The share of videos in the internet traffic has been growing, therefore understanding how videos capture attention on a global scale is also of growing importance. Most current research focus on modeling the number of views, but we argue that video engagement, or time spent watching is a more appropriate measure for resource allocation problems in attention, networking, and promotion activities. In this paper, we present a first large-scale measurement of video-level aggregate engagement from publicly available data streams, on a collection of 5.3 million YouTube videos published over two months in 2016. We study a set of metrics including time and the average percentage of a video watched. We define a new metric, relative engagement, that is calibrated against video properties and strongly correlate with recognized notions of quality. Moreover, we find that engagement measures of a video are stable over time, thus separating the concerns for modeling engagement and those for popularity -- the latter is known to be unstable over time and driven by external promotions. We also find engagement metrics predictable from a cold-start setup, having most of its variance explained by video context, topics and channel information -- R2=0.77. Our observations imply several prospective uses of engagement metrics -- choosing engaging topics for video production, or promoting engaging videos in recommender systems.
Measuring the co-evolution of online engagement with (mis)information and its visibility at scale
University of Warwick（华威大学）；The Alan Turing Institute（艾伦·图灵研究所）；Institute of Cognitive Sciences and Technologies, National Research Council of Italy（意大利国家研究委员会认知科学与技术研究所）；Institute for Futures Studies（未来研究所）；Linköping University（林雪平大学）；University of Padua（帕多瓦大学）；Padua Center for Network Medicine（帕多瓦网络医学中心）；Istituto Nazionale di Fisica Nucleare, Sez. Padova（意大利国家核物理研究所帕多瓦分部）
Machines in the Crowd? Measuring the Footprint of Machine-Generated Text on Reddit
DIMES Dept., University of Calabria Italy；IT University of Copenhagen\ Centre for AI Denmark；DIMES Dept., University of Calabria；IT University of Copenhagen\ Centre for AI
