Persuasion process on social networks

2021 ◽  
Author(s):  
Haibo Hu ◽  
Nannan Xu
Keyword(s):  
Social Networks ◽  
Mass Media ◽  
Social Interactions ◽  
Relative Proportion ◽  
Opinion Dynamics ◽  
Theoretical Models ◽  
Vital Role ◽  
Opinion Formation ◽  
Media Effect ◽  
The Arab Spring

From the Rwandan genocide to the Arab Spring movement, it has been well known that social networks, offline or online, and mass media can collectively change and amplify public opinions, however there are few theoretical models to characterize the persuasion process. In this paper, we propose an opinion dynamics model based on invasion process with media effect and committed agents, and analytically obtain the fraction of each opinion at the steady state. We find that the relative proportion of committed agents plays a vital role in influencing corresponding opinion formation, and social networks can enhance the influence of committed agents through the interaction between individuals. Mass media can affect individuals not only directly due to exposure but indirectly due to social interactions. This paper reveals the influence of mass media and committed agents on the final distribution of opinions through a persuasion process, and lays the foundation for building more general models that consider individual heterogeneity and external influences.

Opinion Dynamics Optimization by Varying Susceptibility to Persuasion via Non-Convex Local Search

2021 ◽  
Vol 16 (2) ◽  
pp. 1-34
Author(s):  
Rediet Abebe ◽  
T.-H. HUBERT Chan ◽  
Jon Kleinberg ◽  
Zhibin Liang ◽  
David Parkes ◽  
...  
Keyword(s):  
Local Search ◽  
Objective Function ◽  
Iterative Process ◽  
Large Scale ◽  
Opinion Dynamics ◽  
Theoretical Models ◽  
Global Optimum ◽  
Local Optimum ◽  
Opinion Formation ◽  
Long Line

A long line of work in social psychology has studied variations in people’s susceptibility to persuasion—the extent to which they are willing to modify their opinions on a topic. This body of literature suggests an interesting perspective on theoretical models of opinion formation by interacting parties in a network: in addition to considering interventions that directly modify people’s intrinsic opinions, it is also natural to consider interventions that modify people’s susceptibility to persuasion. In this work, motivated by this fact, we propose an influence optimization problem. Specifically, we adopt a popular model for social opinion dynamics, where each agent has some fixed innate opinion, and a resistance that measures the importance it places on its innate opinion; agents influence one another’s opinions through an iterative process. Under certain conditions, this iterative process converges to some equilibrium opinion vector. For the unbudgeted variant of the problem, the goal is to modify the resistance of any number of agents (within some given range) such that the sum of the equilibrium opinions is minimized; for the budgeted variant, in addition the algorithm is given upfront a restriction on the number of agents whose resistance may be modified. We prove that the objective function is in general non-convex. Hence, formulating the problem as a convex program as in an early version of this work (Abebe et al., KDD’18) might have potential correctness issues. We instead analyze the structure of the objective function, and show that any local optimum is also a global optimum, which is somehow surprising as the objective function might not be convex. Furthermore, we combine the iterative process and the local search paradigm to design very efficient algorithms that can solve the unbudgeted variant of the problem optimally on large-scale graphs containing millions of nodes. Finally, we propose and evaluate experimentally a family of heuristics for the budgeted variant of the problem.

PHYSICS OF THE MIND: OPINION DYNAMICS AND DECISION MAKING PROCESSES BASED ON A BINARY NETWORK MODEL

2008 ◽  
Vol 22 (25n26) ◽  
pp. 4482-4494 ◽  
Author(s):  
F. V. KUSMARTSEV ◽  
KARL E. KÜRTEN
Keyword(s):  
Decision Making ◽  
Social Networks ◽  
Chaotic Behavior ◽  
Opinion Dynamics ◽  
Human Mind ◽  
Critical Parameters ◽  
Opinion Formation ◽  
Natural Catastrophes ◽  
Risk Condition ◽  
Binary Network

