A Systematic Review of Social Media Analytics Pipelines: Verification, Optimization, and Scalable Computing Perspectives

Abstract

Social media platforms generate vast volumes of dynamic, heterogeneous, and real-time data, making them valuable for analytics in domains such as marketing, healthcare, disaster management, and public policy. To extract meaningful insights, organizations rely on social media analytics pipelines that integrate stages including data acquisition, preprocessing, storage, analysis, and visualization. However, the growing scale and complexity of such data introduce challenges related to verification, optimization, and scalability. This paper presents a systematic review of 30 studies published between 2018 and 2023, examining key architectural components, processing frameworks, and emerging technologies for efficient pipeline design. It highlights verification mechanisms such as misinformation detection, fact-checking, and trust-aware systems to ensure data reliability. The review also explores optimization strategies, including data reduction, indexing, hybrid batch-stream processing, and AI-driven tuning, which improve efficiency and reduce latency. Furthermore, it analyzes scalable computing approaches like distributed systems, cloud and edge computing, and microservices architectures. While tools such as Apache Spark and Hadoop enhance performance, challenges remain in real-time verification, energy efficiency, data privacy, and integration of multi-modal data, indicating important directions for future research.