论文标题
在Instagram上多模式受欢迎程度预测的限制 - 一种新的健壮，高效且可解释的基线
On the Limits to Multi-Modal Popularity Prediction on Instagram -- A New Robust, Efficient and Explainable Baseline
论文作者
论文摘要
我们的全球人口在Instagram等平台上贡献了视觉内容，试图以前所未有且增加的速度表达自己并吸引观众。在本文中，我们重新审视了Instagram上的受欢迎程度预测。我们为基于人群的受欢迎程度预测提供了强劲，高效且可解释的基线，从而实现了强劲的排名。我们在计算机视觉中采用了最新的方法来最大程度地利用从视觉模式中提取的信息。我们使用转移学习来提取视觉语义，例如概念，场景和对象，从而在广泛的，可解释的消融研究中进行了新的审查。我们将特征选择介绍到一个可靠且可扩展的模型，但也说明了特征交互，为计算社会科学的进一步探究提供了新的方向。我们最强大的模型为Instagram上基于人群的可预测性提供了下限。这些模型立即适用于社交媒体监控和影响者的识别。
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.
