Spread Of Social Networks Research Articles

An application of Rumor Propagation In Google trends

Daley and Kendle’s pioneering mathematical basis for modelling rumor propagation around 1965 has paved a strong platform for researchers to come up with new generalizations for practical scenarios of rumor spreading in social networks. In this study, we use a generalization of original DK model, Ignorant-Spreader-Stifler (ISS) model to analyze the cricket related searches in Sri Lanka on Google.The secondary data used for the study were obtained from Google trends for a five-year period from 2012 to 2017, targeting the three t20 matches held during the period. We introduce two algorithms on Maple mathematical software to estimate the parameters of the ISS model numerically. We observe that the way people search on web about cricket is roughly analogous to spreading a rumour and forecasting is better when modelling with most recent data than considering more data back in time and we also find that the predicting accuracy is significantly equal of the two models built considering the data in 2012 and 2014.

Key nodes of misinformation source inference: A message-passing-based approach

The misinformation spreading in social networks causes unpredictable damage to the networked system, thus inferring the misinformation source is an important research topic in the field of network science and security. Many source inference algorithms have been proposed to find the most likely propagation source through observable snapshot. However, under limited observable conditions, observing different nodes states markedly affects the algorithm’s effectiveness. Yet, we still lack relevant research on which nodes can more accurately assist us in completing source inference. Here, we propose the heuristic message-passing-based algorithm to find the key nodes that can maximize the accuracy of source inference, which uses the average rank of the source in the message-passing method as a measure and performs continuous annealing on this basis to update the set. As a comparison, we propose random selection algorithm as the basic, high-eigenvalue algorithm and high-degree algorithm focused on centrality, and basic message-passing-based algorithm from the perspective of energy entropy in message passing. Through extensive numerical simulation on artificial and real-world networks, compared with other four algorithms, our heuristic message-passing-based algorithm finds the optimal key node set that can more accurately complete source inference. Moreover, it has over 8% higher inference accuracy than other methods in low visibility situations especially.

Popularity of Social Networks Among Armenian Users

Now there is active debate about the influence of virtual (online) communication on real (offline) communication. Do online and offline communications complement each other, or will online communication gradually completely replace real (offline) communication, including in the professional and personal life of a modern person? If people stay at home and go about their daily activities without interacting with other members of society and this suit them, then they will not make efforts to create social connections outside the online sphere, and the field of real (offline) relationships is completely or significantly transformed. The article discusses: the spread of social networks among Armenian users, online and/or offline communication preferences of Armenian users in one or another area of life, depending on age, gender and the subject of communication. As a result of the study, conducted by questionnaire, preferences were identified on the basis of which users build online communication, which determines the purposes of using social networks in Armenia.The research was carried out with the financial support of Yerevan State University according to the scientific project “Mechanisms of dissemination and consumption of fake news in social networks”, 2022-2024. The article was prepared according to the International Academic Mobility Program, organized by the National Research Institute of the Communications Development for young scientists from foreign countries.

