Engagement dynamics and sensitivity analysis of YouTube videos - 2017 PROJECT TITLE : Engagement dynamics and sensitivity analysis of YouTube videos - 2017 ABSTRACT: YouTube, with lots of content creators, has become the preferred destination for viewing videos online. Through the Partner program, YouTube permits content creators to monetize their fashionable videos. Of important importance for content creators is which meta-level features (title, tag, thumbnail, and outline) are most sensitive for promoting video popularity. The popularity of videos conjointly depends on the social dynamics, i.e., the interaction of the content creators (or channels) with YouTube users. Using real-world data consisting of about half-dozen million videos unfold over twenty five thousand channels, we empirically examine the sensitivity of YouTube meta-level options and social dynamics. The key meta-level features that impact the read counts of a video embrace: first day read count, range of subscribers, distinction of the video thumbnail, Google hits, range of keywords, video category, title length, and range of higher-case letters in the title, respectively, and illustrate that these meta-level features will be used to estimate the recognition of a video. Also, optimizing the meta-level options once a video is posted increases the recognition of videos. Within the context of social dynamics, we discover that there's a causal relationship between views to a channel and the associated range of subscribers. Additionally, insights into the effects of scheduling and video playthrough in an exceedingly channel also are provided. Our findings offer a helpful understanding of user engagement in YouTube. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest Enhancing Binary Classification by Modeling Uncertain Boundary in Three-Way Decisions - 2017 Discovering Newsworthy Themes From Sequenced Data: A Step Towards Computational Journalism - 2017