Information and Influence in Social Networks

This study examines the association between various domains of social network support and the long-term marital success of husbands and wives who had been married for an average of 20 years. We hypothesized that three domains of social network support (e.g., support related specifically to the relationship, affective overlap, and general personal support) would predict marital success. Overall, the findings suggest that social networks are influential in long-term marital relationships. For both husbands and wives, relationship-specific support predicted positive change in marital success from 1991 to 1994. Neither affective overlap nor personal support predicted marital success. This study underscores the significance of different domains of social support in long-term romantic relationships, the role of social support in predicting marital success, and the role of marital success in predicting social support. Couples function within social contexts, vulnerable to the influences and pressures of members of their social networks (Boissevain, 1974; Lewis, 1973; Milardo & Lewis, 1985; Ridley & Avery, 1979). Because of their importance for each spouse, these social influences can affect the quality and course of marital relationships. This study examines the connection between the influence of social networks and marital success among people in longterm marriages. Most previous research examines this connection using cross-sectional data or longitudinal data in which time intervals are just months apart; samples of dating partners or samples in which many of the spouses are in their early years of marriage; samples in which only one of the partners, rather than the couple, is in the relationship under study; or samples of college students. In contrast, the study presented here is longitudinal. It covers 3 years, focuses on long-term (1437 years) married couples, gathers data from both spouses rather than just one partner, and uses a sample recruited from the community at large. Before considering the association between marital relations and the broader social context, however, it is worthwhile to review the defining characteristics of social networks used in this report. A social network is a collection of people known by an individual. A psychological network is a specific type of social network composed of people in whom an individual is most likely to confide or place trust, and it typically includes parents, in-laws, other kin, and close friends. These are people who can alter an individual's definition of self by communicating their thoughts and ideas about the individual's actions to him or her. Those thoughts and ideas can influence the initiation, maintenance, and dissolution of romantic relationships (Surra, 1988). Two primary models have been proposed to account for the influence that networks exert on romantic relationships-the support model and the interference model (Milardo & Lewis, 1985). The support model asserts that network members help couples build feelings of satisfaction with their partners by validating their relationships (e.g., commenting about how nice a pair the couple makes), nurturing a sense of coupleness by inviting the pair to social events as a couple (Lewis, 1973), and asserting that the couple can work through their problems by offering the pair aid when problems arise (Julien, Markman, Leveille, Chartrand, & Begin, 1994; Milardo, 1983; Milardo & Lewis, 1985). In contrast, the interference model asserts that network members contribute to the breakdown of dyadic unions by fostering partners' feelings of dissatisfaction with their relationships. These models are hypothesized to function through the mechanisms of uncertainty reduction, social comparison, social sanctions, resocialization, and self-regulation. Although most research considers how these processes work in dating relationships (Bates, 1942; Parks & Adelman, 1983; Surra & Milardo, 1991; Sussman, 1953; Titus, 1980), there is reason to believe that the processes also operate in marriages. …

Social network influence dissemination focuses on employing a small number of seed sets to generate the most significant possible influence in social networks and considers forwarding to be the only technique of information transmission, ignoring all other ways. Users, for example, can post a message via this mode of distribution (called para), which is difficult to trace, posing a danger of privacy leakage. This research tries to address the aforementioned issues by developing a social network information transmission model that supports the paranormal relationship. It suggests a way of disseminating information called Local Greedy, which aids in the protection of user privacy. Its effect helps to reconcile the conflict between privacy protection and information distribution. Aiming at the enumeration problem of seed set selection, an incremental strategy that supports privacy protection is proposed to construct seed sets to reduce time overhead; a local influence subgraph method of computing nodes is given to estimate the influence of seed set propagation quickly; the group satisfies the constraints of privacy protection, and a plan is proposed to deduce the upper limit of the probability of node leakage state, avoiding the time cost of using the Monte Carlo method using the crawled Sina Weibo dataset. Experimental verification and example analysis are carried out, and the results show the effectiveness of the proposed method.

