Individuals In Social Network Research Articles

Social Media App for Connecting Similar Interests People Using flutter

Understanding interest similarity in Online Social Networks (OSNs) is crucial for various applications. This study addresses the challenge of determining interest similarity on platforms like Facebook, where users may not explicitly disclose their interests. Utilizing a substantial dataset of 479,048 users and 5,263,351 user-generated interests, the research focuses on movies, music, and TV shows. Findings reveal homophily in interest similarity, demonstrating that individuals tend to share more similar tastes when they have comparable demographic information or are connected as friends. A practical prediction model is proposed, facilitating the selection of users with high-interest similarities and enhancing decision-making for OSN applications. Additionally, the paper introduces a novel method using a tag network to connect users with similar interests, outperforming traditional methods by providing a more efficient means of connecting like-minded individuals in social networks. Key Word:Face image synthesis, Generative adversarial network, Face Recognition.

FACTORS AND CHANNELS OF WOMEN'S SOCIAL MOBILITY IN MODERN SOCIETIES

This article provides information about the sources of social mobility, the motivation to achieve, the law of increasing needs, and the hierarchical system of distribution of social results. The presence of channels of social mobility in society, including various social institutions -the military, religion, education, political organizations, trade unions, family, marriage, art, sports, inheritance, elections, mass media, social networks, social networks of individuals and their groups. creates conditions for movement.

REGULATION OF MECHANISMS OF SOCIAL MOBILITY

This article provides information about the sources of social mobility, the motivation to achieve, the law of increasing needs, and the hierarchical system of distribution of social results. The presence of channels of social mobility in society, includingvarious social institutions -the military, religion, education, political organizations, trade unions, family, marriage, art, sports, inheritance, elections, mass media, social networks, social networks of individuals and their groups. creates conditionsfor movement.

SOCIAL PROCESSES SOCIAL MOBILLIKNING MOVEMENT QUALITATIVE STREET

This article provides information about the sources of social mobility, the motivation to achieve, the law of increasing needs, and the hierarchical system of distribution of social results. The presence of channels of social mobility in society, including various social institutions -the military, religion, education, political organizations, trade unions, family, marriage, art, sports, inheritance, elections, mass media, social networks, social networks of individuals and their groups. creates conditions for movement.

Male and stale? Questioning the role of “opinion leaders” in agricultural programs

Social networks can influence people’s behaviour and therefore it is assumed that central individuals in social networks, also called “opinion leaders”, play a key role in driving change in agricultural and food systems. I analyse the outcomes of an intervention (that encouraged Sulawesi smallholder farmers to take a specific action toward improving the health of their cocoa trees) to assess the impact of engaging opinion leaders in agricultural programs that aim to change farmers’ practices. The intervention has been implemented through (a) 18 opinion leaders identified by interviews and a social network survey of 1885 cocoa farmers; and (b) 18 randomly selected farmers who were not central in local social networks. The obtained social networks and statistical data were quantitatively analysed and the results were interpreted with input from the field staff. Contrary to expectations, the highly socially central opinion leaders were not more effective in promoting the initiative in their communities. On average, randomly selected low-centrality farmers convinced almost twice as many of their peers to take the recommended action as compared to the identified opinion leaders (17.1 versus 8.6) but the variation within the random group was also significantly higher. Importantly, while the identified opinion leaders were mostly senior men, women performed better in influencing others into taking action even when their centrality in local social networks of agricultural advice was lower. I discuss the implications of the conventional selection of perceived opinion leaders as model farmers for achieving sustainable and equitable change at scale in agriculture and propose practical alternatives.

Understanding the Transformation of Domestic Architecture Through the Novel “Cevdet Bey and His Sons”

Domestic architecture is a collective product shaped by social networks of individuals, families and societies. But the literature of Turkish housing history, examines housing generally through physical, economic or political factors. Reason for such state can be associated with the difficulty in collecting social data from the past due to the absence of witnesses and tangible materials from the past. Benefiting from sources of other disciplines may give opportunity in obtaining clues about the social life of a previous period. Literature, which contains information about the social life of the past, is one of the disciplines that can contribute to the field of architecture. In this study, 2006 Nobel laureate Orhan Pamuk’s novel “Cevdet Bey and His Sons”, which portrays three different periods with three different housing typologies, is examined. Texts are analyzed over nine factors that İlhan Tekeli (1996) claims to be effective in domestic architecture. When the details obtained from the novel are combined with the literature of housing history, a comprehensive perspective is built upon domestic architecture.

