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 Reddit1 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.
There exist several qualitative theories of the kinds of content that are likely to go viral @cite_3 @cite_27 . Only a few works have quantitatively analyzed content, for instance Tweets @cite_33 and New York Times articles @cite_28 to predict their virality. However, in spite of them being a large part of our online experience, the connections between content in visual media and their virality has not been analyzed. This forms the focus of our work.
@cite_28 @cite_27 @cite_33 @cite_3
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Why are certain pieces of online content (e.g., advertisements, videos, news articles) more viral than others? This article takes a psychological approach to understanding diffusion. Using a unique data set of all the New York Times articles published over a three-month period, the authors examine how emotion shapes virality. The results indicate that positive content is more viral than negative content, but the relationship between emotion and social transmission is more complex than valence alone. Virality is partially driven by physiological arousal. Content that evokes high-arousal positive (awe) or negative (anger or anxiety) emotions is more viral. Content that evokes low-arousal, or deactivating, emotions (e.g., sadness) is less viral. These results hold even when the authors control for how surprising, interesting, or practically useful content is (all of which are positively linked to virality), as well as external drivers of attention (e.g., how prominently content was featured). Experimental re... Retweeting is the key mechanism for information diffusion in Twitter. It emerged as a simple yet powerful way of disseminating information in the Twitter social network. Even though a lot of information is shared in Twitter, little is known yet about how and why certain information spreads more widely than others. In this paper, we examine a number of features that might affect retweetability of tweets. We gathered content and contextual features from 74M tweets and used this data set to identify factors that are significantly associated with retweet rate. We also built a predictive retweet model. We found that, amongst content features, URLs and hashtags have strong relationships with retweetability. Amongst contextual features, the number of followers and followees as well as the age of the account seem to affect retweetability, while, interestingly, the number of past tweets does not predict retweetability of a user's tweet. We believe that this research would inform the design of sensemaking and analytics tools for social media streams. Social transmission is everywhere. Friends talk about restaurants, policy wonks rant about legislation, analysts trade stock tips, neighbors gossip, and teens chitchat. Further, such interpersonal communication affects everything from decision making and well-being (Asch, 1956; Mehl, Vazire, Holleran, & Clark, 2010) to the spread of ideas, the persistence of stereotypes, and the diffusion of culture (Heath, 1996; Heath, Bell, & Sternberg, 2001; Kashima, 2008; Schaller, Conway, & Tanchuk, 2002; Schaller & Crandall, 2004). But although it is clear that social transmission is both frequent and important, what drives people to share, and why are some stories and information shared more than others?