Abstract
In this chapter we discuss methods for efficiently modeling the diverse information carried by social media. The problem is viewed as a multi-modal analysis process where specialized techniques are used to overcome the obstacles arising from the heterogeneity of data. Focusing at the optimal combination of low-level features (i.e., early fusion), we present a bio-inspired algorithm for feature selection that weights the features based on their appropriateness to represent a resource. Under the same objective of optimal feature combination we also examine the use of pLSA-based aspect models, as the means to define a latent semantic space where heterogeneous types of information can be effectively combined. Tagged images taken from social sites have been used in the characteristic scenarios of image clustering and retrieval, to demonstrate the benefits of multi-modal analysis in social media.
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Notes
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As many users find the tagging process tedious, the scenario that most photos in each group have been assigned only one tag is not far from reality.
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For Flickr resources and metadata download the Flickr API along with the utility wget were used.
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Acknowledgements
This work was sponsored by the European Commission as part of the Information Society Technologies (IST) programme under grant agreement no215453—WeKnowIt and the contract FP7-248984 GLOCAL.
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Nikolopoulos, S., Giannakidou, E., Kompatsiaris, I., Patras, I., Vakali, A. (2011). Combining Multi-modal Features for Social Media Analysis. In: Hoi, S., Luo, J., Boll, S., Xu, D., Jin, R., King, I. (eds) Social Media Modeling and Computing. Springer, London. https://doi.org/10.1007/978-0-85729-436-4_4
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