Opinion dynamics have attracted the interest of researchers from different fields. Local interactions among individuals create interesting dynamics for the system as a whole. Such dynamics are important from a variety of perspectives. Group decision making, successful marketing, and constructing networks (in which consensus can be reached or prevented) are a few examples of existing or potential applications. The invention of the Internet has made the opinion fusion faster, unilateral, and on a whole different scale. Spread of fake news, propaganda, and election interferences have made it clear there is an essential need to know more about these dynamics. The emergence of new ideas in the field has accelerated over the last few years. In the first quarter of 2020, at least 50 research papers have emerged, either peer-reviewed and published or on preprint outlets such as arXiv. In this paper, we summarize these ground-breaking ideas and their fascinating extensions and introduce newly surfaced concepts.

Recent advances in opinion propagation dynamics: A 2020 Survey

On The Role of Community Structure in Evolution of Opinion Formation: A New Bounded Confidence Opinion Dynamics

pdf/identifying-influential-nodes-in-complex-networks-effective-1s4rt70a8y.pdf

Identifying influential nodes in complex networks: Effective distance gravity model

New trends in influence maximization models

Positive opinion maximization in signed social networks

CAOM: A community-based approach to tackle opinion maximization for social networks

Opinion maximization in social networks is an optimization problem, which targets at determining some influential individuals (i.e., seed nodes), propagating the desired opinion to their neighbors, and eventually obtaining maximum opinion spread. Previous studies assume that influence power of one individual is mainly calculated by using some network structure properties and once the opinion of one individual is determined, its opinion usually keeps unchanged. However, in the real scenario, the influence power of one individual may be unknown and should be closely associated with the dynamic opinion formation process. In this paper, we propose a novel Influence Power-based Opinion Framework (IPOF) to solve the opinion maximization problem, which is composed of two phases: 1) influence power estimation, and 2) elimination of influence overlapping (EIO). Specifically, we design the exponential influence power and estimate the unknown parameter of influence power through maximum likelihood estimation due to its simplicity, practicability, and superior convergence in large samples. To generate the opinion series dynamically, the weighted voter model is proposed by leveraging influence power and intimate degree. Moreover, we also prove that the likelihood function is concave by using Hessian matrix. To determine the initial seed nodes and facilitate large opinion propagation, influence power-based EIO algorithm is proposed. Experimental results in six social networks demonstrate that the proposed approach outperforms the state-of-the-art benchmarks.

Opinion Maximization Through Unknown Influence Power in Social Networks Under Weighted Voter Model

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CAOM: A community-based approach to tackle opinion maximization for social networks

Opinion Maximization Through Unknown Influence Power in Social Networks Under Weighted Voter Model