Influential Users In Online Social Networks Research Articles

Discovering Hidden Topical Hubs and Authorities Across Multiple Online Social Networks

Finding influential users in online social networks (OSNs) is an important problem with many possible useful applications. Many methods have been proposed to identify influential users in OSNs. PageRank and HITs are two well known examples that determine influential users through link analysis. In recent years, new models that consider both content and social network links have been developed. The Hub and Authority Topic (HAT) model is one that extends HITS to identify topic-specific hubs and authorities by jointly learning hubs, authorities, and topical interests from users' relationship and textual content. However, many of the previous works are confined to identifying influential users within a single OSN. These models, when applied to multiple OSNs, could not learn influential users under a common set of topics nor address platform preferences. In this paper, we therefore propose the MPHAT model, an extension of HAT, to jointly model the topic-specific hub users, authority users, their topical interests and platform preferences. We evaluate MPHAT against several existing state-of-the-art methods in three tasks: (i) modeling of topics, (ii) platform choice prediction, and (iii) link recommendation. Based on our extensive experiments in multiple OSNs settings using synthetic datasets and real-world datasets from Twitter and Instagram, we show that MPHAT is comparable to state-of-the-art topic models in learning topics but outperforms the state-of-the-art models in platform prediction and link recommendation tasks. We also empirically demonstrate the ability of MPHAT to determine influential users within and across multiple OSNs.

Identifying Multiple Influential Users Based on the Overlapping Influence in Multiplex Networks

Online social networks (OSNs) are interaction platforms that can promote knowledge spreading, rumor propagation, and virus diffusion. Identifying influential users in OSNs is of great significance for accelerating the information propagation especially when information is able to travel across multiple channels. However, most previous studies are limited to a single network or select multiple influential users based on the centrality ranking result of each user, not addressing the overlapping influence (OI) among users. In practice, the collective influence of multiple users is not equal to the total sum of these users' influences. In this paper, we propose a novel OI-based method for identifying multiple influential users in multiplex social networks. We first define the effective spreading shortest path (ESSP) by utilizing the concept of spreading rate in order to denote the relative location of users. Then, the collective influence is quantified by taking the topological factor and the location distribution of users into account. The identified users based on our proposed method are central and relatively scattered with a low overlapping influence. With the Susceptible-Infected-Recovered (SIR) model, we estimate our proposed method with other benchmark algorithms. Experimental results in both synthetic and real-world networks verify that our proposed method has a better performance in terms of the spreading efficiency.

Analysis of Online Social Network Connections for Identification of Influential Users

Online social networks (OSNs) are structures that help users to interact, exchange, and propagate new ideas. The identification of the influential users in OSNs is a significant process for accelerating the propagation of information that includes marketing applications or hindering the dissemination of unwanted contents, such as viruses, negative online behaviors, and rumors. This article presents a detailed survey of influential users’ identification algorithms and their performance evaluation approaches in OSNs. The survey covers recent techniques, applications, and open research issues on analysis of OSN connections for identification of influential users.

Who will lead and who will follow: Identifying Influential Users in Online Social Networks

Along with the explosive growth of the phenomenon Online Social Networks (OSN), identifying influential users in OSN has received a great deal of attention in recent years. However, the development of practical approaches for identifying them is still in its infancy. By means of a structured literature review, the authors analyze and synthesize the publications particularly from two perspectives. From a research perspective, they find that existing approaches mostly build on users’ connectivity and activity but hardly consider further characteristics of influential users. Moreover, they outline two major research streams. It becomes apparent that most marketing-oriented articles draw on real-world data of OSN, while more technology-oriented papers rather have a theoretical approach and mostly evaluate their artifacts by means of formal proofs. The authors find that a stronger collaboration between the scientific Business and Information Systems Engineering (BISE) and Marketing communities could be mutually beneficial. With respect to a practitioner’s perspective, they compile advice on the practical application of approaches for the identification of influential users. It is hoped that the results can stimulate and guide future research.