Leveraging Massive User Contributions for Knowledge Extraction

The collective intelligence that emerges from the collaboration, competition, and co-ordination among individuals in social networks has opened up new opportunities for knowledge extraction. Valuable knowledge is stored and often “hidden” in massive user contributions, challenging researchers to find methods for leveraging these contributions and unfold this knowledge. In this chapter we investigate the problem of knowledge extraction from social media. We provide background information for knowledge extraction methods that operate on social media, and present three methods that use Flickr data to extract different types of knowledge namely, the community structure of tag-networks, the emerging trends and events in users tag activity, and the associations between image regions and tags in user tagged images. Our evaluation results show that despite the noise existing in massive user contributions, efficient methods can be developed to mine the semantics emerging from these data and facilitate knowledge extraction.

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