Unraveling the Taste Fabric of Social Networks

Unraveling the Taste Fabric of Social Networks

Hugo Liu (The Media Laboratory, USA), Pattie Maes (The Media Laboratory, USA) and Glorianna Davenport (The Media Laboratory, USA)
Copyright: © 2009 |Pages: 27
DOI: 10.4018/978-1-60566-088-2.ch015
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Abstract

Popular online social networks such as Friendster and MySpace do more than simply reveal the superficial structure of social connectedness — the rich meanings bottled within social network profiles themselves imply deeper patterns of culture and taste. If these latent semantic fabrics of taste could be harvested formally, the resultant resource would afford completely novel ways for representing and reasoning about web users and people in general. This paper narrates the theory and technique of such a feat — the natural language text of 100,000 social network profiles were captured, mapped into a diverse ontology of music, books, films, foods, etc., and machine learning was applied to infer a semantic fabric of taste. Taste fabrics bring us closer to improvisational manipulations of meaning, and afford us at least three semantic functions — the creation of semantically flexible user representations, crossdomain taste-based recommendation, and the computation of taste-similarity between people — whose use cases are demonstrated within the context of three applications — the InterestMap, Ambient Semantics, and IdentityMirror. Finally, we evaluate the quality of the taste fabrics, and distill from this research reusable methodologies and techniques of consequence to the semantic mining and Semantic Web communities.
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Introduction

Recently, an online social network phenomenon has swept over the Web — MySpace, Friendster, Orkut, thefacebook, LinkedIn — and the signs say that social networks are here to stay; they constitute the social Semantic Web. Few could have imagined it — tens of millions of Web users joining these social network sites, listing openly their online friends and enlisting offline ones too, and more often than not, specifying in great detail and with apparent exhibitionism tidbits about who they are, what music they listen to, what films they fancy. Erstwhile, computer scientists were struggling to extract user profiles by scraping personal homepages, but now, the extraction task is greatly simplified. Not only do self-described personal social network profiles avail greater detail about a user’s interests than a homepage, but on the three most popular sites, these interests are distributed across a greater spectrum of interests such as books, music, films, television shows, foods, sports, passions, profession, etc. Furthermore, the presentation of these user interests is greatly condensed. Whereas interests are sprinkled across hard-to-parse natural language text on personal homepages, the prevailing convention on social network profiles sees interests given as punctuation-delimited keywords and keyphrases (see examples of profiles in Figure 1), sorted by interest genres.

Figure 1.

Examples of social network profile formats, on Orkut (left) and Friendster (right). Note the similarity of categories between the two.

It could be argued that online social networks reflect — with a great degree of insight — the social and cultural order of offline society in general, though we readily concede that not all social segments are fairly represented. Notwithstanding, social network profiles are still a goldmine of information about people and socialization. Much computational research has aimed to understand and model the surface connectedness and social clustering of people within online social network through the application of graph theory to friend-relationships (Wasserman, 1994; Jensen & Neville, 2002; McCallum, Corrada-Emmanuel, & Wang, 2005); ethnographers are finding these networks new resources for studying social behavior in-the-wild. Online social networks have also implemented site features that allow persons to be searched or matched with others on the basis of shared interest keywords.

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