Social media analytics requires processing high-volume, noisy text data in near real-time. Data collection: Twitter/X API v2 filtered stream for real-time keywords, Reddit pushshift for forum discussions, YouTube Data API v3 for comments. Processing pipeline: ingestion (Kafka) -> text cleaning (remove URLs, emojis to text, normalize hashtags) -> NLP processing -> storage (Elasticsearch for search, ClickHouse for analytics). Brand sentiment tracking: fine-tuned FinBERT or RoBERTa for aspect-based sentiment. Track sentiment trend lines. Alert on sudden sentiment shifts (3-sigma from rolling baseline). Topic modeling: BERTopic uses BERT sentence embeddings + HDBSCAN clustering to discover emerging topics without predefined categories. Better than LDA for short social media text. Track topic popularity over time to identify emerging trends. Influencer analysis: network analysis identifying accounts with high reach AND engagement (not just followers). Weighted influence score combining followers, engagement rate, domain relevance. Bot detection: behavioral features (posting frequency, account age, follower/following ratio, content originality) classify automated accounts. Crisis detection: multi-signal early warning system: sentiment drop + volume spike + negative keyword cluster + influencer amplification = crisis alert. Playbook integration: auto-notify PR team, draft initial response. Competitive intelligence: track competitor mentions, product launches, customer complaints, feature requests.