Click versus share: A feature-driven study of micro-video popularity and virality in social media

Abstract

Micro-video has recently become an important form of user generated contents in the social media of microblogging. It is propagated by sharing and reaches the other users through being clicked and watched. Besides the traditional popular-ity metric for a micro-video such as click (or view) count, share count can indicate its virality in social domain. Un-derstanding the differences between clicking and sharing be-haviors is fundamental when evaluating the actual inu-ence of micro-videos in social media. However, since that click data is usually not public available, above question has not been investigated in most studies. Thanks to a mas-sive set of anonymized data from a major operator covering the whole China, we jointly study both clicking and shar-ing behaviors of over 10,000 micro-videos in Sina Weibo, the largest microblogging service and micro-video platform in China. Having extracted a rich set of features covering micro-video publishers, description texts and those shared users, we are able to identify the most inuential features for click and share. From our studies, we observe that publisher-related features (post and followee counts) as well as the video duration have more impact on click, while video-description-related features including topical features and emoticon count are more correlated to share. Impacted by different features, the received clicks and shares of a micro-video may differ a lot from each other. Based on above obser-vations, we build a prediction model for existing deviations among these two metrics, which can aid the development of a more effective and attractive micro-video platform.