The technological advances of the last few years have progressively enabled online content sharing at the global level. The dynamics of content popularity are of great interest from both commercial and research perspectives. Apart from the unbelievable amount of data to be processed, the key issue is to understand how digital content is accessed, diffused, and shared. Accomplishing this requires basic and consistent statistics about content usage, which most popular platforms (such as YouTube) either do not provide or only make available to content owners.

This paper proposes a research tool (YOUStatAnalyzer) that works on the current version of YouTube to analyze the complex relationship between social interactions and the dynamic processes governing popularity. It applies web-scraping techniques to allow researchers to quickly build large datasets for analyzing the evolution and the dynamics of content popularity. It uses a flexible query strategy that can target single pieces of content as well as apply different search criteria, including keywords and categories.

Though the approach is not completely novel, the tool is interesting and might be successfully used in several research initiatives and studies. It provides consistent support in terms of analysis capability. The major limitation of the paper, probably due to its brevity, is the lack of discussion of issues related to big data processing.