We propose a new theory of the human mind. The formation of human mind is considered as a collective process of the mutual interaction of people via exchange of opinions and formation of collective decisions. We investigate the associated dynamical processes of the decision making when people are put in different conditions including risk situations in natural catastrophes when the decision must be made very fast or at national elections. We also investigate conditions at which the fast formation of opinion is arising as a result of open discussions or public vote. Under a risk condition the system is very close to chaos and therefore the opinion formation is related to the order disorder transition. We study dramatic changes which may happen with societies which in physical terms may be considered as phase transitions from ordered to chaotic behavior. Our results are applicable to changes which are arising in various social networks as well as in opinion formation arising as a result of open discussions. One focus of this study is the determination of critical parameters, which influence a formation of stable mind, public opinion and where the society is placed “at the edge of chaos”. We show that social networks have both, the necessary stability and the potential for evolutionary improvements or self-destruction. We also show that the time needed for a discussion to take a proper decision depends crucially on the nature of the interactions between the entities as well as on the topology of the social networks.

scholarly journals The impact of noise and topology on opinion dynamics in social networks

2021 ◽  
Vol 8 (4) ◽  
Author(s):  
Samuel Stern ◽  
Giacomo Livan
Keyword(s):  
Social Networks ◽  
Social Interactions ◽  
Peer Influence ◽  
Linear Models ◽  
Explanatory Power ◽  
Opinion Dynamics ◽  
And Topology ◽  
Stochastic Setting ◽  
The Impact ◽  
Do So

We investigate the impact of noise and topology on opinion diversity in social networks. We do so by extending well-established models of opinion dynamics to a stochastic setting where agents are subject both to assimilative forces by their local social interactions, as well as to idiosyncratic factors preventing their population from reaching consensus. We model the latter to account for both scenarios where noise is entirely exogenous to peer influence and cases where it is instead endogenous, arising from the agents’ desire to maintain some uniqueness in their opinions. We derive a general analytical expression for opinion diversity, which holds for any network and depends on the network’s topology through its spectral properties alone. Using this expression, we find that opinion diversity decreases as communities and clusters are broken down. We test our predictions against data describing empirical influence networks between major news outlets and find that incorporating our measure in linear models for the sentiment expressed by such sources on a variety of topics yields a notable improvement in terms of explanatory power.

scholarly journals Group size and modularity interact to shape the spread of infection and information through animal societies

2021 ◽  
Author(s):  
Julian C Evans ◽  
David J Hodgson ◽  
Neeltje J Boogert ◽  
Matthew J Silk
Keyword(s):  
Social Networks ◽  
Social Interactions ◽  
Simulation Models ◽  
Theoretical Models ◽  
Social Structures ◽  
Trade Off ◽  
The Social ◽  
The Difference ◽  
Spread Of Infection ◽  
Animal Societies

Social interactions between animals can provide many benefits, including the ability to gain useful environmental information through social learning. However, these social contacts can also facilitate the transmission of infectious diseases through a population. Animals engaging in social interactions must therefore face a trade-off between the potential informational benefits and the risk of acquiring disease. In order to understand how this trade-off can influence animal sociality, it is necessary to quantify the effects of different social structures on individuals' likelihood of acquiring information versus infection Theoretical models have suggested that modular social networks, associated with the formation of groups or sub-groups, can slow spread of infection by trapping it within particular groups. However these social structures will not necessarily impact the spread of information in the same way if its transmission is considered as a "complex contagion", e.g. through individuals copying the majority (conformist learning). Here we use simulation models to demonstrate that modular networks can promote the spread of information relative to the spread of infection, but only when the network is fragmented and group sizes are small. We show that the difference in transmission between information and disease is maximised for more well-connected social networks when the likelihood of transmission is intermediate. Our results have important implications for understanding the selective pressures operating on the social structure of animal societies, revealing that highly fragmented networks such as those formed in fission-fusion social groups and multilevel societies can be effective in modulating the infection-information trade-off for individuals within them.

