Will This Video Go Viral? Explaining and Predicting the Popularity of Youtube Videos

What makes content go viral? Which videos become popular and why others don't? Such questions have elicited significant attention from both researchers and industry, particularly in the context of online media. A range of models have been recently proposed to explain and predict popularity; however, there is a short supply of practical tools, accessible for regular users, that leverage these theoretical results. HIPie -- an interactive visualization system -- is created to fill this gap, by enabling users to reason about the virality and the popularity of online videos. It retrieves the metadata and the past popularity series of Youtube videos, it employs Hawkes Intensity Process, a state-of-the-art online popularity model for explaining and predicting video popularity, and it presents videos comparatively in a series of interactive plots. This system will help both content consumers and content producers in a range of data-driven inquiries, such as to comparatively analyze videos and channels, to explain and predict future popularity, to identify viral videos, and to estimate response to online promotion.Comment: 4 page

Predicting Successful Memes using Network and Community Structure

We investigate the predictability of successful memes using their early spreading patterns in the underlying social networks. We propose and analyze a comprehensive set of features and develop an accurate model to predict future popularity of a meme given its early spreading patterns. Our paper provides the first comprehensive comparison of existing predictive frameworks. We categorize our features into three groups: influence of early adopters, community concentration, and characteristics of adoption time series. We find that features based on community structure are the most powerful predictors of future success. We also find that early popularity of a meme is not a good predictor of its future popularity, contrary to common belief. Our methods outperform other approaches, particularly in the task of detecting very popular or unpopular memes.Comment: 10 pages, 6 figures, 2 tables. Proceedings of 8th AAAI Intl. Conf. on Weblogs and social media (ICWSM 2014

Will This Video Go Viral? Explaining and Predicting the Popularity of Youtube Videos

What makes content go viral? Which videos become popular and why others don't? Such questions have elicited significant attention from both researchers and industry, particularly in the context of online media. A range of models have been recently proposed to explain and predict popularity; however, there is a short supply of practical tools, accessible for regular users, that leverage these theoretical results. HIPie -- an interactive visualization system -- is created to fill this gap, by enabling users to reason about the virality and the popularity of online videos. It retrieves the metadata and the past popularity series of Youtube videos, it employs Hawkes Intensity Process, a state-of-the-art online popularity model for explaining and predicting video popularity, and it presents videos comparatively in a series of interactive plots. This system will help both content consumers and content producers in a range of data-driven inquiries, such as to comparatively analyze videos and channels, to explain and predict future popularity, to identify viral videos, and to estimate response to online promotion

A Survey on Prediction of Movie’s Box Office Collection Using Social Media

Predicting the box office profits of a movie prior to its world wide release are a significant but also an exigent problem that needs a advanced of Intelligence. Currently, social media has given away its diagnostic strength in a variety of fields, which encourages us to develop social media substance to predict box office profits. The collection of movies in provisions of profit relies on so many features for instance its making studio, type, screenplay superiority, pre release endorsement etc, each of which are usually utilized to approximation their probable achievement at the box office. Nevertheless, the “Wisdom of Crowd” and social media have been accredited as a powerful indication in appreciative customer activities to media. In this survey, we converse the influence of socially created Meta data derived from the social multimedia websites and review the effect of social media on box office collection and success of movies. This survey paper is written for (social networking) investigators who looking for to evaluate prediction of movies using social media. It gives a complete study of social media analytics for social networking, wikis, actually easy syndication feeds, blogs, newsgroups, chat and news feeds etc. Keywords: Social Networking, Social media. Movie’s Box Office, Prediction, Profitability, Sentiment analysi