فتانه تقی یاره

Knowing the current public opinion and predicting its trend using opinion formation models is very applicable. The social impact model of opinion formation is a discrete binary opinion model. It describes how interactions among individuals and sharing their opinions about a specific topic in a social network affect the dynamics of their opinions and form the opinion of society. The society could be an online social network. In this research, we considered the effect of segregation on opinion formation. Segregation is a phenomenon that happens due to homophily and is measured based upon network topology. Homophily is the tendency of individuals to interact with others who share similar traits. We used scale-free networks to model interactions between individuals. The social impact model includes a noise parameter, which is the stochastic part of the model, dealing with the inexplicable behavior of individuals and the effects of other influentials, e.g., mass media. Since this noise is a white noise with no bias toward any possible opinion, for simplicity, we assumed a noise-free social impact model, which is valid in equilibrium analysis we considered. The results reveal that with the same attributes for the individuals, the more segregated opinion group dominates the less segregated opinion group on average. Therefore, with the same population size and individual characteristics of both opinion groups, segregation is an overall influential factor for opinion formation. A more segregated opinion group attracts some individuals from the other group and becomes the majority opinion group of society in equilibrium.

Several factors such as engagement with peer groups, government policies, personal attitudes can affect people’s opinion about a specific subject. Most of scholars in this area focus on the interaction of individuals in social network and overlook other factors. In this paper, an opinion formation model is presented in which the internal tendencies of individuals are considered as an intrinsic property. In this model, people revise their opinion based on their neighbors’ opinion, trust/distrust between them and their own internal tendency. By internal tendency we mean a set of internal factors which may affect the decision of individuals. Simulation results show that this model is able to predict individuals’ opinion which might present their preferences to different products in social network when parameters of the model are identified and assigned. As this model can predict people’s opinion in the market, it can be used in definition of a marketing or production strategy.

Opinion formation is a collective behavior, describing the dynamics of people’s opinions due to their interactions. Nowadays, social media are broadly used and cause a lot of interactions among users who mainly know each other merely as a username, but significantly influence each others’ opinions and emotions. Both emotions and opinions spread across users in social media via their exchanged posts. Furthermore, based on psychology research, emotion affects people’s opinion. In this research, we implemented two binary classifiers to predict the users’ next opinions considering previous posts sent in online community: an original classifier, a classifier based on the social impact model of opinion formation; and an emotion-integrated classifier, a classifier based on the social impact model of opinion formation integrated with an emotion model to achieve an improved model. To evaluate the improved classifier, we used a dataset containing some debates from the CreateDebate.com website and compared the performance of the original classifier with the performance of the emotion integrated classifier. The experiment results show that considering emotions improves the accuracy and precision of the social impact model of opinion formation in social media.