscillators. Using a minimal two-node model, we establish that heterogeneous oscillatory activity facilitates toxic outbreaks and induces symmetry breaking in the spreading patterns. We then extend the model formulation to larger networks and perform numerical simulations of the slow-fast dynamics on common network motifs and on the brain connectome. The simulations corroborate the findings from the minimal model, underscoring the significance of multiple-timescale dynamics in the modeling of neurodegenerative diseases.

My favourite part was learning different ways to play: qualitatively evaluating a socially prescribed creative play programme

OBJECTIVES

Social prescription is classically thought of as an initiative for older people. This qualitative study aimed to evaluate the first socially prescribed creative play programme for families of 0-3-year-old children in the UK, examining the experience of the parents throughout the stages of the programme.

STUDY DESIGN

The evaluation ran longitudinally over 5 weeks using interviews, field notes, and questionnaire data.

METHODS

The evaluation was carried out over 5 weeks in 2022 using intervention leaders' and researcher's field notes, nine parent semi-structured interviews, and 17 parent questionnaires on their experiences. Data were analysed using inductive interpretive thematic analysis.

RESULTS

After analysis of the corpus of data, three themes that interacted with each other were identified: Support Systems that Parents Trust, Calming in Chaos, and Practical Parenting Utility. Parents said that they were more likely to sign up for the programme when they trusted the recommender and the organisation running the programme. They found the socially prescribed group more relaxed and calm than other groups, and their daily lives. The knowledge about health behaviours and modelling of play were the main take-home skills reported.

CONCLUSION

In order for parents to be receptive to practical parenting knowledge the SP aimed to foster, parents must first establish trust in a calming atmosphere. Social links and child development were the key factors parents identified linking to well-being. This research could inform public health policy on social prescription for families.

Role of public relations practices in content management: the mediating role of new media platforms

Public relations practices are widely accompanied by communication and persuasion. Especially today, when new media platforms provide direct accessibility, communication through PR has become more improved. This research focused on media organizations in the UAE, with a special consideration given to their audience content management. The researchers applied the case study method and selected a sample of n = 280 individuals from n = 12 media houses currently working in the UAE. The results obtained by structural equation modeling (SEM) revealed that media organizations in the UAE pay significant consideration to public relations practices (p > 0.000) and new media adoption (p > 0.000). Moreover, both these public relations practices (p > 0.000) and new media adoption were also found to significantly focus on two-way communication. Consequently, this two-way communication is significantly affecting content management among these organizations (p > 0.000), leading to the design, evaluation, and alteration of content that is acceptable and liked by their audiences. Thus, it has been concluded that media content and its management is not a simple task. Audience and communication are two basic factors that play an important role in this regard. Furthermore, the role of public relations practices also enhances communication and content management practices, leading to even more constructive outcomes.

Gravity-Based Community Vulnerability Evaluation Model in Social Networks: GBCVE

The usage of social media around the world is ever-increasing. Social media statistics from 2019 show that there are 3.5 billion social media users worldwide. However, the existence of community structure renders the network vulnerable to attacks and large-scale losses. How does one comprehensively consider the multiple information sources and effectively evaluate the vulnerability of the community? To answer this question, we design a gravity-based community vulnerability evaluation (GBCVE) model for multiple information considerations. Specifically, we construct the community network by the Jensen-Shannon divergence and log-sigmoid transition function to show the relationship between communities. The number of edges inside community and outside of each community, as well as the gravity index are the three important factors used in this model for evaluating the community vulnerability. These three factors correspond to the interior information of the community, small-scale interaction relationship, and large-scale interaction relationship, respectively. A fuzzy ranking algorithm is then used to describe the vulnerability relationship between different communities, and the sensitivity of different weighting parameters is then analyzed by Sobol' indices. We validate and demonstrate the applicability of our proposed community vulnerability evaluation method via three real-world complex network test examples. Our proposed model can be applied to find vulnerable components in a network to mitigate the influence of public opinions or natural disasters in real time. The community vulnerability evaluation results from our proposed model are expected to shed light on other properties of communities within social networks and have real-world applications across network science.

