Identification of influencers in online social networks: measuring influence considering multidimensional factors exploration

Abstract

Online Social networks exhibit heterogeneous nature with nodes playing far different roles in structure and function. To identify influencers is thus very significant, allowing us to control the outbreak of public negative opinion, to conduct advertisements for e-commercial products, to predict popular scientific publications, and so on. The identification of influencers attracts increasing attentions from both computer science and communication science, with multiple dimensional metrics ranging from structure-based to information-based and action-based. However, most work simply rely on one dimensional metrics. Therefore, in this paper, we analyze three dimensional characteristics (structure-based, information-based, and action-based factors) to develop the multidimensional social influence (MSI) measurement approach. With topic distillation and conditional expectation, the MSI approach can not only measure users topic-level influence, but also measure users global-level influence. Based on data collected from SinaWeibo.com, the experimental results show that the proposed framework outperforms two traditional methods (LeaderRank and FBI) both on the topic-level and the global-level. The proposed framework can be effectively applied to promote word-of-mouth marketing, and to steer public opinion in certain directions, even to support decisions during a negotiation process.

Social Media Influencers: Customer Attitudes and Impact on Purchase Behaviour

“…Lee and Eastin develop and validate a multi-dimensional measure of Perceived Authenticity of Social Media Influencers (PASMIs) consisting of sincerity, truthful endorsements, visibility, expertise, and uniqueness [24]. Zhuang et al propose a complex measurement approach to determine both users' topic-level influence and users' global-level influence [25]. There are numerous pre-defined influencer indices accessible, with Klout score being one of the earliest examples.…”

Towards a Real Comprehension of Social Networks Effects on Cybermedia Web Traffic: Quantitative Impact in the 27 European Union Countries Most Popular Online Journals

“…The rst of these approaches is reinforced by the consolidation of the manual semantic web, considered not only as a strategic way of showing how the network to which users were accustomed is being transformed both in its interfaces and in the visualization of its contents (DiNucci, 1999) but also as its changing as a great agora where Internet users from all over the world establish a conversation on a global scale in which they share all kinds of contents including journalistic ones (O'Reilly, 2005), with contributions referring to the use of social networks as marketing tools placed at the service of speci c productive sectors (Gibreel, Al Otaibi, and Altmann, 2018) but also with those that delve into their dimension as collaborative learning platforms (Nasrir Ansari, and Khan, 2020) or as creators of certain states of opinion that in uence electoral consultations in democratic societies (Kahne and Bowyer, 2018). These proposals are also outlined through the gure of the prosumer in its aspect of producer and creator of content (To er, 1980; Kotler, 1986;Ritzer and Jurgenson, 2010) highlighting subjects such as the capacity to generate topics of the most varied nature (Bird, 2011), segmentation according to population groups (Olin-Scheller and Wikström, 2010; Martínez-Sala, Segarra, and Monserrat, 2018), the in uencer phenomenon (Garibay et al, 2019;Hughes, Swaminathan, and Brooks, 2019;Zhuang, Li, and Zhuang, 2021) or everything that has to do with the implementation of crowdfunding projects (Fanea-Ivanovici, 2019; Clauss et al 2020).…”

User Real-Time Influence Ranking Algorithm of Social Networks Considering Interactivity and Topicality

“…Generally speaking, a topic on Weibo is the collection of all blogs that address a single public opinion event [ 38 ]. Users typically post blogs on the same topic, and other users’ forwarding comments on these blogs are usually centered on the topic [ 39 ]. Based on this circumstance, we collect all blogs and the forwarding comments that correspond with them under a topic and put them together into a topic document set.…”