Influence Maximization Diffusion Models based on Engagement and Activeness on Instagram

Abstract An influencer is an impactful content creator on social media. The emergence of influencers led to increased influencer marketing. The task of picking the right influencers is widely studied through influence maximization (IM). Existing IM studies have matured in terms of theoretical performance, but not very realistic in real-world. First, existing IM diffusion models didn't consider the engagement level and activeness of the users. Secondly, there were no studies that compare activated users against actual influenced users on Instagram. To address both problems, three new realistic diffusion models are proposed, based on the Independent Cascade and Linear Threshold models, namely IC-u, LT-u and UAD models. This study was implemented using Instagram data. Meanwhile, UAD model uses two thresholds, namely user's awareness, and user's tendency. These models incorporate user's activeness and engagement factors that represent the susceptibility to influence and the degree of influence, respectively. The proposed models were proven to be up to 2.72x more realistic and produced more engaging and more active users. The seeds set (influencers) identified by the IM algorithms under the proposed models are expected to have more impact on an actual brand marketing campaign if compared to existing models.