Latent Geospatial Semantics of Social Media

  • Authors:
  • Sergej Sizov

  • Affiliations:
  • University of Koblenz-Landau, Institute WeST

  • Venue:
  • ACM Transactions on Intelligent Systems and Technology (TIST)
  • Year:
  • 2012

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Abstract

Multimodal understanding of shared content is an important success factor for many Web 2.0 applications and platforms. This article addresses the fundamental question of geo-spatial awareness in social media applications. In this context, we introduce an approach for improved characterization of social media by combining text features (e.g., tags as a prominent example of short, unstructured text labels) with spatial knowledge (e.g., geotags, coordinates of images, and videos). Our model-based framework GeoFolk combines these two aspects in order to construct better algorithms for content management, retrieval, and sharing. We demonstrate in systematic studies the benefits of this combination for a broad spectrum of scenarios related to social media: recommender systems, automatic content organization and filtering, and event detection. Furthermore, we establish a simple and technically sound model that can be seen as a reference baseline for future research in the field of geotagged social media.