Understanding Image Virality

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.

Pre-print, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015

References in corpus (5)

• DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
• Deep Fragment Embeddings for Bidirectional Image Sentence Mapping
• Exploring Image Virality in Google Plus
• Deep Feelings: A Massive Cross-Lingual Study on the Relation between Emotions and Virality
• A Scalable Heuristic for Viral Marketing Under the Tipping Model

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• Image memorability predicts social media virality and externally-associated commenting
• Viraliency: Pooling Local Virality
• Salienteye: Maximizing Engagement While Maintaining Artistic Style on Instagram Using Deep Neural Networks
• On the Limits to Multi-Modal Popularity Prediction on Instagram -- A New Robust, Efficient and Explainable Baseline
• Modeling Image Virality with Pairwise Spatial Transformer Networks
• Pay Attention to Virality: understanding popularity of social media videos with the attention mechanism
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• cvpaper.challenge in 2016: Futuristic Computer Vision through 1,600 Papers Survey
• cvpaper.challenge in 2015 - A review of CVPR2015 and DeepSurvey
• On the Virality of Animated GIFs on Tumblr
• Compare and Contrast: Learning Prominent Visual Differences