Understanding representativeness in cellular web logs at city scale is essential for web applications. Most of the existing work on cellular web analyses or applications is built upon data from a single network in a city, which may not be representative of the overall usage patterns since multiple cellular networks coexist in most cities in the world. In this paper, we conduct the first comprehensive investigation of multiple cellular networks in a city with a 100% user penetration rate. We study web usage pattern (e.g., internet access services) correlation and difference between diverse cellular networks in terms of spatial and temporal dimensions to quantify the representativeness of web usage from a single network in usage patterns of all users in the same city. Moreover, relying on three external datasets, we study the correlation between the representativeness and contextual factors (e.g., Point-of-Interest, population, and mobility) to explain the potential causalities for the representativeness difference. We found that contextual diversity is a key reason for representativeness difference, and representativeness has a significant impact on the performance of real-world applications. Based on the analysis results, we further design a correction model to address the bias of single cellphone networks and improve representativeness by 45.8%.