Authors:
Christoffer Riis
1
;
Damian Konrad Kowalczyk
2
;
3
and
Lars Kai Hansen
3
Affiliations:
1
Technical University of Denmark, DTU Compute, Matematiktorvet 303B, 2800 Kongens Lyngby, Denmark
;
2
Microsoft Corporation, Business Applications Group, Kanalvej 7, 2800 Kongens Lyngby, Denmark
;
3
Technical University of Denmark, DTU Compute, Matematiktorvet 303B, Denmark
Keyword(s):
Visual, Popularity, Explainable, Instagram, Social.
Abstract:
Our global population contributes visual content on platforms like Instagram, attempting to express themselves and engage their audiences, at an unprecedented and increasing rate. In this paper, we revisit the popularity prediction on Instagram. We present a robust, efficient, and explainable baseline for population-based popularity prediction, achieving strong ranking performance. We employ the latest methods in computer vision to maximise the information extracted from the visual modality. We use transfer learning to extract visual semantics such as concepts, scenes, and objects, allowing a new level of scrutiny in an extensive, explainable ablation study. We inform feature selection towards a robust and scalable model, but also illustrate feature interactions, offering new directions for further inquiry in computational social science. Our strongest models inform a lower limit to population-based predictability of popularity on Instagram. The models are immediately applicable to s
ocial media monitoring and influencer identification.
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