Detecting Academic Fraud Using Benford Law : The Case of Professor James Hunton

We investigate whether Benford's Law can be used to detect retracted academic papers that have employed fraudulent/manipulated data from other academic papers that have not been retracted. We use the case of Professor James Hunton who had 37 of his articles retracted because there were grave concerns that they contained mis-stated or fabricated datasets. We construct several Benford conformity measures, based on first significant digits contained in the articles, to determine whether Hunton's retracted papers differ significantly from a control group of non-retracted articles by competing authors. Our results clearly indicate that Hunton's retracted papers significantly deviate from Benford Law, relative to the control group of papers. In additional analysis we also find these results are generalisable to other authors with retracted papers. Our findings suggest that potentially both co-authors and journals could consider implementing a data analytical tool which employs Benford Law to highlight potential ‘red flag' papers, with a view to decreasing the risk of fraudulent activity and thereby enhancing the credibility of academic papers and journals