Forecasting Civil Unrest in South Africa Using Social Media Data: A Hybrid Machine Learning Approach

Civil unrest, encompassing protests and riots, is an increasing global concern, with incidents rising at an alarming rate, a trend that has been observed in South Africa over the years. This issue is particularly pronounced in today’s social media era, where platforms like ‘X’ (formerly Twitter) serve as powerful tools for mobilization. This raises the question: What factors drive civil unrest, and how can machine learning, using social media data, be employed to forecast such events? In response, this study had as objective to develop a hybrid machine learning model to forecast protest and riot events in South Africa using Twitter data. Employing the CRISP-DM methodology, data was collected from Twitter for the period between 2019 and 2024, resulting in 18,487 curated tweets, with associated ground truth data extracted from the ACLED database. Using this data, a hybrid model combining Bidirectional LSTM (Bi-LSTM) networks with eXtreme Gradient Boosting (XGBoost) for classification and regression tasks was developed to forecast civil unrest in South Africa. Additionally, SHapley Additive exPlanations (SHAP) were used for model explainability. The proposed model outperformed the base model, achieving an R-squared value of 33% for protests and 23% for riots in regression, along with classification accuracies of 92% for protests and 86.2% for riots. SHAP results indicated that the key predictors of unrest included sentiment-related features, tweet engagement features, regional factors, the day of the week, public holidays, and the topics being discussed. This study demonstrates the value of a hybrid model in forecasting civil unrest events and identifies key features that stakeholders can use to target their efforts more precisely in addressing civil unrest, ensuring resources are allocated where they are needed most. The study concludes with a discussion of valuable insights for stakeholders on how to leverage social media data to predict and mitigate civil unrest.