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In this age of Facebook, Instagram, and Twitter, there is rapidly growing interest in understanding network-enabled opinion dynamics in large groups of autonomous agents. The phenomena of opinion polarization, the spread of propaganda and fake news, and the manipulation of sentiment is of interest to large numbers of organizations and people. Whether it is the more nefarious players such as foreign governments that are attempting to sway elections or it is more open and above board, such as researchers who want to make large groups of people aware of helpful innovations, what is at stake is often significant.

In this paper, we review opinion dynamics including the extensions of many classical models as well as some new models that deepen understanding. For example, we look at models that track the evolution of an individual’s power, that include noise, and that feature sequentially dependent topics, to name a few.

While the first papers studying opinion dynamics appeared over 60 years ago, there is still a great deal of room for innovation and exploration. We believe that the political climate and the extraordinary (even unprecedented) events in the sphere of politics in the last few years will inspire new interest and new ideas.

It is our aim to help those interested researchers understand what has already been explored in a significant portion of the field of opinion dynamics. We believe that in doing this, it will become clear that there is still much to be done.

In todays world, identity of human beings has expanded beyond the real world to the cyber world. Virtual identity of millions of users is present at various web-based Social Networking Sites (SNSs) such as Myspace, Facebook, and Twitter. Interactions through SNSs have become a part of our daily practices, which eventually leaves a big trail of behavioral pattern in virtual domain. In this paper, the authors examined the feasibility of person identification using such social network activities as behavioral biometrics. Experimentation includes extraction of a number of idiosyncratic features from SNSs and analysis of their performance as novel social behavioral biometric features.

The aim of this work is to implement a statistical mechanics theory of social interaction, generalizing econometric discrete choice models. A class of simple mean-field discrete models is introduced and discussed both from the theoretical and phenomenological point of view. We propose a parameter evaluation procedure and test it by fitting the model against three families of data coming from different cases: the estimated interaction parameters are found to have similar positive values, giving a quantitative confirmation of the peer imitation behavior found in social psychology. Furthermore, all the values of the interaction parameters belong to the phase transition regime suggesting its possible role in the study of social systems.

We develop a simple Keynesian type business cycle model in which heterogeneous agents are either optimistic or pessimistic. If the majority of the agents are optimistic, then consumption expenditures are high and the economy booms, otherwise consumption expenditures are low and the economy is in a recession. Within our model, the sentiment of the agents is affected by their social interactions. For instance, people regularly meet each other and thus their mood may change. Overall, our model suggests that swings in consumer confidence may generate irregular fluctuations in economic activity.

Brand awareness is recognized to be an important determinant in shaping the success of durables [13, 16], yet it is very difficult to be quantified. This is exactly the main goal of this paper: propose a suitable model where brand awareness of two competing firms is modeled and, eventually, estimated. To this aim, we build a random utility model for a duopoly where each competitor is characterized by different pricing strategies and brand awareness. As a result, different levels of market shares will emerge at the equilibrium. As a case study, we calibrate the model with real data from the smartphone industry obtaining an estimate of the value of the brand awareness of two leading brands.

The influence of additional information on the decision making of agents, who are the interacting members of a society, is analyzed within the mathematical framework based on the use of quantum probabilities. The introduction of social interactions, which influence the decisions of individual agents, leads to a generalization of the quantum decision theory (QDT) developed earlier by the authors for separate individuals. The generalized approach is free of the standard paradoxes of classical decision theory. This approach also explains the error-attenuation effects observed for the paradoxes occurring when decision makers, who are members of a society, consult with each other, increasing in this way the available mutual information. A precise correspondence between QDT and classical utility theory is formulated via the introduction of an intermediate probabilistic version of utility theory of a novel form, which obeys the requirement that zero-utility prospects should have zero probability weights.

This study investigated the influence of social interactions and subjective norms on individuals’ social media postings. The study developed a theoretical model by extending the Theory of Reasoned Action with social interactions. A CB-SEM analysis was conducted to test the hypothesised relationships based on the data collected from 312 social media users. Results indicated that social interactions (i.e. likes, shares, comments and follows) were significantly related to the attitude towards the selfie-posting behaviour. Further, the attitude and subjective norms were significantly related to behavioural intentions, which together accounted for a significant amount of variance in the actual behaviour. The findings contributed to literature by introducing the significant role of “social interactions” in predicting the attitude towards the selfie-posting behaviour.