Strategies for Social Networks Modeling

The aim of the study is a practical analysis of social network services as a place to store multimedia resources that allow you to store and share digital photos, audio and video recordings, text files, presentations, and organize resource discussions for free. They can be used as educational materials (educational videos, photos on various topics, audio and video recordings of scientists' speeches, eyewitnesses of events, etc.), as well as means to store student video, photo, audio files and creative works. Method. Theoretical materials of the research are the best practices of foreign and domestic scientists in the field of network communications and their impact on the future specialists preparing in geography and tourism, namely articles, monographs, abstracts, dissertations. The study uses the method of theoretical materials analysis, as well as methods of observation, abstraction in identifying the qualification characteristics of social Internet networks. Content analysis (analysis of the social networks community messages content); statistical and comparative information analysis was carried out. Empirical research on the social Internet networks lies in creating and filling a community on the social Internet network, as well as engaging students in that community. Results. In the research it was found out that social networks can be used to solve the following tasks: to organize the collective work of students in the classroom (and outside it), which will be favourable to the communication, to gain the team experience; to ensure the development of the students personalized learning environment; to promote students independent learning, as everyone works in his own rhythm; to promote informal communication between the tutor and the students; to change the tutor's role, who will become an assistant, friend, mentor; to create digital educational content of the disciplines. The scientific novelty of the obtained results lies in the fact that on the comprehensive analysis of social internet networks, internet blogs, tourist internet resources basis, their place and role in the information and educational environment of higher education institutions are determined, in particular: the specifics of the content, prospects and mechanisms of the development of the social Internet networks from the point of view of training future tourists are determined; the dependence of the functions of the Social Internet Network, Internet blogs on their structural elements is established. Practical significance. The obtained scientific results are used in the educational process of future specialists in the field of training "Tourism", with the educational qualification "tourism expert, organizer of excursion and animation activities" when teaching the disciplines "study of local lore and tourism", "tourist work", "recreational geography", "geography of Ukraine", "professional competencies in tourism business", "organization of tourist activities". The research practices can be used to conduct tourist, museum, local history, excursion and other types of student's practice.

Modeling, analysis, control, and management of complex social networks represent an important area of interdisciplinary research in an advanced digitalized world. In the last decade social networks have produced significant online applications which are running on top of a modern Internet infrastructure and have been identified as major driver of the fast growing Internet traffic. The "Second International Workshop on Modeling, Analysis and Management of Social Networks and Their Applications" (SOCNET 2018) held at Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany, on February 28, 2018, has covered related research issues of social networks in modern information society. The Proceedings of SOCNET 2018 highlight the topics of a tutorial on "Network Analysis in Python" complementing the workshop program, present an invited talk "From the Age of Emperors to the Age of Empathy", and summarize the contributions of eight reviewed papers. The covered topics ranged from theoretical oriented studies focusing on the structural inference of topic networks, the modeling of group dynamics, and the analysis of emergency response networks to the application areas of social networks such as social media used in organizations or social network applications and their impact on modern information society. The Proceedings of SOCNET 2018 may stimulate the readers' future research on monitoring, modeling, and analysis of social networks and encourage their development efforts regarding social network applications of the next generation.

With the advent of social network services such as Twitter, Facebook, Tumbler, and Google+, the research on social network and media analysis has been greatly advanced. In recent years, the interactions among people, sharing of knowledge and experiences, community activities in social network services increase greatly, which would make the research on social networks more important. Furthermore, as social media contents within social network services are rapidly being produced and consumed, the social media contents now account for the majority of content published on the world wide web. Social media is differentiated from traditional media in many aspects such as its frequency, quality, usability, immediacy, and permanence, which leads to significant potential to the social media analysis research.

The aim of this study is to reveal Open Education related social media usage in Turkey through social network analyses. To this end, the most widely used social media network in Turkey, Facebook, was chosen. All the pages and groups created on Facebook related to Open Education were found. A total of 207 groups and 521 pages were accessed and entered into the social network analysis. Within the scope of this study, the density and centrality features of the network were analyzed for groups whereas the density, centrality, and degrees features of the network were analyzed for pages. The study's results revealed that while pages and groups related to programs emphasizing current job opportunities and student support services were more actively used, commercial accounts attempted to exploit students' exam anxiety instead of generating actual content. The findings also revealed that well structured institutional pages appeal to students and have the potential to reach high levels of popular in short periods of time.

