Social Influence Model Research Articles

Influence of social network on method musical composition

The method of musical composition (MMC) is a metaheuristic based on sociocultural creativity systems. Within the MMC, models of social influence and social learning are used and integrated in a social network, which is composed of a set of individuals with links between them and involves a set of interaction rules. In this paper, a comparative study on the performance of the MMC with different network structures is proposed. Sixteen benchmark nonlinear optimization problems are solved, taking into account nine social topologies, which are: (a) linear, (b) tree, (c) star, (d) ring, (e) platoons, (f) von Neumann, (g) full connection, (h) random and (i) small world. In addition, the update of each topology structure was tested according to four different strategies: one static, two dynamic and one self-adaptive states. An exhaustive statistical analysis of the obtained numerical results indicates that the social dynamics has no significant impact on the MMC's behavior. However, the topology structures can be classified into groups that consistently influence the performance level of the MMC. More precisely, a structure characterized by a low value of its mean number of neighbors and a rather fast information transfer process (star topology) performs in a radically opposite way as structures where each agent has many neighbors (random and complete topologies). These observations allow to provide some guidelines for the selection of a network topology used within a social algorithm.

Social Influences in Sequential Decision Making.

People often make decisions in a social environment. The present work examines social influence on people’s decisions in a sequential decision-making situation. In the first experimental study, we implemented an information cascade paradigm, illustrating that people infer information from decisions of others and use this information to make their own decisions. We followed a cognitive modeling approach to elicit the weight people give to social as compared to private individual information. The proposed social influence model shows that participants overweight their own private information relative to social information, contrary to the normative Bayesian account. In our second study, we embedded the abstract decision problem of Study 1 in a medical decision-making problem. We examined whether in a medical situation people also take others’ authority into account in addition to the information that their decisions convey. The social influence model illustrates that people weight social information differentially according to the authority of other decision makers. The influence of authority was strongest when an authority's decision contrasted with private information. Both studies illustrate how the social environment provides sources of information that people integrate differently for their decisions.

Timing Matters: How Social Influence Affects Adoption Pre- and Post-Product Release

Social influence is typically studied after a product is released. Yet, audience expectations and discussions begin before a product’s release. This observation suggests a need to understand adoption processes over a product’s life cycle. To explore pre- and postrelease social influence processes, this article uses survey data from Americans exposed to word of mouth for 309 Hollywood movies released over two and a half years. The data suggest pre- and postrelease social influences operate differently. Prerelease social influence displays a critical transition point with relation to adoption: before a critical value, any level of social influence is negligibly related to adoption, but after the critical value, the relationship between social influence and adoption is large and substantive. In contrast, postrelease social influence exhibits a positive linear relationship with adoption. Prerelease social influence is argued to require more exposures than postrelease social influence because of differences in the diagnosticity and accessibility of the information. To complement the survey data, computational models are used to test alternative hypotheses. Evidence from the computational models supports the proposed model of social influence.

Group Ethical Decision Making: A Social Influence Approach

Despite the theoretical and practical value of studying ethical decision making, we know little about group ethical decision making. The existing research pays little attention to inputs and group processes of group ethical decision making. In this paper I, a) propose a social influence model that highlights the influence nature of group ethical decision making, b) identify a subset of group members that is likely to advocate ethical solutions in groups and three types of influence (i.e., normative, informational, and emotional influence) that the subset can have on group members, c) discuss characteristics of group members and groups affecting the three influence processes, and d) outline situational factors that influence group ethical decision making. I provide research propositions and discuss theoretical contributions and practical implications.

