Abstract
As a new information sharing platform, microblog has got explosive growth in recent years and has become an important source for public opinion mining. A variety of information like the reviews of brands/products or the trends of events can be socially sensed from such kind of data. However, it is still a challenging task to search relevant microblogs as the user generated content tends to be mixed with noise. Besides short text, image is getting popular in microblogs due to its power in visual information conveying. In this paper, we leverage textual and visual cues integratedly and propose a general re-ranking approach for microblog retrieval via multi-graph semi-supervised learning. We argue that the different types of information in microblogs correspond to different relationships among microblogs and each type of the relationship can be represented as a similarity graph. We then integrate different graphs into a unified framework and solve them simultaneously for microblog re-ranking. Extensive experiments on a recently published Brand-Social-Net dataset showed the effectiveness of the proposed method and marginal improvements have been achieved in accuracy as compared to the single graph model based method.
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