Quality of Service (QoS) is crucial for public transport (PT) systems, as it directly impacts user satisfaction. Customer Satisfaction Surveys (CSS) measure how satisfied PT users are with various QoS attributes and gather additional data on socioeconomic characteristics and trip details. However, these surveys are often costly, time-consuming, and limited in sample size. To ensure reliable responses and shorter completion times, only a few QoS attributes are usually included. This research explores whether social media can measure customer satisfaction with PT services. We review previous studies and analyze how big data from social media can be used in PT systems. Social media can gauge PT customer satisfaction using techniques like semantic analysis, sentiment analysis, and social network analysis. Benefits include low-cost, real-time data collection and insights into user-specific needs and sentiments. Our analysis shows that social media can provide valuable insights for PT services but should not replace traditional CSS due to limitations, particularly in sample representativeness. We propose a framework to enrich PT QoS measurement surveys with big social media data. This framework outlines the technological and operational requirements for using social media in QoS assessment. It highlights its potential for improving our understanding of gaps in PT QoS data, monitoring specific PT QoS attributes, and enhancing the efficiency of QoS measurement programs.