Challenging social media threats using collective well-being-aware recommendation algorithms and an educational virtual companion

Social media have become an integral part of our lives, expanding our interlinking capabilities to new levels. There is plenty to be said about their positive effects. On the other hand, however, some serious negative implications of social media have been repeatedly highlighted in recent years, pointing at various threats to society and its more vulnerable members, such as teenagers, in particular, ranging from much-discussed problems such as digital addiction and polarization to manipulative influences of algorithms and further to more teenager-specific issues (e.g., body stereotyping). The impact of social media-both at an individual and societal level-is characterized by the complex interplay between the users' interactions and the intelligent components of the platform. Thus, users' understanding of social media mechanisms plays a determinant role. We thus propose a theoretical framework based on an adaptive "Social Media Virtual Companion" for educating and supporting an entire community, teenage students, to interact in social media environments in order to achieve desirable conditions, defined in terms of a community-specific and participatory designed measure of Collective Well-Being (CWB). This Companion combines automatic processing with expert intervention and guidance. The virtual Companion will be powered by a Recommender System (CWB-RS) that will optimize a CWB metric instead of engagement or platform profit, which currently largely drives recommender systems thereby disregarding any societal collateral effect. CWB-RS will optimize CWB both in the short term by balancing the level of social media threats the users are exposed to, and in the long term by adopting an Intelligent Tutor System role and enabling adaptive and personalized sequencing of playful learning activities. We put an emphasis on experts and educators in the educationally managed social media community of the Companion. They play five key roles: (a) use the Companion in classroom-based educational activities; (b) guide the definition of the CWB; (c) provide a hierarchical structure of learning strategies, objectives and activities that will support and contain the adaptive sequencing algorithms of the CWB-RS based on hierarchical reinforcement learning; (d) act as moderators of direct conflicts between the members of the community; and, finally, (e) monitor and address ethical and educational issues that are beyond the intelligent agent's competence and control. This framework offers a possible approach to understanding how to design social media systems and embedded educational interventions that favor a more healthy and positive society. Preliminary results on the performance of the Companion's components and studies of the educational and psychological underlying principles are presented.

Predicting opinion evolution based on information diffusion in social networks using a hybrid fuzzy based approach

Social media plays an important role in disseminating information and analysing public and government opinions. The vast majority of previous research has examined information diffusion and opinion analysis separately. This study proposes a new framework for analysing both information diffusion and opinion evolution. The change in opinion over time is known as opinion evolution. To propose a new model for predicting information diffusion and opinion analysis in social media, a forest fire algorithm, cuckoo search, and fuzzy c-means clustering are used. The forest fire algorithm is used to determine the diffuser and non-diffuser of information in social networks, and fuzzy c-means clustering with the cuckoo search optimization algorithm is proposed to cluster Twitter content into various opinion categories and to determine opinion change. On different Twitter data sets, the proposed model outperformed the existing methods in terms of precision, recall, and accuracy.

Propagation, detection and correction of errors using the sequence database network

Nucleotide and protein sequences stored in public databases are the cornerstone of many bioinformatics analyses. The records containing these sequences are prone to a wide range of errors, including incorrect functional annotation, sequence contamination and taxonomic misclassification. One source of information that can help to detect errors are the strong interdependency between records. Novel sequences in one database draw their annotations from existing records, may generate new records in multiple other locations and will have varying degrees of similarity with existing records across a range of attributes. A network perspective of these relationships between sequence records, within and across databases, offers new opportunities to detect-or even correct-erroneous entries and more broadly to make inferences about record quality. Here, we describe this novel perspective of sequence database records as a rich network, which we call the sequence database network, and illustrate the opportunities this perspective offers for quantification of database quality and detection of spurious entries. We provide an overview of the relevant databases and describe how the interdependencies between sequence records across these databases can be exploited by network analyses. We review the process of sequence annotation and provide a classification of sources of error, highlighting propagation as a major source. We illustrate the value of a network perspective through three case studies that use network analysis to detect errors, and explore the quality and quantity of critical relationships that would inform such network analyses. This systematic description of a network perspective of sequence database records provides a novel direction to combat the proliferation of errors within these critical bioinformatics resources.

Two-step seeding strategy in multiplex networks with inter-layer conversion cost of influence

In this paper, we investigate the problem of influence seeding strategy in multilayer networks. In consideration of the fact that there exist inter-layer conversion costs associated with influence diffusion between layers in multiplex networks, a novel two-step seeding strategy is proposed to identify influential individuals in multiplex networks. The first step is to determine the target layer, and the second step is to identify the target seeds. Specifically, we first propose two comparable layer selection strategies, namely, multiplex betweenness centrality and multi-hop multiplex neighbors (MMNs), to determine the target layer of seeding diffusion and then construct a multiplex gravity centrality (MGC) in the manner of the gravity model to identify the influential seeds in the target layer. Subsequently, we employ a redefined independent cascade model to evaluate the effectiveness of our proposed seeding strategy by comparing it with other commonly used centrality indicators, which is validated on both synthetic and real-world network datasets. The experimental results indicate that our proposed seeding strategy can obtain greater influence coverage. In addition, parameter analysis of a neighborhood range demonstrates that MMN-based target layer selection is relatively robust, and a smaller value of a neighborhood range can enable MGC to achieve better influence performance.