Threshold cascade dynamics on signed random networks

Relationships between individuals in social networks, genes in biological systems, and spins in magnetic systems often reflect a mixture of positive (friendly) and negative (antagonistic) interactions. However, most studies of complex networks have focused on networks consisting solely of positive interactions. Here, we study threshold cascades on signed networks composed of both positive and negative connections, focusing on when a pair of nodes connected by a negative link can only be activated exclusively to each other. We found that the negative interactions not only suppress global cascades, but also induce the heterogeneity in activation patterns manifesting from single-node to network levels. Our results suggest that negative interactions may be an important source of the variability in cascading dynamics.

Network Public Opinion Propagation Control Model of Major Emergencies Based on Heat Conduction Theory

In physics, the process of heat conduction of objects is similar to the process of network public opinion propagation of major emergencies. Based on the theory of heat transfer, this paper analogizes the process of network public opinion information propagation and object heat conduction and analogizes the exchange of network public opinion information between individuals in social networks to the course of object heat transfer. Based on the accumulation of individual energy in social network, the propagation model of network public opinion is established, and the propagation control model of network public opinion in major emergencies is established based on the heat conduction theory. The influence of individual interest, propensity to spread, transmission coefficient of network public opinion information, and the breadth of dissemination on the dissemination of network public opinion in major emergencies are analyzed, respectively. This study provides some inspiration for the disposal and governance of network public opinion, propagation of major emergencies.

Modeling and Analysis of Matthew Effect Under Switching Social Networks via Distributed Competition

Dear editor, This letter models and analyzes the Matthew effect under switching social networks via distributed competition. A competitive strategy is leveraged to trace the evolution of individuals in social systems under the Matthew effect. In addition, a consensus filter is utilized to decentralize the model with undirected graphs used to describe the interactions among individuals in social networks. Further, a term is added to the dynamic consensus filter to compensate for the influence of the switching of communication networks on the model. In this letter, the convergence of the proposed Matthew effect model is theoretically proved, and simulative experiments are conducted to verify the availability of the model. In particular, this letter points out the state and evolution of each individual in social systems with distributed competition, and the influence of the social environment on the development of individuals is also intuitively displayed.

Indywidualny kapitał społeczny i jego znaczenie dla dobrostanu emocjonalnego studentów w czasie pandemii COVID-19

The article presents the results of the research conducted during the COVID-19 pandemic in 2021 among students of the University of Bialystok. The aim of the study was to determine the meaning of individual social capital for the emotional well-being of students. Emotional well-being has been defined as an overall assessment of an individual’s quality of life. Individual social capital is a potential resource of personal and social networks of individuals, which they “use” in various life situations. The study showed an interdependence between the individual social capital of students and their emotional well-being.

Familiarity, dominance, sex and season shape common waxbill social networks

AbstractIn gregarious animals, social network positions of individuals may influence their life-history and fitness. Although association patterns and the position of individuals in social networks can be shaped by phenotypic differences and by past interactions, few studies have quantified their relative importance. We evaluated how phenotypic differences and familiarity influence social preferences and the position of individuals within the social network. We monitored wild-caught common waxbills (Estrilda astrild) with radio-frequency identifiers in a large mesocosm during the non-breeding and breeding seasons of two consecutive years. We found that social networks were similar, and that the centrality of individuals was repeatable, across seasons and years, indicating a stable social phenotype. Nonetheless, there were seasonal changes in social structure: waxbills associated more strongly with opposite-sex individuals in breeding seasons, whereas in non-breeding seasons they instead assorted according to similarities in social dominance. We also observed stronger assortment between birds that were introduced to the mesocosm at the same time, indicating long-lasting bonds among familiar individuals. Waxbills that had been introduced to the mesocosm more recently occupied more central network positions, especially during breeding seasons, perhaps indicating that these birds had less socially-differentiated associations with flock members. Finally, individual differences in color ornamentation and behavioral assays of personality, inhibitory control, and stress were not related to network centrality or association patterns. Together, these results suggest that, in gregarious species like the common waxbill, social networks may be more strongly shaped by long-lasting associations with familiar individuals than by phenotypic differences among group members.