Social Network Influences on Service Use Among Urban, African American Youth with Mental Health Problems

Influence models enable the modelling of the spread of ideas, opinions and behaviours in social networks. Bounded rationality in social networks suggests that players make non-optimum decisions due to the limitations of access to information. Based on the premise that adopting a state or an idea can be regarded as being ‘rational’, we propose an influence model based on the heterogeneous bounded rationality of players in a social network. We employ the quantal response equilibrium model to incorporate the bounded rationality in the context of social influence. We hypothesise that bounded rationality of following a seed or adopting the strategy of a seed is negatively proportional to the distance from that node, and it follows that closeness centrality is the appropriate measure to place influencers in a social network. We argue that this model can be used in scenarios where there are multiple types of influencers and varying pay-offs of adopting a state. We compare different seed placement mechanisms to compare and contrast the optimum method to minimise the existing social influence in a network when there are multiple and conflicting seeds. We ascertain that placing of opposing seeds according to a measure derived from a combination of the betweenness centrality values from the seeds, and the closeness centrality of the network provide the maximum negative influence. Further, we extend this model to a strategic decision-making scenario where each seed operates a strategy in a strategic game.

The development of the post-industrial society requires the acceleration of the integration of the national economy into the globalized economic space. This stage is characterized by active informatization of all spheres of life in society, which requires information security and cyber protection for high-quality information provision of the country’s population, intellectualization of economic processes and prevention of destructive informational influence on the social status of the individual. It is also necessary to consider that for Ukraine the specified stage is complicated by the hybrid war with Russia, which requires strengthening the protection of information from cyber-attacks and the formation of new approaches in preserving the quality of information on social networks. Therefore, the purpose of this article is the development of scientific and methodological approaches in information security management, strengthening its social significance. This requires solving a certain range of tasks of identifying and preventing socially dangerous information based on the use of economic and mathematical methods and models. The article highlights the main directions of socialization of modern technological development and theoretically substantiates its significant impact on human consciousness and behavior, puts a person in front of serious challenges and, under conditions of hybrid warfare, requires strengthening of information security in social networks. In the work, the components of information security are supplemented, its impact on social aspects of society’s life is highlighted. As a result of the research, the authors proposed a scientific and methodical approach to the construction of a system for countering the spread of socially dangerous information in social networks. Besides, its functional elements are highlighted. A method of combating the spread of harmful information in social networks has been developed, which solves the problem of information support of the decision-making process and includes providing the chosen and alternative options to the person making the decision, with the justification of the choice. This creates prerequisites for information support for making a well-founded management decision.

Event-based social networks (EBSNs) provide convenient online platforms for users to organize, attend and share social events. Understanding users’ social influences in social networks can benefit many applications, such as social recommendation and social marketing. In this paper, we focus on the problem of predicting users’ social influences on upcoming events in EBSNs. We formulate this prediction problem as the estimation of unobserved entries of the constructed user-event social influence matrix, where each entry represents the influence value of a user on an event. In particular, we define a user’s social influence on a given event as the proportion of the user’s friends who are influenced by him/her to attend the event. To solve this problem, we present a combined collaborative filtering model, namely, Matrix Factorization with Event Neighborhood (MF-EN) model, by incorporating event-based neighborhood method into matrix factorization. Due to the fact that the constructed social influence matrix is very sparse and the overlap values in the matrix are few, it is challenging to find reliable similar event neighbors using the widely adopted similarity measures (e.g., Pearson correlation and Cosine similarity). To address this challenge, we propose an additional information based neighborhood discovery (AID) method by considering three event-specific features in EBSNs. The parameters of our MF-EN model are determined by minimizing the associated regularized squared error function through stochastic gradient descent. We conduct a comprehensive performance evaluation on real-world datasets collected from DoubanEvent. Experimental results demonstrate the superiority of the proposed model compared to several alternatives.

The purpose of this work is to develop an algorithm and a method for identifying key nodes involved in the dissemination of criminal information within social networks. This study focuses on social network analysis (SNA) metrics that facilitate the detection of influential actors in organized groups, particularly activists who serve as primary disseminators of criminal content. The research objects include both the textual content and metadata of users on social media platforms such as "Vkontakte" and "YouTube." To achieve this goal, an algorithm for detecting nodes that distribute criminal information has been developed. A conceptual model has been constructed, integrating network analysis principles with computational techniques to assess influence. This model introduces a novel framework for evaluating social network nodes based on a combination of structural, semantic, and emotional factors. Specifically, it incorporates influence assessment metrics that consider the heterogeneous nature of content, including its linguistic features, sentiment, and patterns of engagement. Additionally, the model accounts for the emission dynamics of criminal content, allowing for a more precise determination of high-risk nodes within the network. A method for quantifying the influence of social network nodes engaged in criminal content dissemination has been formulated. This method utilizes centrality measures along with content analysis techniques to improve accuracy in detecting key actors. Experimental validation conducted on multiple real-world datasets (including VKontakte groups and known extremist networks) demonstrated that the proposed method achieves an accuracy of up to 80% in identifying the most influential criminal nodes. Compared to baseline centrality-based methods, our approach provides more reliable detection due to the integration of semantic-emotional metrics and emission indicators. The results confirm the practical value of the method in operational scenarios such as the early detection of criminal activity and the prioritization of threat actors for monitoring. These findings have strong implications for real-world applications in law enforcement and cybersecurity. By leveraging advanced algorithmic techniques for social network monitoring, authorities can proactively detect and mitigate the spread of criminal information.

