Discovering social influencers with network visualization: evidence from the tourism domain

Identifying influential users and predicting their network impact on social networks have attracted tremendous interest from both academia and industry. Social networks follow a power-law degree distribution of nodes, with a few hub nodes and a long tail of peripheral nodes. While there exist consolidated approaches supporting the identification and characterization of hub nodes, research on the analysis of the multi-layered distribution of peripheral nodes is limited. In social media, hub nodes represent social influencers. However, the literature provides evidence of the multi-layered structure of influence networks, emphasizing the distinction between influencers and influence. Information seems to spread following multi-hop paths across nodes in peripheral network layers. This paper proposes a visual approach to the graphical representation and exploration of peripheral layers and clusters by exploiting the theory of k-shell decomposition analysis. The core concept of the proposed approach is to partition the node set of a graph into pre-defined hub and peripheral nodes. Then, a power-law based modified force-directed method is applied to clearly display local multi-layered neighborhood clusters around hub nodes based on a characterization of the content of message that we refer to as content specificity. We put forward three hypotheses that allow the graphical identification of the peripheral nodes that are more likely to be influential and contribute to the spread of information. Hypotheses are tested on a large sample of tweets from the tourism domain.