Социальные сети и рождаемость

Despite the predominant view of fertility as a rationally planned and independent behavior, more and more papers appear that emphasize the need to include measures of social networks and social interactions in fertility research. In this review we discuss several arguments to consider fertility as a socially embedded process. The notion of social embeddedness expresses the shift from the macro and micro levels of analysis to the meso level, disclosing the influence of weak ties (friends, peers and co-workers) and network mechanisms on bearing children and becoming parents. The integration of social influence models into the research on fertility behavior enables students to explain the gap between intentions to have a child and actually becoming a parent in terms of interaction between weak and strong ties. The impact of network mechanisms is tested for different number of births, timing of marriage, and transition to parenthood. Several types of network mechanisms are distinguished: social learning, social contagion, social pressure and social support. Social learning concerns circulation of relevant information and experience exchange with friends, peers and co-workers. Social support involves mobilization of strong and weak ties for material and emotional maintenance in pregnancy, childbearing, childcare, and decision making on new births. Social learning and social support play a decisive role in reducing uncertainty and costs related to childbearing and childcare, especially for countries will low levels of economic sustainability and generalized trust. Social contagion and social pressure are mechanisms that express vertical and horizontal processes of social diffusion related to the spread of social norms and settings on marriage and parenthood. A network approach provides new insight on the role of education, age and religiosity in fertility decisions.

Dynamic Socialized Gaussian Process Models for Human Behavior Prediction in a Health Social Network.

Modeling and predicting human behaviors, such as the level and intensity of physical activity, is a key to preventing the cascade of obesity and helping spread healthy behaviors in a social network. In our conference paper, we have developed a social influence model, named Socialized Gaussian Process (SGP), for socialized human behavior modeling. Instead of explicitly modeling social influence as individuals' behaviors influenced by their friends' previous behaviors, SGP models the dynamic social correlation as the result of social influence. The SGP model naturally incorporates personal behavior factor and social correlation factor (i.e., the homophily principle: Friends tend to perform similar behaviors) into a unified model. And it models the social influence factor (i.e., an individual's behavior can be affected by his/her friends) implicitly in dynamic social correlation schemes. The detailed experimental evaluation has shown the SGP model achieves better prediction accuracy compared with most of baseline methods. However, a Socialized Random Forest model may perform better at the beginning compared with the SGP model. One of the main reasons is the dynamic social correlation function is purely based on the users' sequential behaviors without considering other physical activity-related features. To address this issue, we further propose a novel "multi-feature SGP model" (mfSGP) which improves the SGP model by using multiple physical activity-related features in the dynamic social correlation learning. Extensive experimental results illustrate that the mfSGP model clearly outperforms all other models in terms of prediction accuracy and running time.

The unified model of social influence and its application in influence maximization

The study of information dissemination on a social network has gained significant importance with the rise of social media. Since the true dynamics are hidden, various diffusion models have been exposed to explain the cascading behavior. Such models require extensive simulation for estimating the dissemination over time. In an earlier work, we proposed a unified model which provides an approximate analytical solution to the problem of predicting probability of infection of every node in the network over time. Our model generalizes a large class of diffusion process. We demonstrate through extensive empirical evaluation that the error of approximation is small. We build upon our unified model to develop an efficient method for influence maximization. Unlike most approaches, we assume that diffusion spreads not only via the edges of the underlying network, but also through temporal functions of external out-of-network processes. We empirically evaluate our approach and compare it against state-of-the-art approaches on real-world large-scale networks. The evaluation demonstrates that our method has significant performance gains over widely used seed-set selection algorithms.

Social sciences and medical humanities: the new focus of psychiatry

The clinical practice of psychiatry should incorporate a biopsychosocial model of illness, acknowledging both cultural and social influences on the patient's experience. Medical humanities include a number of academic disciplines that complement the clinical practice of psychiatry. The medical profession, including psychiatry, has a social responsibility to study the psychosocial context within which people become ill and have to be treated. Although the biopsychosocial model of illness has strong theoretical foundations, its application in clinical practice is limited. A new approach would be to restructure medical student teaching to include medical humanities in the first year, and to share such education with other professions.

Stalking Victimization, Labeling, and Reporting: Findings From the NCVS Stalking Victimization Supplement.

Using the National Crime Victimization Survey 2006 Stalking Victimization Supplement (NCVS-SVS) and guided by Greenberg and Ruback's social influence model, this study examines the effects of individual (e.g., severity, sex, victim-offender relationship) and contextual (e.g., location) factors on stalking victimization risk, victim labeling and help seeking, and victim and third-party police contacts. Logistic regression results suggest individual and contextual characteristics matter. Consistent with prior research and the theoretical model, the positive effects of severity and sex (female) were significant across all dependent variables, whereas the interaction effect of victim-offender relationship and location held only for third-party police contacts.