Hostility and Perceptions of Support in Ambiguous Social Interactions

2006 ◽  
Vol 27 (2) ◽  
pp. 108-115 ◽  
Author(s):  
Ana-Maria Vranceanu ◽  
Linda C. Gallo ◽  
Laura M. Bogart
Keyword(s):  
Social Support ◽  
Social Networks ◽  
Information Processing ◽  
Social Interactions ◽  
Social Information Processing ◽  
Perceived Social Support ◽  
Social Information ◽  
Inverse Association ◽  
Processing Bias ◽  
Information Processing Bias

The present study investigated whether a social information processing bias contributes to the inverse association between trait hostility and perceived social support. A sample of 104 undergraduates (50 men) completed a measure of hostility and rated videotaped interactions in which a speaker disclosed a problem while a listener reacted ambiguously. Results showed that hostile persons rated listeners as less friendly and socially supportive across six conversations, although the nature of the hostility effect varied by sex, target rated, and manner in which support was assessed. Hostility and target interactively impacted ratings of support and affiliation only for men. At least in part, a social information processing bias could contribute to hostile persons' perceptions of their social networks.

scholarly journals Optimization Algorithms for Detection of Social Interactions

Algorithms ◽  
2020 ◽  
Vol 13 (6) ◽  
pp. 139 ◽  
Author(s):  
Vincenzo Cutello ◽  
Georgia Fargetta ◽  
Mario Pavone ◽  
Rocco A. Scollo
Keyword(s):  
Social Networks ◽  
Local Search ◽  
Social Interactions ◽  
Community Detection ◽  
Experimental Analysis ◽  
Search Strategy ◽  
Random Search ◽  
Optimization Methods ◽  
Research Areas ◽  
Greedy Optimization

Community detection is one of the most challenging and interesting problems in many research areas. Being able to detect highly linked communities in a network can lead to many benefits, such as understanding relationships between entities or interactions between biological genes, for instance. Two different immunological algorithms have been designed for this problem, called Opt-IA and Hybrid-IA, respectively. The main difference between the two algorithms is the search strategy and related immunological operators developed: the first carries out a random search together with purely stochastic operators; the last one is instead based on a deterministic Local Search that tries to refine and improve the current solutions discovered. The robustness of Opt-IA and Hybrid-IA has been assessed on several real social networks. These same networks have also been considered for comparing both algorithms with other seven different metaheuristics and the well-known greedy optimization Louvain algorithm. The experimental analysis conducted proves that Opt-IA and Hybrid-IA are reliable optimization methods for community detection, outperforming all compared algorithms.

scholarly journals The switching mechanisms of social network densification

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Teruyoshi Kobayashi ◽  
Mathieu Génois
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Context ◽  
Social Interactions ◽  
Regime Switching ◽  
Face To Face ◽  
The Social ◽  
Switching Property ◽  
Distinctive Type ◽  
Insight Into

AbstractDensification and sparsification of social networks are attributed to two fundamental mechanisms: a change in the population in the system, and/or a change in the chances that people in the system are connected. In theory, each of these mechanisms generates a distinctive type of densification scaling, but in reality both types are generally mixed. Here, we develop a Bayesian statistical method to identify the extent to which each of these mechanisms is at play at a given point in time, taking the mixed densification scaling as input. We apply the method to networks of face-to-face interactions of individuals and reveal that the main mechanism that causes densification and sparsification occasionally switches, the frequency of which depending on the social context. The proposed method uncovers an inherent regime-switching property of network dynamics, which will provide a new insight into the mechanics behind evolving social interactions.

scholarly journals Emergence and evolution of social networks through exploration of the Adjacent Possible space

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Enrico Ubaldi ◽  
Raffaella Burioni ◽  
Vittorio Loreto ◽  
Francesca Tria
Keyword(s):  
Social Networks ◽  
Community Structure ◽  
Social Interactions ◽  
Clustering Coefficient ◽  
Human Beings ◽  
Broad Distribution ◽  
Phone Calls ◽  
Dynamical Features ◽  
Twitter Users ◽  
American Physical

AbstractThe interactions among human beings represent the backbone of our societies. How people establish new connections and allocate their social interactions among them can reveal a lot of our social organisation. We leverage on a recent mathematical formalisation of the Adjacent Possible space to propose a microscopic model accounting for the growth and dynamics of social networks. At the individual’s level, our model correctly reproduces the rate at which people acquire new acquaintances as well as how they allocate their interactions among existing edges. On the macroscopic side, the model reproduces the key topological and dynamical features of social networks: the broad distribution of degree and activities, the average clustering coefficient and the community structure. The theory is born out in three diverse real-world social networks: the network of mentions between Twitter users, the network of co-authorship of the American Physical Society journals, and a mobile-phone-calls network.

Sign in / Sign up

Export Citation Format

Share Document