Open AccessInvestigation of Benford’s Law with YouTube Social Media Statistics
Author(s) -
ChiWei Chen,
Shao-Yu Yu,
HsinYi Chen
Publication year - 2021
Publication title -
journal of student research
Language(s) - English
Resource type - Journals
ISSN - 2167-1907
DOI - 10.47611/jsrhs.v10i3.1677
Subject(s) - benford's law , upload , statistics , social media , econometrics , mathematics , computer science , law , political science , operating system
In this study, we used social media data to investigate Benford’s Law. In our experimental analysis, we used three control variables: Total Subscriptions, Total Views, and Video Uploads of Youtube channels to verify if the data is artificial and whether or not it fits Benford’s Law. We noticed how Total Subscriptions does not fit Benford’s Law for the top 5000 most-subscribed channels, and Total Views doesn’t fit for the top 5000 most-viewed channels. The reasons that cause this difference are further investigated in this paper. We also proposed a mathematical model to verify if other datasets fit Benford’s Law. After curve fitting the experimental data, results revealed that closer a and b values in our mathematical model indicate that a dataset fits Benford’s Law.