applications, it is of high interest to discover potentially important knowledge by social media mining in this nascent field. Recently, more and more research efforts have been dedicated to the aforementioned challenges and opportunities. This special issue includes five papers focusing on different aspects of social media mining and knowledge discovery. With the popularity of social media applications, large amounts of social images associated with user tagging information are available, which can be leveraged to boost image retrieval performance. In “Sparse Semantic Metric Learning for Image Retrieval”, Liu et al. propose a sparse semantic metric learning method by discovering knowledge from these social media resources, and apply the learned metric to search relevant images for users. Different from traditional metric learning approaches that use similar or dissimilar constraints over a homogeneous visual space, the proposed method exploits heterogeneous information from the visual features and the tagging information of images, and formulates the learning problem as a sparse constrained one. Extensive experiments were conducted on a real-world dataset to validate the effectiveness of the proposed approach. In most cases, visual information can be regarded as an enhanced content of the textual document. In “Relative Image Similarity Learning with Contextual Information for Internet Crossmedia Retrieval”, to make image-to-image similarity being more consistent with document-to-document similarity, Jiang et al. propose a method to learn image similarities according to the relations of the accompanied textual documents. More specifically, instead of using the static quantitative relations, rank-based learning procedure by employing structural SVM is adopted, and the ranking structure is established by comparing the relative relations of textual information. The proposed method With the rapid advances of Internet and Web 2.0, social networking and social media become more and more popular in humans’ daily lives. The ubiquitous nature of webenabled devices, including desktops, laptops, tablets, and mobile phones, enables users to participate and interact with each other in various web communities, including photo and video sharing platforms, forums, newsgroups, blogs, micro-blogs, bookmarking services, and locationbased services. The rapidly evolving social networks provide a platform for communication, information sharing, and collaboration among friends, colleagues, alumnus, business partners, and many other social relations. To be accompanied by, increasingly rich and massive heterogeneous media data have been generated by the users, such as images, videos, audios, tweets, tags, categories, titles, geo-locations, comments, and viewer ratings, which offer an unprecedented opportunity for studying novel theories and technologies for social media analysis and mining. While researchers from multidisciplinary areas have proposed intelligent methods for processing social media data and employing such rich multi-modality data for various