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unige:92466 
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Analyzing and Predicting GIF Interestingness |
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| Published in | MM '16 Proceedings of the 2016 ACM on Multimedia Conference. Amsterdam, The Netherlands - October 15 - 19, 2016 - . 2016 | |
| Abstract | Animated GIFs have regained huge popularity. They are used in instant messaging, online journalism, social media, among others. In this paper, we present an in-depth study on the interestingness of GIFs. We create and annotate a dataset with a set of affective labels, which allows us to investigate the sources of interest. We show that GIFs of pets are considered more interesting that GIFs of people. Furthermore, we study the connection of interest to other features and factors such as popularity. Finally, we build a predictive model and show that it can estimate GIF interestingness with high accuracy. Our model outperforms the existing methods on GIF popularity, as well as a model based on still image interestingness, by a large margin. We envision that the insights and method developed can be used for automatic recognition and generation of interesting GIFs. | |
| Keywords | GIF — Emotion — Interest — Computer vision | |
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| Research groups | Affective sciences Computer Vision and Multimedia Laboratory | |
| Citation (ISO format) | GYGLI, Michael, SOLEYMANI, Mohammad. Analyzing and Predicting GIF Interestingness. In: MM '16 Proceedings of the 2016 ACM on Multimedia Conference. Amsterdam, The Netherlands. [s.l.] : [s.n.], 2016. https://archive-ouverte.unige.ch/unige:92466 |
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