Social tags are known to be a valuable source of information for image retrieval and organization. However, contrary to the conventional document retrieval, rich tag frequency information in social sharing systems, such as Flickr, is not available, thus we cannot directly use the tag frequency (analogous to the term frequency in a document) to represent the relevance of tags. Many heuristic approaches have been proposed to address this problem, among which the well-known neighbor voting based approaches are the most effective methods. The basic assumption of these methods is that a tag is considered as relevant to the visual content of a target image if this tag is also used to annotate the visual neighbor images of the target image by lots of different users. The main limitation of these approaches is that they treat the voting power of each neighbor image either equally or simply based on its visual similarity. In this paper, we cast the social tag relevance learning problem as an adaptive teleportation random walk process on the voting graph.In particular, we model the relationships among images by constructing a voting graph, and then propose an adaptive teleportation random walk, in which a confidence factor is introduced to control the teleportation probability, on the voting graph. Through this process, direct and indirect relationships among images can be explored to cooperatively estimate the tag relevance. To quantify the performance of our approach, we compare it with state-of-the-art methods on two publicly available datasets (NUS-WIDE and MIR Flickr). The results indicate that our method achieves substantial performance gains on these datasets.

Wikipedia is widely considered the largest and most up-todate online encyclopedia, with its content being continuously maintained by a supporting community. In many cases, real-life events like new scientific findings, resignations, deaths, or catastrophes serve as triggers for collaborative editing of articles about affected entities such as persons or countries. In this paper, we conduct an in-depth analysis of event-related updates in Wikipedia by examining different indicators for events including language, meta annotations, and update bursts. We then study how these indicators can be employed for automatically detecting eventrelated updates. Our experiments on event extraction, clustering, and summarization show promising results towards generating entity-specific news tickers and timelines.

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