Social Media Analytics

• Background Social media platforms are complex ecosystems driven by human emotion, diverse demographics, and interconnected networks. To truly understand an online community, it is required look beneath the surface. This requires understanding how followers feel (Sentiment Analysis), who those followers are (Demographics), what topics are captivating them (Trend Tracking), and how they influence one another (Link Analysis). Combining these four vectors using AI is the key to unlocking true audience intelligence. • Description Participants will be challenged to design and build an AI-driven Social Media Analytics Framework that processes raw platform data to extract deep, actionable audience insights. The system must leverage advanced Artificial Intelligence and Machine Learning techniques to simultaneously infer follower sentiment, map audience demographics, identify top trending narratives, and perform link/network analysis to uncover how information and influence flow among followers. • Expected Solution AI solution must address the following five core components: A. Continuous Data Collection & Timeline Management: Design a multi-platform data ingestion pipeline capable of pulling live data, posts, user interactions, and comments. The architecture must support a structured, time-stamped historical database to map out the exact chronology of conversations. The pipeline platform requirements are categorized as follows: • Essentials (Must-Have): X (formerly Twitter) & Telegram. • Desirable (Good-to-Have): Instagram & Facebook. • Appreciable Additions: Reddit or YouTube (for extracting text-based context from video comments). B. Multi-Dimensional Sentiment Inference: Use Natural Language Processing (NLP) to detect nuanced emotions (e.g., sarcasm, anxiety, excitement, supportive, against etc.) within user posts and comment threads, mapping how these sentiments fluctuate along the established data timeline. C. Automated Demographic Profiling: Develop models to infer aggregate, anonymized follower demographics (such as age brackets, geographic distribution, language, and professional interests) based on public profile indicators, bio text, behavioral patterns etc. D. Real-Time Trend & Topic Detection: Automatically identify, rank, and predict rising trends, viral keywords, and shifting discussions as they emerge chronologically in the dataset. E. Link Analysis & Network Topology: Map the relationships among followers. Identify 'nodes of high influence' (key opinion leaders) and visualize how a trend or sentiment spreads from one user segment to another over time.