The website fingerprinting (or inter-domain WSF), enhanced by various machine learning techniques, has shown its power to identify websites a user has visited. To our best knowledge, a finer-grained problem of web page fingerprinting (or intra-domain WPF) has not been systematically studied by our research community. The WPF attackers, such as government agencies enforcing Internet censorship, are keen to identify the particular web pages (e.g., a political dissident's social media page) visited by the target user. In this work, we investigate the intra-domain WPF among social media websites, against the realistic on-path passive attack scenario. We reveal that delivering large-size data such as images and videos via Content Delivery Networks (CDNs), which is a common practice in social media websites, makes intra-domain WPF highly feasible. The network traffic generated during rendering a social media page exhibits temporal and volumetric patterns that are sufficiently recognizable by machine learning algorithms. We characterize such patterns as CDN bursts, and use features extracted from them to empower classification algorithms to achieve a high classification accuracy (96%) and a low false positive rate (0.02%).