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.

One of the most important aspects of learning is attention. This is even more pronounced in online education due to the instructor’s need for sufficient control over the learner’s environment. This study aims to identify the pattern of changes in the cognitive state of learners, from unconsciousness to consciousness. By observing the brain’s response while learners watch micro videos, we sought to understand the impact of these ego states on learners’ performance in E-Learning environments. Our findings suggest that learners' ego states significantly impact their learning performance. The first phase aimed to precisely detect the transition point from the unconscious to the conscious state. In the second phase, we tried to differentiate between these two states by comparing their learning performance. Finally, the obtained results led us to believe that learning outcomes are subject to a significant increase when the brain state changes. These findings emphasize the importance of early engagement strategies in online learning, as improving the initial phase of content delivery significantly increases overall learning outcomes. By understanding the transition between different ego states, educational content authors would create more effective learning materials that maintain continuous learner engagement.

Today, the Internet of Things is a widely recognized phenomenon that generates a significant amount of data and connects many devices. Many products are incorporating electronic components to facilitate their integration and interaction with the Internet. Scalable and efficient trust management systems are required to maintain network reliability, considering the increasing number of IoT devices and generated data. In order to enable scalable trust management in social IoT, this paper presents a sharding-based scalable trust management approach that combines social interactions with smart contract functionality. Through the division of transaction state into smaller segments and the enhancement of trust value propagation among connected devices, sharding techniques in blockchain can offer scalable trust management protocols. When implementing the model on the Hyperledger Fabric platform, we carried out a thorough evaluation. The model calculates trust in terms of trust convergence and success rate efficiently. We have conducted several tests to evaluate the scalability of the model. To boost it, we have also implemented the state sharding. We also conducted a study to highlight the advantages of the sharding strategy on the scalability of the model. The results demonstrate that using shards significantly improves trust management capacity on the blockchain. The proposed method demonstrates the potential application of sharding in blockchain-based Trust Management (TM) for scalable trust management in SIoT.