COVID-19 and Networks

Ongoing COVID-19 pandemic poses many challenges to the research of artificial intelligence. Epidemics are important in network science for modeling disease spread over networks of contacts between individuals. To prevent disease spread, it is desirable to introduce prioritized isolation of the individuals contacting many and unspecified others, or connecting different groups. Finding such influential individuals in social networks, and simulating the speed and extent of the disease spread are what we need for combating COVID-19. This article focuses on the following topics, and discusses some of the traditional and emerging research attempts: (1) topics related to epidemics in network science, such as epidemic modeling, influence maximization and temporal networks, (2) recent research of network science for COVID-19 and (3) datasets and resources for COVID-19 research.

Crowd attraction-driven community evolution on social network

The evolution analysis of community structure of social network will help us understand the composition of social organizations and the evolution of society better. In order to discover the community structure and the regularity of community evolution in large-scale social networks, this paper analyzes the formation process and influencing factors of communities, and proposes a community evolution analysis method of crowd attraction driven. This method uses the traditional community division method to divide the basic community, and introduces the theory of information propagation into complex network to simulate the information propagation of dynamic social networks. Then defines seed node, the activity of basic community and crowd attraction to research the influence of groups on individuals in social networks. Finally, making basic communities as fixed groups in the network and proposing community detection algorithm based on crowd attraction. Experimental results show that the scheme can effectively detect and identify the community structure in large-scale social networks.

An Agent-Based Modeling Approach to Brain Drain

The phenomenon of brain drain, that is the emigration of highly skilled people, has many undesirable effects, particularly for developing countries. In this study, an agent-based model is developed to understand the dynamics of such emigration. We hypothesise that skilled people's emigration decisions are based on several factors including the overall economic and social difference between the home and host countries, people's ability and capacity to obtain good jobs and start a life abroad, and the barriers of moving abroad. Furthermore, the social network of individuals also plays a significant role. The model is validated using qualitative and quantitative pattern matching with real-world observations. Sensitivity and uncertainty analyses are performed in addition to several scenario analyses. Linear and random forest response surface models are created to provide quick predictions on the number of emigrants as well as to understand the effect sizes of individual parameters. Overall, the study provides an abstract model where brain drain dynamics can be explored. Findings from the simulation outputs show that future socioeconomic state of the country is more important than the current state, lack of barriers results in a large number of emigrants, and network effects ensue compounding effects on emigration. Upon further development and customisation, future versions can assist in the decision-making of social policymakers regarding brain drain.

To racketeer among neighbors: spatial features of criminal collaboration in the American Mafia

ABSTRACT The American Mafia is a network of criminals engaged in drug trafficking, violence and other illegal activities. Here, we analyze a historical spatial social network (SSN) of 680 Mafia members found in a 1960 investigatory dossier compiled by the U.S. Federal Bureau of Narcotics. The dossier includes connections between members who were ‘known criminal associates’ and members are geolocated to a known home address across 15 major U.S. cities. Under an overarching narrative of identifying the network’s proclivities toward security (dispersion) or efficiency (ease of coordination), we pose four research questions related to criminal organizations, power and coordination strategies. We find that the Mafia network is distributed as a portfolio of nearby and distant ties with significant spatial clustering among the Mafia family units. The methods used here differ from former methods that analyze the point pattern locations of individuals and the social network of individuals separately. The research techniques used here contribute to the body of non-planar network analysis methods in GIScience and can be generalized to other types of spatially-embedded social networks.

Measuring the community value in online social networks

Communities in social networks form with different purposes and play a significant role in interpersonal interactions. Analysis of virtual communities indicates a more precise understanding of the behaviours and desires of individuals in social networks. In this paper, new measures have been proposed for analyzing implicit and explicit communities in Online Social Networks (OSNs). The measures of “potential value of the community members” and “value of the community messages”, which are used for calculating the measure of “community value” are among the most important measures introduced in this paper. Another measure introduced is “user influence rate” in a community, which represents the contribution of a person in creating value in a community. To provide a sound dataset, we collected the information from several real implicit communities in Twitter based on different hashtags. Finally, the suggested measures have been analyzed and compared statistically and behaviourally across different communities. The results of this research well indicate the importance and practicality of the measures introduced in Community analysis of Twitter.

