Building occupancy, one of the most important consequences of occupant behaviors, is a driving influencer for building energy consumption and has been receiving increasing attention in the building energy modeling community. With the vast development of information technologies in the era of the internet-of-things, occupant sensing and data acquisition are not limited to a single node or traditional approaches. The prevalence of social networks provides a myriad of publically available social media data that might contain occupancy information in the space for a given time. In this paper, we explore two approaches to extract the typical occupancy schedules for the input to the building energy simulation based on the data from social networks. The first approach uses text classification algorithms to identify whether people are present in the space where they are posting on social media. On top of that, the typical building occupancy schedules are extracted with assumed people counting rules. The second approach utilizes the processed Global Positioning System (GPS) tracking data provided by social networking service companies such as Facebook and Google Maps. Web scraping techniques are used to obtain and post-process the raw data to extract the typical building occupancy schedules. The results show that the extracted building occupancy schedules from different data sources (Twitter, Facebook, and Google Maps) share a similar trend but are slightly distinct from each other and hence may require further validation and corrections. To further demonstrate the application of the extracted Typical Occupancy Schedules from Social Media (TOSSM), data-driven models for predicting hourly energy usage prediction of a university museum are developed with the integration of TOSSM. The results indicate that the incorporation of TOSSM could improve the hourly energy usage prediction accuracy to a small extent regarding the four adopted evaluation metrics for this museum building.