Computational Models of Social Influence and Collective Behavior

This chapter provides an overview of computational modeling of conformity, group deliberation, and social diffusion processes, with an emphasis on social psychological perspectives rather than the kinds of structural and institutional issues modeled in political science and sociology. The chapter attempts to illustrate the benefits of building maps of "parameter space" as a way to generalize across single studies and to develop out-of-sample new predictions.

Multiple substance use patterns in adolescents—A multilevel latent class analysis

BackgroundMultiple substance use among adolescents is associated with a number of negative consequences. Therefore, we aim to investigate multiple substance use patterns among young adolescents and identify possible multilevel predictors. MethodsWe analyzed a longitudinal sample of 2490 German students (51% male; Mage=13.32, SD=0.57) at 45 schools in four German states (Bremen, Hesse, North Rhine–Westphalia, Schleswig–Holstein), who completed two assessments in fall, 2010 and fall, 2013. We conducted multilevel latent class analysis for follow-up data on a variety of outcomes, and tested our final 3-class-model for possible baseline predictors. Follow-up substance use measures included lifetime use, current use, and amount of substance for cigarettes and alcohol. Baseline covariates comprised age, gender, socio-economic status, bullying, victimization, peer and parental use, type of school, and health certification of school. ResultsWe identified three latent classes: non-users (n=1541; 61.9%), experimenters (n=722; 29.0%), and multiusers (n=227; 9.1%). Experimental consumption was predicted by higher baseline age (OR=1.71; 1.31–2.24), paternal drinking (OR=2.89; 1.23–6.79), and school type (OR=2.57; 1.83–3.61), while multiuse was predicted by peer smoking (OR=2.94; 1.80–4.80) and drinking (OR=2.13; 1.32–3.44), maternal drinking (OR=6.26; 2.02–19.43), bullying (OR=1.69; 1.15–2.48), higher age (OR=1.92; 1.40–2.62), and school type (OR=4.76; 2.75–8.24) compared to the non-users class. ConclusionsPrevention and further research on multiple substance use need to concentrate on social influence models and behavior-related interventions, especially at schools without a college-preparatory track.

Animal models of social contact and drug self-administration

Social learning theories of drug abuse propose that individuals imitate drug use behaviors modeled by social peers, and that these behaviors are selectively reinforced and/or punished depending on group norms. Historically, animal models of social influence have focused on distal factors (i.e., those factors outside the drug-taking context) in drug self-administration studies. Recently, several investigators have developed novel models, or significantly modified existing models, to examine the role of proximal factors (i.e., those factors that are immediately present at the time of drug taking) on measures of drug self-administration. Studies using these newer models have revealed several important conclusions regarding the effects of social learning on drug abuse: 1) the presence of a social partner influences drug self-administration, 2) the behavior of a social partner determines whether social contact will increase or decrease drug intake, and 3) social partners can model and imitate specific patterns of drug self-administration. These findings are congruent with those obtained in the human laboratory, providing support for the cross-species generality and validity of these preclinical models. This mini-review describes in detail some of the preclinical animal models used to study social contact and drug self-administration to guide future research on social learning and drug abuse.

A Novel Simulation Method for Binary Discrete Exponential Families, With Application to Social Networks

Stochastic models for finite binary vectors are widely used in sociology, with examples ranging from social influence models on dichotomous behaviors or attitudes to models for random graphs. Exact sampling for such models is difficult in the presence of dependence, leading to the use of Markov chain Monte Carlo (MCMC) as an approximation technique. While often effective, MCMC methods have variable execution time, and the quality of the resulting draws can be difficult to assess. Here, we present a novel alternative method for approximate sampling from binary discrete exponential families having fixed execution time and well-defined quality guarantees. We demonstrate the use of this sampling procedure in the context of random graph generation, with an application to the simulation of a large-scale social network using both geographical covariates and dyadic dependence mechanisms.

Understanding the Low Level of Cervical Cancer Screening in Masaka Uganda Using the ASE Model: A Community-Based Survey.

