Artificial Intelligence in Political Communication: Algorithmic Governance, Sentiment Analysis, and Ethical Challenges in Digital Democracy

Abstract

Artificial Intelligence (AI) is disrupting the global field of political communication research by automating the intensive process of analyzing massive yet ever-growing digital discourses spanning social media, speeches, and news outlets. In a wide ranging meta-analysis, we explore AI's diverse uses from sentiment analysis to predictive modeling and even deepfake detection, reviewing the seminal literature, delineating methodologies such as NLP pipelines, presenting key empirical findings from case studies around the world, and addressing some of the most pressing ethical issues. By integrating recent developments in natural language processing (NLP), machine learning (ML), and large language models (LLMs) through 2025, it advocates for hybrid human-AI protocols that enhance academic rigour, guarantee interpretive subtleties, and reduce endemic biases such as urban scientific data patterns and algorithmic obscurity. Some of the important applications that highlight the empirical capability of AI: The RoBERTa model on Twitter data reaches 88% in the detection of sarcasm within samples of political tweets, while the BERT-LDA hybrid uncovered that, in the data on the U.S. 2024 election, sentiment is positively dominated with a robustness estimate of 54%. Voter turnout is predicted with margins less than 2% by predictive tools, and the personalization gap that the GPT-4 impact brings to the campaign is between 28–35%, as demonstrated during the outreach of 100 million voters of India to Lok Sabha with the Bhashini model. Methodologies highlight reproducible pipelines (API scraping, spaCy preprocessing, and Hugging Face fine-tuning) assessed through F1-scores (0.82 average) and Krippendorff's alpha (>0.75). Legislative GPT-3 pilots (+18% trust gains in the EU) and Brazil's WhatsApp bots (12% vote shifts) are global cases demonstrating transformative impacts. Not just data leaks but ethical urgency: errors in sentiment of 20% rural voters, 25% deception by deepfake, and 15% of studies are on Global South, threaten democratic deliberation. These gaps are filled with hybrid frameworks, and with SHAP explainability, and with datasets of various sizes and types, thus promoting equitable innovation. This work, tailored for media scholars, is part of narrative pedagogy in journalism education, advocating for visible, inclusive and ethical forms of AI the only kind of AI stewardship that will preserve the public sphere from threatening algorithmic curations.