Abstract
Virality of online content on social networking websites is an important but esoteric phenomenon often studied in fields like marketing, psychology and data mining. In this paper we study viral images from a computer vision perspective. We introduce three new image datasets from Reddit, and define a virality score using Reddit metadata. We train classifiers with state-of-the-art image features to predict virality of individual images, relative virality in pairs of images, and the dominant topic of a viral image. We also compare machine performance to human performance on these tasks. We find that computers perform poorly with low level features, and high level information is critical for predicting virality. We encode semantic information through relative attributes. We identify the 5 key visual attributes that correlate with virality. We create an attribute-based characterization of images that can predict relative virality with 68.10% accuracy (SVM+Deep Relative Attributes) – better than humans at 60.12%. Finally, we study how human prediction of image virality varies with different `contexts’ in which the images are viewed, such as the influence of neighbouring images, images recently viewed, as well as the image title or caption. This work is a first step in understanding the complex but important phenomenon of image virality. Our datasets and annotations will be made publicly available.
Abstract (translated by Google)
社交网站上的在线内容的病毒性是在市场营销，心理学和数据挖掘等领域经常研究的一个重要但深奥的现象。在本文中，我们从计算机视觉角度研究病毒图像。我们介绍来自Reddit的三个新的图像数据集，并使用Reddit元数据定义病毒传播得分。我们使用最先进的图像特征来训练分类器，以预测单个图像的病毒传播，图像对的相对病毒传播速度以及病毒图像的主要话题。我们还将这些任务的机器性能与人的表现进行比较。我们发现，计算机表现不佳，低层次的特征，高层次的信息对于预测病毒传播是至关重要的。我们通过相关属性编码语义信息。我们确定与病毒传播相关的5个关键视觉属性。我们创建了一个基于属性的图像表征，可以以68.10％的准确率（SVM +深度相对属性）预测相对的病毒传播速度 - 比人类在60.12％更好。最后，我们研究了图像传播性的人预测如何与这些图像被视为不同的`环境，如相邻图像的影响，最近看的图像，以及图像标题或标题变化。这项工作是了解图像病毒传播的复杂而重要现象的第一步。我们的数据集和注释将公开发布。
URL
https://arxiv.org/abs/1503.02318