Harnessing Machine Learning to Forecast Social Media Advocacy Success

Section: Articles
Issue
Vol. 20 No. 1 (2026): Vol. 20 No. 1 (2026): Volume 20 Issue 1
Pages
53-63

Abstract

Social media has modernized advocacy by enabling individuals to mobilize, spread opinions, and champion justice globally. Accurate prediction of social media advocacy campaign success can play a significant role in promoting social and political change. Motivated by the recent #JusticeForNavalAbbas advocacy campaign in Nigeria, this study aims to predict the success rate of posts related to the campaign. By examining how social media platforms including Facebook, TikTok, Instagram, and X (formerly known as Twitter) influence public opinion, this paper investigates how these platforms attract engagement, amplify the voices of the marginalized, and encourage collective action. The present research leverages five machine learning techniques, including Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and XGBoost (XGB). The dataset was preprocessed and split into training and testing subsets comprising 75% and 25% of the data, respectively. Model performance was evaluated using Accuracy, Precision, Recall, F1-Score, and Area Under the ROC Curve (AUC). RF and XGB demonstrated exceptional performance in predicting social media post success, achieving accuracies of 98% and 97%, respectively. The findings highlight the significance of social media platforms in amplifying advocacy campaigns at both local and global levels.

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Authors

Muhammad Bala

Abdulmalik Ahmad Lawan

Aliko Dangote University of Science and Technology

Abdo Sulaiman Abdi

Information Technology Department, Duhok Technical Institute, Duhok Polytechnic University, Duhok, Iraq

Identifiers

DOI: 10.33899/rjcsm.v20i1.60662

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Harnessing Machine Learning to Forecast Social Media Advocacy Success. (2026). AL-Rafidain Journal of Computer Sciences and Mathematics, 20(1), 53-63. https://doi.org/10.33899/rjcsm.v20i1.60662
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How to Cite

Harnessing Machine Learning to Forecast Social Media Advocacy Success. (2026). AL-Rafidain Journal of Computer Sciences and Mathematics, 20(1), 53-63. https://doi.org/10.33899/rjcsm.v20i1.60662