Instagram上多模态流行预测的局限性--一个新的鲁棒、高效和可解释的基线
On the Limits to Multi-Modal Popularity Prediction on Instagram -- A New Robust, Efficient and Explainable Baseline
摘要：
我们的全球人口在Instagram等平台上贡献视觉内容，试图以前所未有的速度表达自己并吸引观众。在本文中，我们重新审视了Instagram上的流行预测。我们提出了一个强大的，高效的，可解释的基线，基于人口的流行度预测，实现强大的排名性能。我们采用最新的方法在计算机视觉，以最大限度地提高信息提取的视觉模态。我们使用迁移学习来提取视觉语义，如概念，场景和对象，允许在广泛的，可解释的消融研究中进行新的审查。我们通知特征选择走向一个强大的和可扩展的模型，但也说明了功能的相互作用，在计算社会科学的进一步调查提供了新的方向。我们最强大的模型为Instagram上基于人口的流行度预测提供了下限。这些模型立即适用于社交媒体监控和影响者识别。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