ВЗАЄМОЗВ’ЯЗОК САМООЦІНКИ ТА САМОПРЕЗЕНТАЦІЇ МОЛОДІ У СОЦІАЛЬНИХ МЕРЕЖАХ

The article describes the features of correlation of self-esteem and self-attitude of youth that uses social networks. The phenomenon of self-presentation has acquired great significance and relevance due to the emergence and spread of social networks that involve a demonstration of one’s Self in the dimension of social networks and taking of a certain position. In modern world, social networks help young people feel connected to others and present themselves as individuals. Self-esteem is an assessment that a person gives to themselves, their qualities and abilities, as well as their place in the social environment. A person's self-esteem shows their social adaptation, it plays a role of a regulator of behaviour and activity that accumulates the entire life experience of an individual. The phenomenon of self-presentation may be interpreted within one of three paradigms: 1) self-presentation is defined as a general characteristic of an individual's social behaviour, that is, purposeful and conscious behaviour aimed at creating a certain impression on others; the process or the subject’s constant desire to present the desired image to the external and internal public); 2) self-presentation as a mean for elimination of cognitive dissonance; 3) there is a relation between person’s self-presentation and self-awareness. The results of an empirical study of the features of the correlation between self-esteem and self-presentation of young people in social networks are presented. It was found empirically that half of the interviewees had a low level of self-esteem, it was also found that the self-presentation tactics of the assertive type predominate in terms of manifestation, and the tactics of the defensive type acquired comparatively less manifestation. In accordance with the results of the correlation analysis, it was determined that when the level of self-esteem increases, young people use defensive type defensive type of self-presentation less. In other words, when the level of self-esteem increases, young people begin to lean towards self-presentation strategies aimed primarily at managing impressions, and not at protecting their own image in the eyes of others.

The influence of social communications on the formation of public opinion of citizens during the war

With the development of information technology and the spread of social networks, communication has become more accessible and faster, and therefore, social communications are becoming an increasingly relevant and important issue for modern society. The research aims to study the current state of social communications in Ukrainian and global society, considering their transformation and identifying development trends. The research was conducted using the methods of analysis, systematisation, deduction, generalisation, and surveying. The research provides knowledge on the current state of social communications in Ukrainian and global society. Various aspects of communication processes, their transformation and development trends were studied and analysed, which allowed to understand their role and impact on modern society. The study revealed the peculiarities of social communications in different contexts, such as war, revolution, and pandemic. Particular attention was paid to the impact of social media platforms, such as Facebook, Twitter, Instagram, LinkedIn, and TikTok, on the formation of public opinion, cultural stereotypes, and types of users on social media. The study was conducted to investigate the social communications of Ukrainians, including their activity on social media. The study helped to establish how social communications affect the interaction of various social groups and institutions, including political processes, civic engagement, and youth culture, as well as trends in social communications. The results of the study of social communications can be used by researchers to expand knowledge in the field of communications, develop new theories and approaches, and understand modern communication processes in society.

Multi pathways temporal distance unravels the hidden geometry of network-driven processes

Network-based interactions allow one to model many technological and natural systems, where understanding information flow between nodes is important to predict their functioning. The complex interplay between network connectivity and dynamics can be captured by scaling laws overcoming the paradigm of information spread being solely dependent on network structure. Here, we capitalize on this paradigm to identify the relevant paths for perturbation propagation. We introduce a multi-pathways temporal distance between nodes that overcomes the limitation of focussing only on the shortest path. This metric predicts the latent geometry induced by the dynamics in which the signal propagation resembles the traveling wave solution of reaction-diffusion systems. We validate the framework on a set of synthetic dynamical models, showing that it outperforms existing approaches in predicting arrival times. On a set of empirical contact-based social systems, we show that it can be reliably used also for models of infectious diseases spread - such as the Susceptible-Infected-Susceptible - with remarkable accuracy in predicting the observed timing of infections. Our framework naturally encodes the concerted behavior of the ensemble of paths connecting two nodes in conveying perturbations, with applications ranging from regulatory dynamics within cells to epidemic spreading in social networks.

La costruzione del sociale nell’epoca della postrealtà

In a contemporaneity in progress, the emergence of disruptive phenomena, unexpected and not underlying the rules of control and immediate quantification, induces the subject to a continuous representation and participation in the public debate, driven by the need to regain possession, even if only apparently, of the space of a mixture in which reality and fiction are contaminated in a relationship of continuous exchange until it is impossible to resolve their factuality and their construction. The intent is to theoretically outline the contours and the correlation phenomena between the understanding and manipulation of reality and the elements of communication, identity formation and social implications through the spread of social networks, deep fakes and artificial intelligence in the post-reality period.

