Do Pandemic Related Datasets with High Artificial Control Still Follow the Benford’s Law?

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Benford’s Law (BL) is being used extensively in research for several purposes including for the detection of potential manipulations of the data to detect fraud since datasets tend to follow the Benford’s distribution when they occur naturally without artificial control. The COVID-19 pandemic has heavily impacted business and non-business-related activities. Datasets related to the pandemic are being used in many different analyses to arrive at different conclusions. However, the credibility of the results and conclusions depend heavily on the accuracy of the datasets. The COVID-19 related datasets are obvious results of intense human intervention and artificial control efforts; therefore, the question arises as to whether Benford’s analysis can still be used to detect anomalous datasets among them? This research uses several publicly available datasets and uses predictive analytics to perform the Benford’s analysis. The applicability of BL is first verified using a regular dataset occurred prior to the pandemic, and then applied on COVID-19 related datasets to test the research hypothesis. The results demonstrate that even the datasets with sufficiently large sample sizes with considerable human intervention and artificial control follow the Benford’s distribution and that Benford’s analysis can still detect the anomalous datasets. The findings are anticipated to be useful for the data analysts and researchers and adds to the current literature gap. This paper may also serve as a class case study for the academia teaching data analytics.

Original languageEnglish (US)
Title of host publicationProceedings - 4th European Rome Conference 2021
EditorsMario Fargnoli, Mara Lombardi, Massimo Tronci, Patrick Dallasega, Matteo Mario Savino, Francesco Costantino, Giulio Di Gravio, Riccardo Patriarca
PublisherIEOM Society
Pages1143-1152
Number of pages10
ISBN (Print)9781792361272
StatePublished - 2021
Event4th European International Conference on Industrial Engineering and Operations Management, IEOM 2021 - Virtual, Online
Duration: Aug 2 2021Aug 5 2021

Publication series

NameProceedings of the International Conference on Industrial Engineering and Operations Management
ISSN (Electronic)2169-8767

Conference

Conference4th European International Conference on Industrial Engineering and Operations Management, IEOM 2021
CityVirtual, Online
Period8/2/218/5/21

All Science Journal Classification (ASJC) codes

  • Strategy and Management
  • Management Science and Operations Research
  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering

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