Quantum Enhanced Multimodal Analysis of Political Polarization on TikTok: A Case Study of Ethiopia's Digital Public Sphere

Ethiopia's political landscape, characterized by ethnic federalism and the National Dialogue process, faces escalating polarization amplified by TikTok's algorithmically curated content. Traditional machine learning approaches struggle to capture the quantum-like dynamics of political discourse, superposition of identities, entanglement of ethnic and ideological factors, and context-dependent meaning. This study introduces the first quantum-enhanced multimodal framework for analyzing political discourse on Ethiopian TikTok, integrating quantum entanglement-driven fake news detection (Q-ALIGNer), quantum LSTM sentiment analysis, and quantum frequency-based opinion shift modeling (OpinionXf). We developed a hybrid quantum-classical pipeline processing 50,000 Amharic, Oromo, Tigrinya, and English TikTok videos. Q-ALIGNer encodes text, video, and audio modalities as quantum states with entanglement-based fusion. Quantum LSTM captures temporal sentiment evolution, while OpinionXf models opinion shifts using frequency-domain transformations. Performance was evaluated against classical baselines using 10-fold cross-validation. Q-ALIGNER achieved 92.5% accuracy, outperforming classical models by 8.2–13.9%, with only 4.6% accuracy drop under adversarial attack versus 11.9% for classical models. Quantum LSTM achieved 89.7% accuracy with 15.2% MAE reduction over AfriBERTa. Sarcasm detection improved by 8.4% and coded political language by 9.1%. OpinionXf achieved 85.7% precision and 100% recall for 72-hour early warning, detecting shifts 3–6 days before classical models. Ablation study revealed quantum layers contributed 46.3% and entanglement 53.7% of total performance gain. Entanglement-based similarity maps revealed three political actor clusters with intra-cluster entanglement 0.85–0.92 versus inter-cluster 0.65–0.72. Quantum-enhanced frameworks significantly improve detection of misinformation, sentiment polarization, and opinion shifts in Ethiopian political discourse, enabling proactive early warning systems. Deploy the 3-layer quantum model with all-to-all entanglement for Ethiopia's National Dialogue Commission, prioritizing high-persuadability local issues while approaching identity-based topics through deliberative processes.