Appalachia, a region that spans 420 counties in 13 states from New York to Mississippi, has a greater proportion of residents with low socioeconomic status (SES), including lower household incomes, higher unemployment, and deficits in education compared to the United States (U.S.) as a whole. These factors contribute to disparities such as access to quality healthcare and an increased cancer burden. Cervical cancer incidence and mortality rates in Appalachia are significantly higher than among other women in the U.S. In order to understand and intervene on cancer health disparities, the Centers for Population Health and Health Disparities (CPHHD) initiative developed and tested a multi-level socio-ecological framework (Warnecke model) which included social, behavioral, environmental and biological variables as contributing and interacting factors. The Ohio State University Comprehensive Cancer Center's CPHHD (P50CA015632) used this framework to better understand why cervical cancer disparities exist and to attempt to reduce the cervical cancer burden within Appalachian Ohio. Four studies, that spanned the Warnecke model levels of influence, were conducted to address important factors causing cervical cancer: 1) the genetic contributions to invasive cervical cancer; 2) the influence of social networks on smoking behaviors; 3) the biological effects of stress on immunity to the HPV vaccine; 4) and the effects of a multi-level intervention on uptake of the HPV vaccine among adolescent girls. The results of each study were examined on a multi-level basis and then the significant factors from each study were included in an overall multi-level model to explain the disparity of cervical cancer in Appalachian Ohio. In all, a total of 1340 participants were included in these studies. Results from the multi-level models from each study revealed that 1) the interaction of individual risk factors and genetic mechanisms is related to cervical cancer in non-smokers; 2) in terms of tobacco use, individual factors such as age and depression as well as an individual's social network and social influence and social cohesion are relevant factors; 3) poverty, discrimination, social cohesion, neighborhood disadvantage, social support, age, SES, and depression were associated with the immune response to the HPV vaccine; and 4) the interplay of institutional structures such as healthcare systems and families, social influences, as well as SES were associated with HPV vaccine uptake among adolescent girls. Taken together, these results suggest that upstream (poverty, discrimination, health care systems, families, social cohesion, social networks, social support, social influences, and neighborhood factors) as well as downstream factors (tobacco use, age, SES, education, depression, genetics and immune function) together contribute to cervical cancer disparities in Appalachian Ohio. Interventions must now be developed and implemented across these multiple levels to reduce disparities. Citation Format: Electra D. Paskett, Ryan Baltic, Cecilia R. DeGraffinreid, Mary Ellen Wewers, Mack T. Ruffin, IV, Christopher M. Weghorst, Thomas J. Knobloch, Mira L. Katz, Cathy M. Tatum, Michael L. Pennell, Bo Lu, Erinn M. Hade, Amy K. Ferketich. Multi-level research designs for understanding the determinants of cervical cancer disparities. [abstract]. In: Proceedings of the Ninth AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2016 Sep 25-28; Fort Lauderdale, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2017;26(2 Suppl):Abstract nr PR03.

The target set selection (TSS) problem was initially proposed to study the spread of information, ideas, or influence in social networks. It has been used to model many problems arising in various practical applications. In this paper, we study the TSS problem for plane graphs. We provide a characterization of plane graphs $ G $ for which $ \min_3(G) = 3 $. Consequently, we show that the minimum degree of such plane graphs is at most 4.

The analysis of social networks, which focuses on the relationship between social entities today is an area of active research. It is a set of tools for research, in particular, in combination with artificial intelligence methods such as machine learning, deep learning. The paper examined the current quality of the assessment of information in social networks, analyzed the methods of searching and sorting information in various social networks, as well as the process of providing recommendations to users. Social media data is an inexhaustible source of research and business opportunities. In general, social media data is information gathered from social networks that shows how users interact with content. Methods of improving search results for personalizing recommendations in social networks are given. These indicators and statistics provide an effective understanding of the strategy of behavior in social networks. The advantages and disadvantages of a multifactor assessment system are considered. The possible ways of integrating the combined system of evaluating information elements by the user to optimize search queries and filtering big data are identified.