Cervical cancer is one of the leading causes of cancer deaths among women globally and its impact is mostly felt in developing countries like Uganda where its prevalence is higher and utilization of cancer screening services is low. This study aimed to identify factors associated with intention to screen for cervical cancer among women of reproductive age in Masaka Uganda using the attitude, social influence and self efficacy (ASE) model. A descriptive community based survey was conducted among 416 women. A semi-structured interviewer administered questionnaire was used to collect data. Unadjusted and adjusted prevalence ratios (PR) were computed using a generalized linear model with Poisson family and a log link using STATA 12. Only 7% (29/416) of our study respondents had ever screened for cervical cancer although a higher proportion (63%, 262/416) reported intention to screen for cervical cancer. The intention to screen for cervical cancer was higher among those who said they were at risk of developing cervical cancer (Adjusted prevalence ratio [PR] 2.0, 95% CI 1.60–2.58), those who said they would refer other women for screening (Adjusted PR 1.4, 95% CI 1.06–1.88) and higher among those who were unafraid of being diagnosed with cervical cancer (Adjusted PR 1.6, 95% CI 1.36–1.93). Those who reported discussions on cervical cancer with health care providers (Adjusted PR 1.2, 95% CI 1.05–1.44), those living with a sexual partner (Adjusted PR 1.4, 95% CI 1.11–1.68), and those who were formally employed (Adjusted PR 1.2, 95% CI 1.03–1.35) more frequently reported intention to screen for cervical cancer. In conclusion, health education to increase risk perception, improve women's attitudes towards screening for cervical cancer and address the fears held by the women would increase intention to screen for cervical cancer. Interventions should also target increased discussions with health workers.

Generalized behavioral framework for choice models of social influence: Behavioral and data concerns in travel behavior

Over the past two decades, transportation has begun a shift from an individual focus to a social focus. Accordingly, discrete choice models have begun to integrate social context into its framework. Social influence, the process of having one’s behavior be affected by others, has been one approach to this integration. This paper provides a review and discussion of the incorporation of social influence into discrete choice models with specific application in travel behavior analysis. The discussion begins with a generalized framework to describe choice models of social influence. This framework focuses on the behavioral microfoundations of social influence and choice by separating the social influence mechanism from the source of its influence and by explicitly acknowledging the role of the social network in the model structure. This contrasts with prior work that focused on the measurement of contextual, endogenous, and correlated effects. Then, the state of the art in travel behavior research is reviewed using a taxonomy based on the generalized framework with research performed in sociology, social psychology, and social network analysis. The discussion then shifts to the importance of understanding the motivations for social influence, and the formation and structure of social networks are explored. Additionally, the challenges of collecting data for social influence studies are mentioned and the paper concludes with a look at the challenges in the field and areas for future research.

Understanding Motivation and Social Influence in Stakeholder Prioritization

Insight into organizational responses to stakeholder claims and influence attempts is critical to understand the challenges currently facing managers and organizations. Drawing on Kelman’s (1958) model of social influence, we advance the field’s understanding of the factors driving firm-level prioritization of competing stakeholder claims by developing a theoretical framework that accounts for both the stakeholder attributes that are important to relevant decision makers, and the decision makers’ motivations for accepting or rejecting the influence attempts of varying stakeholders. Our framework distinguishes itself from existing research by focusing on stakeholder prioritization, not salience, recognizing that stakeholder-related decisions result from group interaction and that important decision makers are not limited to those found within the classic boundaries of the firm. Consequently, we argue that decision makers are simultaneously stakeholders with attributes that might be relevant to other decision makers involved in prioritization. In addition, we identify a more extensive set of stakeholder attributes that includes powerlessness and illegitimacy.

Field evidence of social influence in the expression of political preferences: the case of secessionists flags in Barcelona.

