Realistic influence maximization based on followers score and engagement grade on instagram

Abstract

In recent years, the emergence of social media influencers attracts the study of a realistic influence maximization (IM) technique. The theoretical performance of IM has become matured. However, it is not enough since IM has to be implemented in a social media environment. Realistic IM algorithms and diffusion models have been proposed, such as the addition of user factors or a learning agent. However, most studies still relied on the influence spread benchmark, which makes the usefulness questionable. This research is among the first IM study using Instagram data. In this study, two diffusion models are proposed, which are based on the original IC and LT models, with the addition of the engagement grade (EG) factor. An algorithm called IMFS (IM with followers score) is proposed to accommodate the new models as well as IC and LT. In addition, realistic benchmark methods are proposed, namely the average engagement of the activated users, and the overlapping between post likers and activated users. The result shows that the proposed models are 2-3x more realistic if compared to IC and LT.