Accession Number : AD1024886


Title :   Understanding Image Virality


Descriptive Note : Conference Paper


Corporate Author : Virginia Polytechnic Institute and State University Blacksburg United States


Personal Author(s) : Deza,Arturo ; Parikh,Devi


Full Text : https://apps.dtic.mil/dtic/tr/fulltext/u2/1024886.pdf


Report Date : 07 Jun 2015


Pagination or Media Count : 11


Abstract : Virality of online content on social networking websites is an important but esoteric phenomenon often studied infields 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 a reviewed, 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.


Descriptors :   computer vision , social networking services , images , semantics


Subject Categories : Computer Systems


Distribution Statement : APPROVED FOR PUBLIC RELEASE