CRIMINALIZATION OF THE INFORMATION SPACE AND CRIMINAL ACTIVITIES OF EXTREMIST GROUPS IN SOCIAL NETWORKS

Despite all its advantages, the information space is a huge public danger, since, in particular, it is used to commit offenses. Offenses committed in the information space are characterized by high latency, since law enforcement agencies do not have enough opportunities to investigate and disclose these crimes, since they can be committed on the territory of one country, while the location of the offender may be on the territory of a completely different country or continent.This article discusses the criminalization of the information space and the criminal activity of extremist groups in social networks.The relevance of this article lies in the fact that this article examines the problems that have appeared with the spread of social networks, and what kind of public danger they pose. Crimes committed through the use of the Internet are characterized by high latency and pose huge problems for law enforcement agencies. To combat Internet crime, States need to improve the technical support of their own law enforcement agencies.

The Role of the Media in Managing Wars – An Analytical Study

Media and war, two words intertwined throughout different historical eras, the media played a vital and pivotal role in managing wars and armed conflicts, the media was employed as a means of mobilization and mobilization, preparation and control of events, organizing the flow of information, mobilizing consensus among the elites, and psychological warfare, and to the extent that techniques and tools have developed Wars throughout the different historical eras, technologies and media have developed to the same extent. At the present time, the importance of the media in managing wars and armed conflicts has increased due to the spread of digital media and social media (new media), and the media in the current era, known as the era of crises, has become the most important tools for managing wars and conflicts. In the era of algorithms, new digital technologies and the spread of social networks, wars and armed conflicts have become media wars.

СОЦІАЛЬНІ МЕРЕЖІ ЯК КАТАЛІЗАТОРИ ДЕМОКРАТИЧНИХ ПРОЦЕСІВ У СУСПІЛЬСТВІ

<p><strong><em>The purpose</em></strong><em> of the study is to conduct a detailed scientific and practical substantiation of the features of social networks as catalysts for democratic processes in society due to modern conditions in the framework of globalization processes.</em></p><p><strong><em>Research methodology.</em></strong><em> The study used general scientific (generalization, comparison, induction and deduction) and empirical-theoretical methods (analysis, synthesis). The use of system-structural and statistical analysis made it possible to differentiate the main ratings of social network platforms, the number of social network users for the period 2012–2022 and the penetration of social networks by region in January 2022. Based on the survey, it was determined which social network citizens of Ukraine use to receive news.</em></p><p><strong><em>Results.</em></strong><em> It is determined that social networks are becoming a global phenomenon of communication and are developing independently and in an extremely dynamic environment of new innovative technologies. It is established that social networks are a segment of communication and socialization, which influences the formation of new social structures. It is determined that social networks are part of the public discourse, which allows to directly and publicly respond to political processes in society, which opens the possibility of political influence. The analysis found that the use of social networks is far from evenly distributed around the world, and the total number of social network users around the world has almost doubled from 1.48 billion in 2012 to 4.62 billion in 2022.</em></p><p><strong><em>Novelty. </em></strong><em>The scientific novelty of the results obtained in the article is due to the solution of an important scientific problem, which is to develop theoretical principles and practical recommendations for analyzing the features of social networks as catalysts for democratic processes in society. The article further develops the study on the main indicators of the spread of social networks in the global and national dimensions.</em></p><p><strong><em>Practical significance. </em></strong><em>The results of the study can be used in the study of the scientific field of «Social Communications». The results can be directed to further research of social networks as catalysts of democratic processes in society.</em></p><p><strong><em>Key words:</em></strong><em> social networks, catalysts, democratic processes, society.</em></p>

Influence maximization problem by leveraging the local traveling and node labeling method for discovering most influential nodes in social networks

