Abstract
Abstract
In today's digital landscape, integrating artificial intelligence (AI) and personalization is vital for understanding and engaging audiences on social media platforms. This thesis investigates scalable AI-driven methods to extract meaningful insights from social media content and transform them into actionable strategies for targeted marketing. The research begins with Twitter account classification, where AI models leverage metadata to differentiate individual and organizational accounts. Building upon this, the thesis introduces a framework for personalized paragraph generation aimed at creating compelling landing pages. Using advanced text generation techniques, AI systems produce coherent, relevant, and engaging content tailored to user needs. The study further explores targeted marketing strategies by applying text analysis methods to design and generate personalized landing pages informed by social media insights. This approach bridges the gap between audience understanding and practical marketing applications. To enable deeper content personalization, the thesis develops techniques for aspect-based sentiment analysis. These methods facilitate scalable and detailed sentiment evaluation, addressing challenges such as echo chambers and bias while improving content relevance and fairness. Additionally, the thesis proposes a programmable, stance-directed AI architecture that generates human-like, personalized social media content. This framework aligns AI outputs with user preferences and stances, fostering humanized and context-aware communication strategies. Through theoretical advancements and experimental validation, this work demonstrates how AI can bridge the gap between social media insights, user segmentation, and targeted marketing.