Authors - Yogini Prasanna Paturkar, Amol P. Bhagat, Priti A. Khodke Abstract - Social media platforms generate vast amounts of interaction data, offering valuable insights into user behavior, preferences, and trends. However, the sheer volume and velocity of this data pose significant challenges for real-time analysis and computational efficiency. This paper proposes a framework for random sampling of social media interaction data to address these challenges. By employing probabilistic sampling methods, we aim to reduce data volume while preserving key statistical properties and minimizing information loss. The proposed methodology leverages stratified and weighted random sampling techniques to ensure the representation of diverse user groups and interaction types. Applications of this approach include sentiment analysis, trend detection, and user behavior modeling. Preliminary experiments demonstrate that random sampling can achieve a significant reduction in computational overhead while maintaining analytical accuracy within an acceptable margin of error. This framework has the potential to enhance data processing pipelines in fields such as marketing, public opinion analysis, and event monitoring, enabling timely and resource-efficient decision-making.