The influence maximization problem has gained particular importance in viral marketing for large-scale spreading in social networks. Developing a fast and appropriate algorithm to identify an optimized seed set for the diffusion process on social networks is crucial due to the fast growth of networks. Most fast methods only focus on the degree of nodes while ignoring the strategic position of nodes in the networks. These methods do not have the required quality in finding a seed set in most networks. On the other hand, many other methods have acceptable quality, but their computational overhead is significant. To address these issues, the main concentration of this paper is to propose a fast and accurate method for the influence maximization problem, which uses a local traveling for labeling of nodes based on the influence power, called the LMP algorithm. In the proposed LMP algorithm, first, a travel starts from a node with the lowest influence power to assign a ranking-label for this node and its neighbor nodes in each step based on their diffusion capability and strategic position. The LMP algorithm uses node labeling steps to reduce search space significantly. Three ranking-labels are used in the proposed algorithm, and nodes with the highest ranking-label are selected as candidate nodes. This local and fast step strictly reduces the search space. Finally, the LMP algorithm selects seed nodes based on the topology features and the strategic position of the candidate and connector. The performance of the proposed algorithm is benchmarked with the well-known and recently proposed seed selection algorithms. The experimental results are performed on real-world and synthetic networks to validate the efficiency and effectiveness. The experiments exhibit that the proposed algorithm is the fastest in comparison with other state-of-the-art algorithms, and it has linear time complexity. In addition, it can achieve a good tradeoff between the efficiency and time complexity in the influence maximization problem.

SMM strategies of domestic higher education institutions

Based on the trends in the development and spread of social networks in public life, it is undeniable that the use of the advantages of these platforms has a positive effect on the marketing of any business and, in particular, higher education institutions. The development of an effective strategy for higher education institutions allows solving a series of important tasks, with which other tools, due to their origin and mechanism of influence, may not work so effectively. Formation of an effective marketing strategy in social networks by higher education institutions brings them closer to the target audience and stakeholders, makes them more open to society, allows flexible response to requests, etc. The effectiveness of the SMM strategy depends no less on the correct choice of a particular social network by a higher education institution. Knowledge of the advantages of the target audiences, the digital behavior of stakeholders' representatives, and their expectations allow the institution to gain additional competitive advantages. The results of marketing studies of practical activities of higher education institutions in Ukraine in the marketing of social networks allow to predict their development and identify priority development areas. According to them, YouTube and Instagram are the most popular social networks chosen by domestic higher education institutions in Ukraine. However, the very development of social networks and the loyalty of different target groups to different social networks inclines the leadership of domestic higher education institutions to conduct targeted activities in other social networks, in particular in TikTok and Telegram. According to the purpose and the chosen social network, the communication strategy of the higher education institution is being formed. According to the results of the research of the sites of domestic higher education institutions, a number of problems and their causes are revealed. The article proposes an algorithm for the development and implementation of the SMM strategy by domestic educational institutions and provides practical recommendations for communication and interaction with potential applicants in social networks.

Blurring the line between publicity and privacy on social media and the privacy paradox

The article examines the situation associated with the spread of social networks, which brought not only new communication opportunities, but also the risks of blurring the boundaries between privacy and publicity. People voluntarily share personal data in exchange for public acceptance. This information is recorded and studied by various government and commercial institutions. The danger to information privacy as a right to control access to personal information is aggravated by the peculiarities of online communication, which is characterized by “context collapse”: the merging of different audiences with different norms and values. Content posted on social media is searchable beyond a specific point in time and situation. If offline communication involves a foreseeable number of interlocutors, there is an “invisible audience” on social networks, which leads to information asymmetry. However, despite the fact that most users are aware of the potential dangers of privacy breaches, they share personal information on social networks. This phenomenon is called the privacy paradox. The reasons for this behavior are a lack of technical and social skills, a reluctance to spend time and energy on measures to minimize risks, a desire to have wide social connections and skepticism about the effectiveness of the efforts being made. The behavior of users on social networks is influenced primarily by factors such as age and education. The most concerned about the preservation of privacy are young people and middle-aged people, as they have to manage the most complex social relations.

