The predictability of social media popularity is a topic of much scientific interest and significant practical importance. We present a new strong baseline for popularity prediction on Instagram, which is both robust and efficient to compute. The approach expands previous work by a comprehensive ablation study of the predictive power of multiple representations of the visual modality and by detailed use of explainability tools . We use transfer learning to extract visual semantics  as concepts, scenes, and objects, which allows us to interpret and explain the trained model and predictions. The study is based in one million posts extracted from Instagram. We approach the problem of popularity prediction as a ranking problem, where we predict the log-normalised number of likes. Through our ablation study design, we can suggest models that outperform a previous state-of-the-art black-box method for multi-modal popularity prediction on Instagram .
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 maximize 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 social media monitoring and influencer identification .
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The predictability of social media popularity is a topic of much scientific interest and significant practical importance.
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.
R
new strong baseline for popularity prediction on Instagram, which is both robust and efficient to compute. The approach expands previous work by a comprehensive ablation study of the predictive power of multiple representations of the visual modality and by detailed use of explainability tools
robust, efficient, and explainable baseline for population-based popularity prediction, achieving strong ranking performance. We employ the latest methods in computer vision to maximize the information extracted from the visual modality
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such
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which allows us to interpret and explain the trained model and predictions. The study is based in one million posts extracted from Instagram. We approach the problem of popularity prediction as a ranking problem, where we predict the log-normalised number of likes. Through our ablation study design, we can suggest models that outperform a previous state-of-the-art black-box method for multi-modal popularity prediction on Instagram
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 social media monitoring and influencer identification