The frequency of power distribution networks in a power grid is called electrical network frequency (ENF). Because it provides the spatio-temporal changes of the power grid in a particular location, ENF is used in many application domains including the prediction of grid instability and blackouts, detection of system breakup, and even digital forensics. In order to build high performing applications and systems, it is necessary to capture a large-scale nationwide or worldwide ENF map. Consequently, many studies have been conducted on the distribution of specialized physical devices that capture the ENF signals. However, this approach is not practical because it requires significant effort from design to setup, moreover, it has a limitation in its efficiency to monitor and stably retain the collection equipment distributed throughout the world. Furthermore, this approach requires a significant budget. In this paper, we proposed a novel approach to constructing the worldwide ENF map by analyzing streaming data obtained by online multimedia services, such as "Youtube", "Earthcam", and "Ustream" instead of expensive specialized hardware. However, extracting accurate ENF from the streaming data is not a straightforward process because multimedia has its own noise and uncertainty. By applying several signal processing techniques, we can reduce noise and uncertainty, and improve the quality of the restored ENF. For the evaluation of this process, we compared the performance between the ENF signals restored by our proposed approach and collected by the frequency disturbance recorder (FDR) from FNET/GridEye. The experimental results show that our proposed approach outperforms in stable acquisition and management of the ENF signals compared to the conventional approach.