Doing data science with platforms crumbs: an investigation into fakes views on YouTube

TL;DR

This study investigates the prevalence and impact of fake views on YouTube, revealing that most are corrected late and can artificially inflate video popularity, with significant implications for online information quality.

Contribution

The paper provides the first large-scale analysis of fake view removal on YouTube, highlighting the extent, timing, and potential effects of fake views on content visibility.

Findings

Most fake views are corrected late in video lifespan

Fake views significantly influence final view counts

YouTube lacks transparency on fake view data

Abstract

This paper contributes to the ongoing discussions on the scholarly access to social media data, discussing a case where this access is barred despite its value for understanding and countering online disinformation and despite the absence of privacy or copyright issues. Our study concerns YouTube's engagement metrics and, more specifically, the way in which the platform removes "fake views" (i.e., views considered as artificial or illegitimate by the platform). Working with one and a half year of data extracted from a thousand French YouTube channels, we show the massive extent of this phenomenon, which concerns the large majority of the channels and more than half the videos in our corpus. Our analysis indicates that most fakes news are corrected relatively late in the life of the videos and that the final view counts of the videos are not independent from the fake views they received. We discuss the potential harm that delays in corrections could produce in content diffusion: by inflating views counts, illegitimate views could make a video appear more popular than it is and unwarrantedly encourage its human and algorithmic recommendation. Unfortunately, we cannot offer a definitive assessment of this phenomenon, because YouTube provides no information on fake views in its API or interface. This paper is, therefore, also a call for greater transparency by YouTube and other online platforms about information that can have crucial implications for the quality of online public debate.