A quick GRASP-based method for influence maximization in social networks

The evolution and spread of social networks have attracted the interest of the scientific community in the last few years. Specifically, several new interesting problems, which are hard to solve, have arisen in the context of viral marketing, disease analysis, and influence analysis, among others. Companies and researchers try to find the elements that maximize profit, stop pandemics, etc. This family of problems is collected under the term Social Network Influence Maximization problem (SNIMP), whose goal is to find the most influential users (commonly known as seeds) in a social network, simulating an influence diffusion model. SNIMP is known to be an mathcal {NP}-hard problem and, therefore, an exact algorithm is not suitable for solving it optimally in reasonable computing time. The main drawback of this optimization problem lies on the computational effort required to evaluate a solution. Since each node is infected with a certain probability, the objective function value must be calculated through a Monte Carlo simulation, resulting in a computationally complex process. The current proposal tries to overcome this limitation by considering a metaheuristic algorithm based on the Greedy Randomized Adaptive Search Procedure (GRASP) framework to design a quick solution procedure for the SNIMP. Our method consists of two distinct stages: construction and local search. The former is based on static features of the network, which notably increases its efficiency since it does not require to perform any simulation during construction. The latter involves a local search based on an intelligent neighborhood exploration strategy to find the most influential users based on swap moves, also aiming for an efficient processing. Experiments performed on 7 well-known social network datasets with 5 different seed set sizes confirm that the proposed algorithm is able to provide competitive results in terms of quality and computing time when comparing it with the best algorithms found in the state of the art.

Fundamental Power of Social Media Interactions for Building a Brand and Customer Relations

The spread of social networks and the high level of penetration of digital content and mobile devices have created a society in which consumers expect constant communication from companies. In addition, communication on social media is often accompanied by the hope of being able to interact on an equal footing. In this way, companies hope that the use of social networks will not only increase sales, but also increase attention, brand page traffic, and customer loyalty. The purpose of this study was to provide the grounds for identifying the correlations between the interactive potential of social media based on dialogue-oriented communication and the quality of relationships with online customers. To achieve this goal, an online questionnaire was used in which 604 respondents from Romania participated (204 social media managers and 400 online customers). The results of the study showed that it is particularly important for companies to understand the role of interactive communication in social media and to thus build a bridge between the orientation of communication towards dialogue and relationships with online customers. It was found that the correct use of social networks could also support the achievement of public relation (PR) objectives. In addition, this study has implications for the development of customer-oriented online communication strategies by Romanian companies.

SELF-PRESENTATION ON SOCIAL NETWORK SITES, BIG FIVE AND DARK TRIAD PERSONALITY TRAITS

The spread of social networks as a new environment allows representing self-image and psychological characteristics more or less different from reality. The studies of social network sites (SNS) confirm that self-presentation on the SNS is characterized by using specific strategies to display and regulate information about oneself. Self-monitoring and perfectionistic self-presentation can be considered essential characteristics of self-presentation on a social network site profile. Various connections between personality traits and characteristics of self-presentation on the SNS are revealed. Many studies replicate the relations between extraversion, neuroticism, and narcissism with social network activity and self-image representation. In the current study, we examined the relationship between the characteristics of self-presentation on a social network (realistic demonstrative self and fake deceiving self) with the Big Five and the Dark Triad traits. The characteristics of self-presentation and personality traits were assessed on a sample of 478 undergraduate students. Generally, a realistic demonstrative self is associated with the desire to show the best side of self (self-monitoring), avoidance of self-discussion, while a fake deceiving self reveals connections focusing on others' reactions to self-presentation. Consciousness, neuroticism, openness, and narcissism are predictors for realistic, demonstrative self-presentation, and conscientiousness and Machiavellianism are predictors for false, deceptive self-presentation. Thus, realistic demonstrative self-presentation is associated with controlling the information posted in the network and the desire to arouse admiration, and false self-presentation manifesting as a purposeful strategy for creating an unrealistic image.

