@article{Shamma_Yew_Kennedy_Churchill_2021, title={Viral Actions: Predicting Video View Counts Using Synchronous Sharing Behaviors}, volume={5}, url={https://ojs.aaai.org/index.php/ICWSM/article/view/14154}, DOI={10.1609/icwsm.v5i1.14154}, abstractNote={ <p> In this article, we present a method for predicting the view count of a YouTube video using a small feature set collected from a synchronous sharing tool. We hypothesize that videos which have a high YouTube view count will exhibit a unique sharing pattern when shared in synchronous environments. Using a one-day sample of 2,188 dyadic sessions from the Yahoo! Zync synchronous sharing tool, we demonstrate how to predict the video’s view count on YouTube, specifically if a video has over 10 million views. The prediction model is 95.8% accurate and done with a relatively small training set; only 15% of the videos had more than one session viewing; in effect, the classifier had a precision of 76.4% and a recall of 81%. We describe a prediction model that relies on using implicit social shared viewing behavior such as how many times a video was paused, rewound, or fast-forwarded as well as the duration of the session. Finally, we present some new directions for future virality research and for the design of future social media tools. </p> }, number={1}, journal={Proceedings of the International AAAI Conference on Web and Social Media}, author={Shamma, David and Yew, Jude and Kennedy, Lyndon and Churchill, Elizabeth}, year={2021}, month={Aug.}, pages={618-621} }