On the Limits to Multi-Modal Popularity Prediction on Instagram - A New Robust, Efficient and Explainable Baseline

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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 so cial media monitoring and influencer identification.
Original languageEnglish
Title of host publicationProceedings of the 13th International Conference on Agents and Artificial Intelligence
Volume2 - ICAART
PublisherSCITEPRESS Digital Library
Publication date2021
Pages1200-1209
ISBN (Print)978-989-758-484-8
DOIs
Publication statusPublished - 2021
Event13th International Conference on Agents and Artificial Intelligence - Online Streaming
Duration: 4 Feb 20216 Feb 2021
http://www.icaart.org/

Conference

Conference13th International Conference on Agents and Artificial Intelligence
LocationOnline Streaming
Period04/02/202106/02/2021
Internet address

Keywords

  • Visual
  • Popularity
  • Explainable
  • Instagram
  • Social

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