Data-driven Analysis and Prediction of Human Rights Violations Against Human Rights Defenders: A Case Study of Eastern Africa

Abstract

Despite the growing availability of big data and machine learning, human rights monitoring in the region remains largely dependent on retrospective reports, eyewitness testimonies, and qualitative assessments, which lack the ability to anticipate future violations. The absence of real- time data processing and predictive analytics limits the ability of policymakers and advocacy groups to implement proactive intervention strategies. As a result, human rights organizations often respond reactively, only after violations occur, rather than deploying preemptive measures to protect HRDs. In this research, a quantitative research design was adopted, utilising a cross-sectional approach to analyse patterns in human rights violations. Data was collected from recognized human rights organisations, human rights databases, and global news agencies. The research employed descriptive analytics to identify trends, K-Means clustering to categorize high-risk regions, and predictive modeling to forecast future violations. Seasonal Autoregressive Integrated Moving Average (SARIMA) was used to model long-term seasonal trends, while Recurrent Neural Networks (RNN) captured short-term fluctuations and nonlinear patterns in the data. The Predictive Human Rights Violations Model (PHRVM) emerged as the most effective, balancing structural seasonality and real-time variations, resulting in higher accuracy and improved forecasting reliability compared to individual models. The findings revealed that human rights violations followed distinct temporal and geo- graphic trends, peaking around election periods, protest seasons, and government crackdowns. While the PHRVM outperformed other forecasting methods during training (MAE : 0.081, RMSE : 0.087), testing revealed a slight increase in prediction error, with MAE rising to 0.684 and RMSE increasing to 1.109. A paired t-test confirmed that the model significantly outperformed a naïve baseline forecast (p < 0.05), validating its predictive capability. This research concluded that human rights violations follow recognizable patterns, making it possible to anticipate high-risk periods and optimize protection efforts for HRDs. This helps policymakers, and advocacy groups to anticipate risks and implement preventive measures before violations escalate. The PHRVM’s success shows the potential of AI-driven forecasting in social science research, offering a more systematic approach to tracking civic space restric- tions. However, for predictive models to be more effective in real-world applications, further refinement is needed, including the integration of real-time data sources such as social media monitoring, remote sensing technologies, and expanded human rights reporting networks. Strengthening these capabilities will enhance model accuracy, responsiveness, and impact, ensuring that human rights organisations can move from reactive responses to preventative protection strategies.