An Adaptive Dempster-Shafer Theory of Evidence Based Trust Model in Multiagent Systems

Multiagent systems (MASs) have a wide range of industrial applications due to agents’ advantages. However, because of the agents’ dynamic behaviors, it is a challenge to ensure the quality of service they present. In this paper, to address this problem, we propose an adaptive agent trust estimation model where agents may decide to go from genuine to malicious or the other way around. In the proposed trust model, both direct trust and indirect reputation are used. However, the indirect reputation derived from the direct experience of third-party agents must have reasonable confidence to be useful. The proposed model introduces a near-perfect measure that utilizes consistency, credibility, and certainty to capture confidence. Moreover, agents are incentivized to contribute correct information (to be honest) through a credit mechanism in the proposed model. Simulation experiments are conducted to evaluate the proposed model’s performance against some of the previous trust models reported in the literature.

Bounded Confidence Evolution of Opinions and Actions in Social Networks

Inspired by the continuous opinion and discrete action (CODA) model, bounded confidence and social networks, the bounded confidence evolution of opinions and actions in social networks is investigated and a social network opinions and actions evolutions (SNOAEs) model is proposed. In the SNOAE model, it is assumed that each agent has a CODA for a certain issue. Agents' opinions are private and invisible, that is, an individual agent only knows its own opinion and cannot obtain other agents' opinions unless there is a social network connection edge that allows their communication; agents' actions are public and visible to all agents and impact other agents' actions. Opinions and actions evolve in a directed social network. In the limitation of the bounded confidence, other agents' actions or agents' opinions noticed or obtained by network communication, respectively, are used by agents to update their opinions. Based on the SNOAE model, the evolution of the opinions and actions with bounded confidence is investigated in social networks both theoretically and experimentally with a detailed simulation analysis. Theoretical research results show that discrete actions can attract agents who trust the discrete action, and make agents to express extreme opinions. Simulation experiments results show that social network connection probability, bounded confidence, and the opinion threshold of action choice parameters have strong impacts on the evolution of opinions and actions. However, the number of agents in the social network has no obvious influence on the evolution of opinions and actions.

Challenges for Corporate Reputation—Online Reputation Management in Times of Global Pandemic

The issue of corporate reputation management in the time of accelerated digitization has been a subject of research by academics and practitioners for more than a decade. The aim of this study was to provide an insight into the issue of reputation management in the Internet environment in the time of global pandemic. As for the structure of the research, the study mapped two horizons of events, the first one being the onset of the pandemic in the first half of 2020, and the second one the period of cancellation of antipandemic measures after 24 months. The research was localized in the market of Central Europe, specifically in the online market of the Slovak Republic. This market synthesized two important factors, namely the highly developmental nature and at the same time the increased degree of restraint it experienced during the two years of the pandemic. A sophisticated online reputation analysis (sentiment analysis, analysis of reputation determinants, and data synthesis through the TOR indicator) was performed on a significant sample of e-commerce representatives, the results of which provided relevant findings on reputational challenges and reputational threats. Based on the findings, it can be stated that the market has adapted relatively quickly to the changed conditions. The pandemic represented a market opportunity rather than an existential threat for the subjects examined. It also played the role of an imaginary accelerator in the evolutionary transition from offline to online.

A potential mechanism for low tolerance feedback loops in social media flagging systems

Many people use social media as a primary information source, but their questionable reliability has pushed platforms to contain misinformation via crowdsourced flagging systems. Such systems, however, assume that users are impartial arbiters of truth. This assumption might be unwarranted, as users might be influenced by their own political biases and tolerance for opposing points of view, besides considering the truth value of a news item. In this paper we simulate a scenario in which users on one side of the polarity spectrum have different tolerance levels for the opinions of the other side. We create a model based on some assumptions about online news consumption, including echo chambers, selective exposure, and confirmation bias. A consequence of such a model is that news sources on the opposite side of the intolerant users attract more flags. We extend the base model in two ways: (i) by allowing news sources to find the path of least resistance that leads to a minimization of backlash, and (ii) by allowing users to change their tolerance level in response to a perceived lower tolerance from users on the other side of the spectrum. With these extensions, in the model we see that intolerance is attractive: news sources are nudged to move their polarity to the side of the intolerant users. Such a model does not support high-tolerance regimes: these regimes are out of equilibrium and will converge towards empirically-supported low-tolerance states under the assumption of partisan but rational users.

