Ranking Micro-Influencers: A Multimedia Framework with Multi-Task and Interpretable Architectures

With the rise in use of social media to promote branded products, the demand for effective influencer marketing has increased. Brands are looking for improved ways to identify valuable influencers among a vast catalogue; this is even more challenging with micro-influencers, which are more affordable than mainstream ones but difficult to discover. In this paper, we propose a novel multi-task learning framework to improve the state of the art in micro-influencer ranking based on multimedia content. Moreover, since the visual congruence between a brand and influencer has been shown to be a good measure of compatibility, we provide an effective visual method for interpreting our model’s decisions, which can also be used to inform brands’ media strategies. We compare with the current state of the art on a recently constructed public dataset and we show significant improvement both in terms of accuracy and model complexity. We also introduce a methodology for tuning the image and text contribution to the final ranking score. The techniques for ranking and interpretation presented in this work can be generalized to arbitrary multimedia ranking tasks that have datasets with a similar structure.