Nüfuz Pazarlaması (Influencer Marketing) ve Satın Alma Davranışı İlişkisi: Sosyal Ağ Kullanıcıları Üzerinden Bir Araştırma

People who succeed in attracting the attention of large masses on social networking platforms with the contents they create and who have the opportunity to reach many people in a short time are called social media celebrities, or influencers, which is the more commonly used term. Brands that noticed these characteristics of social network influencers and want to benefit from less cost and more interaction strategies in marketing have intensified their influence marketing activities. This study aims to determine how influencers are perceived by individuals in social networks and in what direction their current effects affect purchasing behavior. In the research, which has a descriptive approach and is based on a qualitative method, the interview technique was used and in-depth interviews were made through a semi-structured questionnaire with 15 students studying at Erciyes University Institue of Social Sciences. As a result of the analysis and evaluation of the data obtained, it was seen that the brands attracted attention through influencers and their level of recognition increased, but that the participants were cautious in terms of purchasing behavior and that they preferred intimate relationships based on trust in digital environments, too. In addition, brands were advised to supported them with creative steps that provide bidirectional communication in order to ensure the long term and effective relationship established between the influener and consumer.

WITHDRAWN: Experimental investigation of automated system for twitter sentiment analysis to predict the public emotions using machine learning algorithms

Abstract This paper introduces an automated twitter sentiment analysis system for predicting public emotions using machine learning algorithms. Sentiment analysis prediction is an innovative technique to analyze the emotions of the people with their suggestions uploaded in their personal media. One novel idea is a research study of the behavioral attributes of individuals in social networks. Such type of characteristics may be found on other social networking websites viz Twitter, Facebook, Instagram, Snapchat, Flickers etc. So the social networking site is the best way to find actions of people, thought style, mood, selfish networks, and opinions etc. Here, we are dealing with machine learning algorithms to predict users' online social activity which provides an opportunity to actively identify the positive and negative emotion rates. The experimental evaluation shows positive and negative emotion rate for both proposed and existing algorithms DAN, DAN2, SVM and SVM-of. The proposed system gives better results with 95.9% accuracy. The experimental investigation can be found that machine learning algorithms are a good choice for predicting public sentiments using twitter data.

Predictors of marijuana vaping onset and escalation among young adults

BackgroundLittle is known about factors that influence marijuana vaping among young people. We examined cigarette, e-cigarette and marijuana use experiences, social network characteristics and exposure to direct e-cigarette marketing as predictors of marijuana vaping initiation and escalation. MethodsOne-year prospective data were collected between 2017 and 2019 from 2327 young adults (Mean age = 21.2; SD = 2.1; 54 % women) attending 2-year and 4-year colleges in Hawaii. ResultsAmong participants who were never marijuana users at baseline, being a dual user of cigarette and e-cigarette at baseline was the strongest predictor of marijuana vaping initiation, followed by baseline cigarette-only and e-cigarette-only use. Higher prevalence of regular marijuana users in one’s social networks, but not e-cigarette users or cigarette smokers, significantly predicted marijuana vaping initiation a year later. Among baseline current e-cigarette users and lifetime marijuana users, higher presence in social networks of individuals who frequented vape shops at baseline was a significant predictor of increased current marijuana vaping at one-year follow-up. ConclusionsDual use of cigarette and e-cigarette and greater presence in social networks of marijuana users and people who frequent vape shops appear to be robust predictors of marijuana vaping onset and escalation among young adults. In addition to promoting e-cigarette use prevention/cessation, efforts to control marijuana vaping may need to consider promoting smoking prevention/cessation and the effects of increasing prevalence of marijuana use.

Factors Influencing Consumers’ Participation in Social Commerce

ABSTRACT Social networking websites are enjoying widespread usage throughout the world. Its applications are playing a significant role in marketing by allowing users to share information about products and services such as reviews, opinions, and even purchasing behavior with their network of friends and family. These social networks provide new opportunities to communicate with existing customers and attract new customers through the social networks of individuals. Despite its popularity, not all consumers are ready to embrace it. This study aims at examining the factors that influence consumers to engage in social commerce. The research findings contribute to consumer behavior theory by revealing factors that can encourage consumers to participate in social commerce. Results from this study should help online merchants better understand how to effectively reach online consumers through social commerce.