Models of social influence have explored the dynamics of social contagion, imitation, and diffusion of different types of traits, opinions, and conducts. However, few behavioral data indicating social influence dynamics have been obtained from direct observation in “natural” social contexts. The present research provides that kind of evidence in the case of the public expression of political preferences in the city of Barcelona, where thousands of citizens supporting the secession of Catalonia from Spain have placed a Catalan flag in their balconies and windows. Here we present two different studies. 1) During July 2013 we registered the number of flags in 26% of the electoral districts in the city of Barcelona. We find that there is a large dispersion in the density of flags in districts with similar density of pro-independence voters. However, by comparing the moving average to the global mean we find that the density of flags tends to be fostered in electoral districts where there is a clear majority of pro-independence vote, while it is inhibited in the opposite cases. We also show that the distribution of flags in the observed districts deviates significantly from that of an equivalent random distribution. 2) During 17 days around Catalonia’s 2013 national holiday we observed the position at balcony resolution of the flags displayed in the facades of a sub-sample of 82 blocks. We compare the ‘clustering index’ of flags on the facades observed each day to thousands of equivalent random distributions. Again we provide evidence that successive hangings of flags are not independent events but that a local influence mechanism is favoring their clustering. We also find that except for the national holiday day the density of flags tends to be fostered in facades located in electoral districts where there is a clear majority of pro-independence vote.

Effects of attitude, social influence, and self-efficacy model factors on regular mammography performance in life-transition aged women in Korea.

This study analyzed predictors of regular mammography performance in Korea. In addition, we determined factors affecting regular mammography performance in life-transition aged women by applying an attitude, social influence, and self-efficacy (ASE) model. Data were collected from women aged over 40 years residing in province J in Korea. The 178 enrolled subjects provided informed voluntary consent prior to completing a structural questionnaire. The overall regular mammography performance rate of the subjects was 41.6%. Older age, city residency, high income and part-time job were associated with a high regular mammography performance. Among women who had undergone more breast self-examinations (BSE) or more doctors' physical examinations (PE), there were higher regular mammography performance rates. All three ASE model factors were significantly associated with regular mammography performance. Women with a high level of positive ASE values had a significantly high regular mammography performance rate. Within the ASE model, self-efficacy and social influence were particularly important. Logistic regression analysis explained 34.7% of regular mammography performance and PE experience (β=4.645, p=.003), part- time job (β=4.010, p=.050), self-efficacy (β=1.820, p=.026) and social influence (β=1.509, p=.038) were significant factors. Promotional strategies that could improve self-efficacy, reinforce social influence and reduce geographical, time and financial barriers are needed to increase the regular mammography performance rate in life-transition aged.

What Drives Facebook Fans to “Like” Hotel Pages: A Comparison of Three Competing Models

Social media marketing is gaining importance in the hotel industry. The current study developed and tested three competing antecedent models of social media marketing derived from technology, communication, and social psychology theories. The three models were evaluated using structural equation modeling and compared for overall model fit, path significance, explanatory power, and parsimony. The results revealed that the social psychology model, based on social identity theory and social influence model, provided the best explanation of Facebook fans’ intentions to “like” (join) hotel pages. Three different social influence factors, namely, compliance, internalization, and identification, had different effects on fans’ attitudes toward hotel Facebook pages and behavioral intentions to like hotel Facebook pages. These findings have theoretical implications by demonstrating that classic theories of social psychology are applicable in the social media environment. The findings also have practical implications by providing guidance for effective social media marketing in the hotel industry.

Friendship influence on mobile behavior of location based social network users

In mobile computing research area, it is highly desirable to understand the characteristics of user movement so that the user friendly location aware services could be rendered effectively. Location based social networks (LBSNs) have flourished recently and are of great potential for movement behavior exploration and data-driven application design. While there have been some efforts on user check-in movement behavior in LBSNs, they lack comprehensive analysis of social influence on them. To this end, the social-spatial influence and social-temporal influence are analyzed synthetically in this paper based on the related information exposed in LBSNs. The check-in movement behaviors of users are found to be affected by their social friendships both from spatial and temporal dimensions. Furthermore, a probabilistic model of user mobile behavior is proposed, incorporating the comprehensive social influence model with extent personal preference model. The experimental results validate that our proposed model can improve prediction accuracy compared to the state-of-the-art social historical model considering temporal information (SHM+T), which mainly studies the temporal cyclic patterns and uses them to model user mobility, while being with affordable complexity.