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AI Ensemble Framework for OSINT-Based Social Media Crime Prevention in Kenya

Identification

Clé publique

doi:10.71274/ijpp.v14i7.760

Auteurs

Alfred Ngala · Shem Mbandu · Anthony Mile

Année déclarée

2026

Langue déclarée

en

Type source

article

Première détection

2026-11-09T19:04:47.784083+00:00

Dernière observation des métadonnées

2026-11-09T19:04:47.784083+00:00

Dernière vérification HTTP

Non effectuée

Institutions déclarées par la source

The Co-operative University of Kenya

Pays d’affiliation déclarés

KE

Résumé documentaire — langue originale

Criminal networks increasingly exploit social media to organize, recruit, incite, and coordinate unlawful activities, whereas law-enforcement monitoring in Kenya remains largely manual and English-centric. This study aimed to design, implement, and rigorously evaluate an artificial intelligence (AI) ensemble framework for open-source intelligence (OSINT) classification of crime- and threat-relevant content in Kenya's trilingual (English, Kiswahili, and Sheng) and code-switched digital environment. A corpus of 6,212 documents was assembled from six sources: Twitter/X, Facebook, Instagram, TikTok, and two Sheng-language corpora. The corpus was weakly labelled using a 214-term trilingual crime-and-threat lexicon. Label noise was quantified against a manually annotated stratified validation sample (n = 320), yielding Cohen's κ = 0.350, silver-label precision of 0.525, and recall of 0.750. Combined word- and character-level term frequency-inverse document frequency (TF-IDF) features comprising 25,831 dimensions were used to train a hyperparameter-tuned, weighted soft-voting ensemble consisting of multinomial naive Bayes, a calibrated linear support vector machine, random forest, logistic regression, and XGBoost. The decision threshold was calibrated using cross-validated out-of-fold probabilities. The optimized ensemble achieved 98.3% accuracy, 80.6% precision, 62.5% recall, 70.4% F1-score, 0.907 ROC-AUC, 0.724 PR-AUC, 0.702 Matthews correlation coefficient (MCC), and a 0.50% false-positive rate. The ensemble outperformed the best single learner and a frozen multilingual transformer baseline (F1 = 26.2%). Ablation analysis demonstrated the contribution of the Sheng-normalization layer, while comparison of class-imbalance strategies showed that cost-sensitive learning combined with threshold calibration produced the strongest overall performance compared with SMOTE-based resampling. The findings demonstrate that linguistically inclusive classical machine-learning ensembles can provide an accurate, auditable, and computationally deployable approach to proactive social-media crime prevention in low-resource, code-switched settings. The study also derives policy implications concerning threshold-calibrated triage, expert annotation, data-protection impact assessments, and safeguards against dual-use misuse.

Identifiants

OPENALEX

W7221567087

DOI

10.71274/ijpp.v14i7.760

Repères documentaires

Famille documentaire

Doctrine

Types déclarés par les sources

article

Juridictions étudiées — sources explicites

KE

Entités naturelles mentionnées

Forêt

Les mentions d’entités ne constituent pas une preuve de reconnaissance juridique. La juridiction étudiée est distincte du pays d’affiliation des auteurs.

Sources

OpenAlex · Document 1 · Document 2

• DOI · https://doi.org/10.71274/ijpp.v14i7.760
• OpenAlex · https://openalex.org/W7221567087

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• openalex:w2148143831 · CITES
• openalex:w2202488771 · CITES
• openalex:w2793955328 · CITES
• openalex:w28412257 · CITES
• openalex:w2912934387 · CITES
• openalex:w2981731882 · CITES
• openalex:w3194296128 · CITES
• openalex:w4407094964 · CITES
• openalex:w4409514842 · CITES
• openalex:w4417422183 · CITES

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Statut documentaire

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Bibliothèque

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