Legaltech in Public Administration: Prospects and Challenges

The information revolution affects every aspect of our life, such as communication, banking, learning and teaching, entertainment, socializing, and even government and the administration. In some fields the potential of such revolution is already apparent, as new kinds of value is created - suffice it to mention the new forms of communication and marketing, the emergence of new business models in the financial industry, the so-called fintechs, and the spread of social networks. Likewise, information technology creates opportunities and disrupts even the legal industry, with the emergence of legaltech or lawtech. Despite such growing attention, in the field of public administration, the potential uses of new technologies have been for a long time only discussed in theory, and limited implementation has been recorded (in most of the cases, in an experimental or pilot phase). As Part 2 will show, such technologies in the best-case scenario were tested at an experimental level, and only in a few countries some small steps were taken to implement them. A rapid shift, nonetheless, took place with the imposed lockdowns due to the Covid-19 pandemic. Such pandemic will possibly leave its mark not only in history books and medicine textbooks, but also in the field of administration and public law, as it spurred the implementation of the technologies of the information revolution era.

New prediction method for data spreading in social networks based on machine learning algorithm

Information diffusion prediction is the study of the path of dissemination of news, information, or topics in a structured data such as a graph. Research in this area is focused on two goals, tracing the information diffusion path and finding the members that determine future the next path. The major problem of traditional approaches in this area is the use of simple probabilistic methods rather than intelligent methods. Recent years have seen growing interest in the use of machine learning algorithms in this field. Recently, deep learning, which is a branch of machine learning, has been increasingly used in the field of information diffusion prediction. This paper presents a machine learning method based on the graph neural network algorithm, which involves the selection of inactive vertices for activation based on the neighboring vertices that are active in a given scientific topic. Basically, in this method, information diffusion paths are predicted through the activation of inactive vertices byactive vertices. The method is tested on three scientific bibliography datasets: The Digital Bibliography and Library Project (DBLP), Pubmed, and Cora. The method attempts to answer the question that who will be the publisher of thenext article in a specific field of science. The comparison of the proposed method with other methods shows 10% and 5% improved precision in DBL Pand Pubmed datasets, respectively.

Understanding adversarial robustness via critical attacking route

Deep neural networks (DNNs) are vulnerable to adversarial examples which are generated by inputs with imperceptible perturbations. Understanding adversarial robustness of DNNs has become an important issue, which would for certain result in better practical deep learning applications. To address this issue, we try to explain adversarial robustness for deep models from a new perspective of critical attacking route, which is computed by a gradient-based influence propagation strategy. Similar to rumor spreading in social networks, we believe that adversarial noises are amplified and propagated through the critical attacking route. By exploiting neurons’ influences layer by layer, we compose the critical attacking route with neurons that make the highest contributions towards model decision. In this paper, we first draw the close connection between adversarial robustness and critical attacking route, as the route makes the most non-trivial contributions to model predictions in the adversarial setting. By constraining the propagation process and node behaviors on this route, we could weaken the noise propagation and improve model robustness. Also, we find that critical attacking neurons are useful to evaluate sample adversarial hardness that images with higher stimulus are easier to be perturbed into adversarial examples.

Opinion Formation on the Internet: The Influence of Personality, Network Structure, and Content on Sharing Messages Online

Today the majority of people uses online social networks not only to stay in contact with friends, but also to find information about relevant topics, or to spread information. While a lot of research has been conducted into opinion formation, only little is known about which factors influence whether a user of online social networks disseminates information or not. To answer this question, we created an agent-based model and simulated message spreading in social networks using a latent-process model. In our model, we varied four different content types, six different network types, and we varied between a model that includes a personality model for its agents and one that did not. We found that the network type has only a weak influence on the distribution of content, whereas the message type has a clear influence on how many users receive a message. Using a personality model helped achieved more realistic outcomes.