Social media provides a fertile ground for any user to find or share information about various events with others. At the same time, social media is not always used for benign purposes. With the availability of inexpensive and ubiquitous mass communication tools, disseminating false information and propaganda is both convenient and effective. In this research, we studied Online Deviant Groups (ODGs) that conduct cyber propaganda campaigns in order to achieve strategic and political goals, influence mass thinking, and steer behaviors or perspectives about an event. We provide case studies in which various disinformation and propaganda swamped social media during two NATO exercises in 2015. We demonstrate ODGs’ capability to spread anti-NATO propaganda using a highly sophisticated and well-coordinated social media campaign. In particular, blogs were used as virtual spaces where narratives are framed. And, to generate discourse, web traffic was driven to these virtual spaces via other social media platforms such as Twitter, Facebook, and VKontakte. By further examining the information flows within the social media networks, we identify sources of mis/disinformation and their reach, i.e., how far and how quickly the mis/disinformation could travel and consequently detect manipulation. The chapter presents an in-depth examination of the information networks using social network analysis (SNA) and social cyber forensics (SCF) based methodologies to identify prominent information brokers, leading coordinators, and information competitors who seek to further their own agenda. Through SCF tools, e.g., Maltego, we extract metadata associated with disinformation-riddled websites. The extracted metadata helps in uncovering the implicit relations among various ODGs. We further collected the social network of various ODGs (i.e., their friends and followers) and their communication network (i.e., network depicting the flow of information such as tweets, retweets, mentions, and hyperlinks). SNA helped us identify influential users and powerful groups responsible for coordinating the various disinformation campaigns. One of the key research findings is the vitality of the link between blogs and other social media platforms to examine disinformation campaigns.

Experts increasingly use social media to communicate with the wider public, prompted by the need to demonstrate impact and public engagement. While previous research on the use of social media by experts focused on single topics and performed sentiment analysis, we propose to extend the scope by investigating experts’ networks, topics and communicative styles. We perform social and semantic network as well language analysis of top tweeting scientists and economists. We find that economists tweet less, mention fewer people and have fewer Twitter conversations with members of the public than scientists. Scientists use a more informal and involved style and engage wider audiences through multimedia contents, while economists use more jargon, and tend to favour traditional written media. The results point to differences in experts’ communicative practices online, and we propose that disciplinary ways of ‘talking’ may pose obstacles to an effective public communication of expert knowledge.

Community based churn prediction, or the assignment of recognising the influence of a customer’s community in churn prediction has become an important concern for firms in many different industries. While churn prediction until recent times have focused only on transactional dataset (targeted approach), the untargeted approach through product advisement, digital marketing and expressions in customer’s opinion on the social media like Twitter, have not been fully harnessed. Although this data source has become an important influencing factor with lasting impact on churn management. Since Social Network Analysis (SNA) has become a blended approach for churn prediction and management in modern era, customers residing online predominantly and collectively decide and determines the momentum of churn prediction, retention and decision support. In existing SNA approaches, customers are classified as churner or non-churner (1 or 0). Oftentimes, the customer’s opinion is also neglected and the network structure of community members are not exploited. Consequently, the pattern and influential abilities of customers’ opinion on relative members of the community are not analysed. Thus, the research developed a Churn Service Information Graph (CSIG) to define a quadruple churn category (churner, potential churner, inertia customer, premium customer) for non-opinionated customers via the power of relative affinity around opinionated customers on a direct node to node SNA. The essence is to use data mining technique to investigate the patterns of opinion between people in a network or group. Consequently, every member of the online social network community is dynamically classified into a churn category for an improved targeted customer acquisition, retention and/or decision supports in churn management.

In recent years, with the rapid development of the Internet and cloud computing, an enormous amount of information is exchanged on various social networking services. In order to handle and maintain such a mountain of information properly by limited resources in the network, it is very important to comprehend the dynamics for propagation of information or activity on the social network. One of many indices used by social network analysis which investigates the network structure is “Node centrality”. A common characteristic of conventional node centralities is that it depends on the topological structure of network and the value of node centrality does not change unless the topology changes. In our recent research, we have proposed an oscillation model for describing dynamics on networks generated from a certain kind of asymmetric interaction between nodes by using a symmetric matrix. Moreover, we proposed a new extended index of well-known two node centralities based on the oscillation model. In this paper, we discuss generally extension of node centralities based on the oscillation model. In addition, we show that the proposed index can describe various node centralities by properly assigning the weight of each link, and the proposed model is regarded as the fundamental framework for different node centralities.