Workplace literacy skills—how information and digital literacy affect adoption of digital technology

Purpose

Information and digital literacy have recently received much interest, and they are being viewed as critical strategic organisational resources and skills that employees need to obtain in order to function at their workplaces. Yet, the role of employees' literacy seems to be neglected in current literature. This paper aims to explore the roles that information and digital literacy play on the employees' perception in relation to usefulness and ease of use of digital technologies and consequently their intention to use technology in the practices they perform at the workplace.

Design/methodology/approach

This paper builds a conceptual model with key constructs (information literacy and digital literacy) as new antecedents to the technology acceptance model and aims to establish that information literacy and digital literacy are indirect determinants of employees' intention to use digital technologies at the workplace. The data set used in this paper comprises of 121 respondents and structural equation modelling was used.

Findings

The findings reveal that both information literacy and digital literacy have a direct impact on perceived ease of use of technology but not on the perceive usefulness. The findings also show that both literacies have an indirect impact on the intention to use digital technology at work via attitude towards use.

Practical implications

Managers and decision-makers should pay close attention to the literacy levels of their staff. Because literacies are such an important skillset in the digital age, managers and chief information officers may want to start by identifying which work groups or individuals require literacy training and instruction, and then provide specific and relevant training or literacy interventions to help those who lack sufficient literacy.

Originality/value

This is one of the first studies to consider information literacy and digital literacy as new antecedents of the technology acceptance model at the workplace environment.

Opinion dynamics in social networks under competition: the role of influencing factors in consensus-reaching

The rapid development of information technology and social media has provided easy access to the vast data on individual preferences and social interactions. Despite a series of problems, such as privacy disclosure and data sensitivity, it cannot be denied that this access also provides beneficial opportunities and convenience for campaigns involving opinion control (e.g. marketing campaigns and political election). The profitability of opinion and the finiteness of individual attention have already spawned extensive competition for individual preferences on social networks. It is necessary to investigate opinion dynamics over social networks in a competitive environment. To this end, this paper develops a novel social network DeGroot model based on competition game (DGCG) to characterize opinion evolution in a competitive opinion dynamics. Social interactions based on trust relationships are captured in the DGCG model. From the model, we then obtain equilibrium results in a stable state of opinion evolution. We also analyse what role relevant factors play in the final consensus and competitive outcomes, including the resource ratio of both contestants, initial opinions, self-confidence and network structure. Theoretical analyses and numerical simulations show that these factors can significantly sway the consensus and even reverse competition outcomes.

How minimizing conflicts could lead to polarization on social media: An agent-based model investigation

Social media represent an important source of news for many users. They are, however, affected by misinformation and they might be playing a role in the growth of political polarization. In this paper, we create an agent based model to investigate how policing content and backlash on social media (i.e. conflict) can lead to an increase in polarization for both users and news sources. Our model is an advancement over previously proposed models because it allows us to study the polarization of both users and news sources, the evolution of the audience connections between users and sources, and it makes more realistic assumptions about the starting conditions of the system. We find that the tendency of users and sources to avoid policing, backlash and conflict in general can increase polarization online. Specifically polarization comes from the ease of sharing political posts, intolerance for opposing points of view causing backlash and policing, and volatility in changing one’s opinion when faced with new information. On the other hand, it seems that the integrity of a news source in trying to resist the backlash and policing has little effect.

Influence model with opinions and trust score evaluations under the leader-follower environment

A group decision-making process is introduced by utilizing the influence model together with a matrix of interpersonal influences and an opinion matrix. The opinion matrix is constructed with the opinions/advice from one group of experts towards the other. Experts are divided into two groups, one which has more experienced, skilled and qualified persons is known as the group of opinion leaders and the other is known as the group of opinion followers. Sometimes, decision-makers are ordinary agents and their opinion formation is profoundly influenced by opinion leaders. The truthfulness of opinion leaders and the interpersonal influences of decision-makers is also taken into account. Also, a modified definition of trust score evaluation is presented with the understanding of the fact that the maximum trust which a decision-maker can do upon some opinion leader is his/her truthfulness. On the basis of this definition, a trust score matrix is constructed and the influence model is modified to take into account that matrix.

