Ranking Micro-Influencers: a Novel Multi-Task Learning and Interpretable Framework

TL;DR

This paper introduces a multi-task learning framework for ranking micro-influencers based on multimedia content, enhancing accuracy and interpretability to aid brands in influencer selection.

Contribution

It presents a novel multi-task learning approach combined with an interpretable visual method for micro-influencer ranking, improving over existing methods.

Findings

Significant accuracy improvement over state-of-the-art methods

Effective visual interpretation of model decisions

Model complexity is reduced while maintaining performance

Abstract

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 good measure of compatibility, we provide an effective visual method for interpreting our models' 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. The techniques for ranking and interpretation presented in this work can be generalised to arbitrary multimedia ranking tasks that have datasets with a similar structure.