Social media network analysis has become very popular in recent years. How do real networks evolve over time? What are the normal evolving behaviors in a social media network? In order to extract behaviors occurring regularly to reveal the microscopic evolving properties in social networks, the evolving process of networks is modeled as stochastic states transition, and the evolving behaviors are described as topological structure changes of a series of sub graphs. Then, based on Maximal Frequent Sub graph mining technology, RB Miner (Regular Behaviors Miner) algorithm is proposed to identify such regular behaviors in network dataset. The empirical evaluation using both synthetic and real dataset verifies that the proposed algorithm is valid, and the regular behavior patterns show more dynamic information hidden in evolving social networks than normal frequent sub graph patterns.

This paper aims to increase students' understanding of Tarakanita 2 Pluit High School regarding the benefits, network analysis and long-term impact of social media connections. The problems identified involve students' lack of understanding of the positive and negative benefits of social media network analysis. Community education methods were chosen as the main approach to achieve this goal. This method involves a seminar activity entitled "Analyzing Networks: A Study of Social Media Network Analysis," which was held on November 15 2023, and was attended by 26 grade 11 students. The focus of this activity was to provide an in-depth understanding of the basic introductory material, characteristics, and methods of social media network analysis. The proposed solution includes three main stages: presentation of the material, practical test demonstration, and awareness of the long-term benefits of social media connections. The material presented includes information regarding the positive and negative benefits of social media network analysis, characteristics of social networks, and analytical methods used to understand relationships between individuals in cyberspace. The practical test demonstration involves student interaction in analyzing social media networks directly. Awareness of the long-term benefits of social media connections is the final focus, with the aim of changing students' mindsets regarding the long-term impact of their online activities. As a result of this activity, it is hoped that there will be an increase in digital literacy and understanding of teenagers' social media, as well as providing deeper insight into relationships between individuals in cyberspace. Thus, this paper contributes to building positive awareness of social media use among high school students.

Software tools for Social Network Analysis (SNA) are being developed which support various types of analysis of social networks extracted from social media websites (e.g., Twitter). Once extracted and stored in a database such social networks are amenable to analysis by SNA software. This data analysis often involves searching for occurrences of various subgraph patterns (i.e., graphical representations of entities and relationships). The authors have developed the Graph Matching Toolkit (GMT) which provides an intuitive Graphical User Interface (GUI) for a heuristic graph matching algorithm called the Truncated Search Tree (TruST) algorithm. GMT is a visual interface for graph matching algorithms processing large social networks. GMT enables an analyst to draw a subgraph pattern by using a mouse to select categories and labels for nodes and links from drop-down menus. GMT then executes the TruST algorithm to find the top five occurrences of the subgraph pattern within the social network stored in the database. GMT was tested using a simulated counter-insurgency dataset consisting of cellular phone communications within a populated area of operations in Iraq. The results indicated GMT (when executing the TruST graph matching algorithm) is a time-efficient approach to searching large social networks. GMT’s visual interface to a graph matching algorithm enables intelligence analysts to quickly analyze and summarize the large amounts of data necessary to produce actionable intelligence.

Social support networks have been become one of the mostly influential metaphors in many areas around the world, especially in terms of mitigating disaster consequences and revivifying disaster affected livelihoods. More importantly, reciprocal supports ties are more powerful in healing disaster wounds of communities. Especially social capital legacies enrich by reciprocal support networks by the ways in which making may hopes among disaster victims. In this context, the objective of this study is to investigate the social support network behaviors in supporting 16 flood-affected households in the mass flooding event occurred in 2017. Particularly, the study researched social support network behaviors at different flood inundation phases such as before, during, and after. This study collected primary data (mainly social network data) using household survey and filed observations. The study used the Social Network Analysis (SNA) method for the network data analysis. This article shows that flood affected households have received social network supports in different magnitudes at before, during, and after flood inundation phases. More importantly, provision of foods, water and basic needs, sheltering, clearing contaminated households, and emotional supports have mobilized and reciprocated among victims in reviving their livelihoods. The social support network legacies have evolved at different flood inundation phases. This study fills the gabs in the flood disaster discourse on Sri Lankan context.

