Agyemang Edmund F, Nortey Ezekiel N N, Minkah Richard, Asah-Asante Kwame
Department of Statistics and Actuarial Science, College of Basic and Applied Sciences, University of Ghana, Ghana.
Department of Computer Science, Ashesi University, No.1 University Avenue, Berekuso, Accra, Eastern Region, Ghana.
Heliyon. 2023 Jul 18;9(8):e18276. doi: 10.1016/j.heliyon.2023.e18276. eCollection 2023 Aug.
Many allegations have been levelled against the electoral process of many countries across the world by most opposition leaders, especially when they lose a presidential election e.g. Ghana in 2012 and 2020. Therefore, the need to apply election forensic techniques to the certified election results data of valid votes count to statistically verify if some suspected or possible anomalies and irregularities exist in the voting pattern. This paper seeks to provide a comprehensive review of election forensics techniques and make a comparative analysis of Benford's Second-order test of conformity (using the first two digits) and Hartigans' dip test of unimodality to examine the existence of possible anomalies and irregularities in the 2012 and 2020 presidential elections held in Ghana. The findings of the two tests suggest that the electoral process produced possible anomalous data in the 2012 presidential election results (with an overall 16.67% suspected anomalies), whilst possible non-anomalous data was produced in the 2020 presidential election results (with an overall 0% suspected anomaly) of valid votes count. Therefore, the study recommends that for better statistical data analysis on election anomaly detection, Benford's test of conformity and Hartigans' dip test of unimodality should serve as baseline tests (initial screening tools), highlighting areas that may require further investigation or more rigorous analysis and progressively dig deeper into the application of finite mixture fraud models and machine learning techniques. In spite of the promising results Benford's Law, dip test, machine learning algorithms, and network analysis have produced in detecting irregularities in election data, real-world applications remain challenging, particularly when dealing with complex and evolving forms of fraud. Therefore, there is the need for continuous research and innovation to improve the accuracy and effectiveness of these methods and promote transparency and accountability in democratic societies.
许多反对派领导人对世界上许多国家的选举过程提出了诸多指控，尤其是当他们在总统选举中失利时，比如2012年和2020年的加纳。因此，有必要将选举取证技术应用于有效选票计数的经认证的选举结果数据，以统计核实投票模式中是否存在一些可疑或可能的异常及违规情况。本文旨在全面回顾选举取证技术，并对本福德二阶一致性检验（使用前两位数字）和哈蒂根单峰性检验进行比较分析，以检验2012年和2020年在加纳举行的总统选举中是否存在可能的异常和违规情况。这两项检验的结果表明，选举过程在2012年总统选举结果中产生了可能的异常数据（总体可疑异常率为16.67%），而在2020年总统选举有效选票计数结果中产生了可能的非异常数据（总体可疑异常率为0%）。因此，该研究建议，为了更好地对选举异常检测进行统计数据分析，本福德一致性检验和哈蒂根单峰性检验应作为基线检验（初始筛选工具），突出可能需要进一步调查或更严格分析的领域，并逐步深入研究有限混合欺诈模型和机器学习技术的应用。尽管本福德定律、单峰性检验、机器学习算法和网络分析在检测选举数据违规方面取得了有前景的结果，但实际应用仍然具有挑战性，尤其是在处理复杂且不断演变的欺诈形式时。因此，需要持续进行研究和创新，以提高这些方法的准确性和有效性，并促进民主社会的透明度和问责制。