A new model for evaluating the influence of social networks, social learning, and supportive policies on the desire of women for fertility

BACKGROUND: Nowadays, social networks become so famous and attract a lot of users. In recent eras, the increase of online social networks and the digitization of communication types have meant that online social networks have become a significant part of social network examination. OBJECTIVE: In this paper, we investigate the social networks to study the desire of women for fertility. The study has delivered new visions into the elements of reproductive behavior and has discussed the development of increasingly refined and realistic theories of fertility desire. METHODS: A questionnaire is intended for evaluating the elements of the model. Questionnaires were reviewed by experts with significant experiences in this domain. From 384 users of Telegram as an important social network in Iran, data are collected. For statistical examination, the SPSS 22 and SMART- PLS 3.2 software are also utilized. RESULTS: Results confirmed the validity of the model for assessing of the desire of women for fertility. The outcomes have indicated that the social network has a negative effect on the desire of women for fertility. Besides, the results have shown that the role of social networks on social learning is significant and positive. Furthermore, the role of social learning and supportive policies on the desire of women for fertility is positive and significant. CONCLUSIONS: According to findings, managers have enough precision in training women and daughters through social networks and social learning to enhance the desire for fertility. Finally, it is significant to note that since data are self-reported, they could be affected by rationalization and may not correlate with fertility behavior. In future studies, by gathering a comprehensive sample, other important elements can be considered that cause the desire of women for fertility.

Corporate Reputation of Family-Owned Businesses: Parent Companies vs. Their Brands

The reputation of companies is one of their key success factors. It is therefore necessary to value this intangible asset. In order to detect possible threats quickly, continuous monitoring of corporate reputation plays an important role in this valuation process. Family businesses are an ideal object for reputation management research, as through their brands they integrate tradition and addressability at the same time. The main aim of the paper is to discuss the issue of innovative approaches to the online reputation management. We performed an in-depth analysis of online reputation through an Advanced sentiment analysis on the significant sample of ten largest family-owned businesses in the world. Taking into account all relevant determinants of reputation such as Google as well as major social networks, namely Facebook, Twitter, YouTube, and LinkedIn. As there is a noticeable difference between the marketing communication of the parent company and the marketing communication of the brand owned by the company, the findings of the analyses will provide a better insight into the issue of sustainable brand development. By identify good practices, as well as highlighting weaknesses, our research has the ambition to contribute to the shift of knowledge in the field of reputation management.

Stochastic events can explain sustained clustering and polarisation of opinions in social networks

Understanding the processes underlying development and persistence of polarised opinions has been one of the key challenges in social networks for more than two decades. While plausible mechanisms have been suggested, they assume quite specialised interactions between individuals or groups that may only be relevant in particular contexts. We propose that a more broadly relevant explanation might be associated with the influence of external events. An agent-based bounded-confidence model has been used to demonstrate persistent polarisation of opinions within populations exposed to stochastic events (of positive and negative influence) even when all interactions between individuals are noisy and assimilative. Events can have a large impact on the distribution of opinions because their influence acts synchronistically across a large proportion of the population, whereas an individual can only interact with small numbers of other individuals at any particular time.

A novel genetic algorithm-based improvement model for online communities and trust networks

Social network analysis offers an understanding of our modern world, and it affords the ability to represent, analyze and even simulate complex structures. While an unweighted model can be used for online communities, trust or friendship networks should be analyzed with weighted models. To analyze social networks, it is essential to produce realistic social models. However, there are serious differences between social network models and real-life data in terms of their fundamental statistical parameters. In this paper, a genetic algorithm (GA)-based social network improvement method is proposed to produce social networks more similar to real-life data sets. First, it creates a social model based on existing studies in the literature, and then it improves the model with the proposed GA-based approach based on the similarity of the average degree, the k-nearest neighbor, the clustering coefficient, degree distribution and link overlap. This study can be used to model the structural and statistical properties of large-scale societies more realistically. The performance results show that our approach can reduce the dissimilarity between the created social networks and the real-life data sets in terms of their primary statistical properties. It has been shown that the proposed GA-based approach can be used effectively not only in unweighted networks but also in weighted networks.