To contain corruption, the digitalization of anticorruption initiatives is gaining significant attention. This paper explores the role of ICT and social media in combating corruption,focusing specifically on Tunisia. It provides an overview of various platforms and evaluates their usage and effectiveness in promoting citizen engagement. Based on a survey of citizens, the study reveals significant challenges in user engagement and platform effectiveness. The findings highlight the need for improved visibility, feedback mechanisms, and strategic integration to enhance their impact on corruption reduction.

This study investigates the integration of Process Mining (PM) techniques with Machine Learning (ML) algorithms to detect corrupt activities in business processes. PM has gained significant attention for its ability to analyze event logs and uncover inefficiencies, deviations, non-compliance, and regulatory breaches in real processes. However, its application in detecting corruption, fraud, or other unethical practices in organizational processes remains underexplored. Through a structured analysis of existing research, we examine how PM and ML have been applied in related areas such as fraud detection, and evaluate their relevance to addressing corruption-specific challenges. This paper advocates for the use of PM methods combined with ML techniques to improve corruption detection systems, outlining the key challenges and gaps in current approaches. By synthesizing insights from the literature and evaluating use-case applicability, this work provides a foundation for future research into corruption-aware process analytics.