The relationships and interactions between social actors shape the structure and function of social systems. Informal social networks—homogeneous spaces based on shared social and cultural characteristic —play a crucial role in the emergence and persistence of irregular migration, particularly among Georgian migrants in Italy. This study employs a qualitative case study approach focusing on the years 2017–2020. Data were collected through semi-structured interviews with irregular Georgian migrants residing in Italy. Content analysis was applied within the theoretical framework of network theory and symbolic interactionism. The research demonstrates that the idea and practices of irregular migration are primarily generated and transmitted within informal social networks composed of individuals connected by similarities in age, class, and social background. These networks are characterized by tight bonds, mutual trust, and strong norms of reciprocity—often encapsulated in the Georgian idiom, “a hand washes a hand.” The data also support Mark Granovetter’s theory on the strength of weak ties, as the diffusion of diverse and updated information is enabled by more expansive and loosely connected networks (Granovetter, 1973). Such networks serve as vital mechanisms for adaptation and social integration within host countries. In addition, formal institutional supports—such as Georgian Orthodox churches, clergy, and diaspora associations—were found to supplement informal networks in facilitating irregular migration and social support. Informal social networks not only facilitate the decision-making and implementation processes of irregular migration but also create a parallel system of migration management. Their legitimacy and capacity for resource exchange make them a foundational component in sustaining irregular migration as an institutionalized practice.

As agents interact and influence one another in a social network, the opinions they hold about some common topic can change over time. These changes may enable us to infer mechanisms of the network that control how interactions lead to opinion change. Inferring such mechanisms from opinion data could enable analysis of social influence in data-sparse scenarios. However, limited work has focused on this problem, despite its clear value. To address this gap, we create opinion data using agent-based simulation and experimental design. By viewing opinion changes as an information-generating process, opinion dynamics can be studied using entropy. This work explores the relationships between aggregated entropy and five simulation design factors. Three entropy measures are calculated on continuous-valued opinions and are analyzed using a main effects model and cluster analysis. Overall, the choices of influence model and error distribution are most important to the entropy measures, activation regime is important to some measures, and population size is unimportant. Also, design variation can be detected using time-series cluster analysis. These findings may support work in inferring properties about real-world social influence networks using opinion data collected from their members.

Martin Semple and colleagues discuss an experiment to investigate the influence of previously charted blood pressure recordings on the recording of blood pressure by student nurses. The results suggest that previously charted recordings exert a social influence that alters the judgement of student nurses when recording blood pressure

Over the years, online social networks (OSNs) have become popular form of networks for people to create and maintain networks of friends, to share knowledge/interests, and to publish content on a massive scale. The popularity of such OSNs has formed a virtual platform for viral marketing of contents, products, target advertising, and political campaigns. The amount of work dedicated to studying information diffusion in OSNs has increased in recent years. Past research has clearly shown that diffusion of information in social networks depends on both the nature of the information and the structure of the network. In this study, we focus on one of the structural properties, tie strength, in the flow of information through social networks. Several studies have shown that weak ties can act as bridges, linking disconnected groups in the network, which, in turn, allows information to diffuse between subgroups. However, not all weak ties are good weak ties in the network. In this study, we want to prove that a weak tie helps in the spread of information, when a good weak tie is selected. Specifically, we proposed a simple mechanism to select good weak ties in each step of the propagation process. We simulate our mechanism on data collected from two blog networks (wretch.cc and tribe.com) and a Facebook network, and found that an appropriate weak tie can improve the diffusion of information.

Nowadays, instant messaging (IM) software is one of the most popular communication tools. Understanding the complex relationships between individual characteristics and social factors is crucial for enhancing the stickiness of IM groups. This study explores the influence of congenital and acquired factors on the stickiness of IM group users. In fact, this research studied the influence of five-factor model, social network, and social influence on stickiness of IM group users. Structural equation modeling was employed to examine the research hypotheses. The research findings revealed that four key personality traits (such as Extraversion, Neuroticism, Agreeableness, and Conscientiousness) may influence the participation in the IM groups. In terms of social networks, emotional support networks have created an influence on the participation in instant messaging groups. As for the social influence, subjective norms and critical mass may affect the participation in IM groups. The obtained results may provide practical insights for the design and improvement of instant messaging software/applications. Moreover, the government can use the relevant findings to formulate policies and develop feasible administrative objectives.