for many users nowadays social media plays a big role in their world, as it helps to connect with new people while keeping the old bonds intact. The drawback is that we can’t able to see that, the data is being generated every minute of a second and that data is kept secure in the hands of a few tech giants, who are using it and selling it as they see fit and using it in a manner as they see profitable and with this paper I’d like to propose that shifting social media from host-centric to the information-centric network on the blockchain would not only be safer yet convenient and secured.In peer-to-peer system, cryptocurrency is used to pay for and secure content acquisition.The blockchain lets individuals acquire ownership over their Data, Property and Assets. In social networking, a decentralised approach to blockchain technology protects privacy and allows for e-commerce, ledger based crowd funding, smart apps, and smart contracts. Platforms like Facebook, LinkedIn, Instagram, Twitter, Reddit, and other digital social network media platforms offer free for all access, which is convenient in the name of connecting and socialising but they are collecting are data and using it to sell it to other third party buyers for the price of our privacySome of the most important difficulties confronting social media network business period, profit is focused on advertising and analytics. False propaganda based information on (Facebook), rampant bullying and harassment (Twitter), single party controlled censorship, and demonetization are some of the most serious issues facing social networking (YouTube).

<p><em>The rapid growth of digital recruitment platforms in Indonesia has generated a large volume of user content on social media, serving as a vital data source for Social Customer Relationship Management (Social CRM) strategies. Consequently, the strategic insights that can be drawn may be limited. This study applied an integrated analytical approach combining Social Network Analysis (SNA) and lexicon-based sentiment analysis to evaluate public interactions regarding Jobstreet, Glints, and Dealls. The research methodology involved collecting data from platform X (previously known as Twitter) during the period of April 1-30, 2025, which was then analyzed using SNA with Gephi to identify influential actors through centrality metrics, alongside sentiment analysis to measure emotional polarity. The main findings revealed that Jobstreet possessed the healthiest conversational ecosystem, characterized by positive and neutral sentiment from its central actors. Glints exhibited sentiment polarization, and Dealls showed reputational vulnerability due to dominant negative sentiment from its influential users. It was concluded that the integration of these two methods provides a robust framework for designing more responsive and data-driven Social CRM strategies.</em></p><p><strong><em>Keywords</em></strong><strong><em>:</em></strong><em> Social Network Analysis; Sentiment Analysis; Social CRM; Digital Recruitment; Lexicon-Based Features.</em><em></em></p><p align="center"><strong>Abstrak</strong></p><p>Perkembangan pesat platform rekrutmen digital di Indonesia telah menghasilkan volume besar konten pengguna di media sosial, yang menjadi sumber data vital untuk strategi <em>Social Customer Relationship Management</em> (<em>Social</em> CRM). Sehingga hal ini dapat menyebabkan insight strategis yang bisa diambil menjadi terbatas. Penelitian ini menerapkan pendekatan analitis terpadu yang menggabungkan <em>Social Network Analysis</em> (SNA) dan analisis sentimen berbasis leksikon untuk mengevaluasi interaksi publik mengenai Jobstreet, Glints, dan Dealls. Metodologi penelitian melibatkan pengumpulan data dari platform X (sebelumnya dikenal dengan Twitter) selama periode 1-30 April 2025, yang kemudian dianalisis menggunakan SNA dengan Gephi untuk mengidentifikasi aktor berpengaruh melalui metrik sentralitas, serta analisis sentimen untuk mengukur polaritas emosional. Temuan utama mengungkapkan bahwa Jobstreet memiliki ekosistem percakapan paling sehat, ditandai oleh sentimen positif dan netral dari aktor-aktor sentralnya. Sebaliknya, Glints menunjukkan polarisasi sentimen, dan Dealls menunjukkan kerentanan reputasi karena sentimen negatif yang dominan dari para pengguna berpengaruhnya. Disimpulkan bahwa integrasi kedua metode ini menyediakan kerangka kerja yang kuat untuk merancang strategi <em>Social</em> CRM yang lebih responsif dan berbasis data.</p><p><strong>Kata Kunci</strong><strong>:</strong> <em>Analisis Jaringan Sosial; Sentimen; Social CRM; Rekrutmen digital; Lexicon-Based Features.</em><em></em></p>

Social media has become an important reference for information during the last few decades. They have been able to be effective in various fields such as business, entertainment, science, crisis management, politics, etc. For this reason, a social media analysis has become very important for researchers and large companies. The widespread use of social media leads to a complex problem called "accumulation of data". Many data science specialists seek to analyze this data in order to identify the behavioral characteristics of users, analyze interests and needs, and improve marketing processes. Different social media platforms have the ability to use all kinds of media, including text data, video, video, audio, and location information, etc. Therefore, data analysis in social networks is very important. In this research, the concepts and applications of data analysis in social networks will be investigated.