Reflecting Knowledge Diversity on the Web

The Web has proved to be an unprecedented success for facilitating the publication, use and exchange of information on a planetary scale, on virtually every topic, and representing an amazing diversity of opinions, viewpoints, mindsets and backgrounds. Its design principles and core technological components have lead to an unprecedented growth and mass collaboration. This trend is also finding increasing adoption in business environments. Nevertheless, the Web is also confronted with fundamental challenges with respect to the purposeful access, processing and management of these sheer amounts of information, whilst remaining true to its principles, and leveraging the diversity inherently unfolding through world wide scale collaboration. In this chapter we will motivate engagement with these challenges and the development of methods, techniques, software and data sets that leverage diversity as a crucial source of innovation and creativity. We consider how to provide enhanced support for feasibly managing data at a very large scale, and design novel algorithms that reflect diversity in the ways information is selected, ranked, aggregated, presented and used. A successful diversity-aware information management solution will scale to very large amounts of data and hundreds of thousands of users, but also to a plurality of points of views and opinions. Research towards this end is carried out on realistic data sources with billions of items, through open source extensions to popular communication and collaboration platforms such as MediaWiki and WordPress.

[1]  Panagiotis G. Ipeirotis,et al.  Designing novel review ranking systems: predicting the usefulness and impact of reviews , 2007, ICEC.

[2]  Meredith Ringel Morris,et al.  CoSense: enhancing sensemaking for collaborative web search , 2009, CHI.

[3]  Don Tapscott,et al.  Wikinomics: How Mass Collaboration Changes Everything , 2006 .

[4]  Nello Cristianini,et al.  Detection of Bias in Media Outlets with Statistical Learning Methods , 2009 .

[5]  Jean-Paul Chilès,et al.  Wiley Series in Probability and Statistics , 2012 .

[6]  Kieron O'Hara,et al.  The Devil's Long Tail: Religious Moderation and Extremism on the Web , 2009, IEEE Intelligent Systems.

[7]  Pierre Baldi,et al.  Modeling the Internet and the Web: Probabilistic Methods and Algorithms. By Pierre Baldi, Paolo Frasconi, Padhraic Smith, John Wiley and Sons Ltd., West Sussex, England, 2003. 285 pp ISBN 0 470 84906 1 , 2006, Inf. Process. Manag..

[8]  Min Zhang,et al.  A generation model to unify topic relevance and lexicon-based sentiment for opinion retrieval , 2008, SIGIR '08.

[9]  Clay Shirky Here Comes Everybody: The Power of Organizing Without Organizations , 2008 .

[10]  Douglas B. Terry,et al.  Using collaborative filtering to weave an information tapestry , 1992, CACM.

[11]  M. Neale,et al.  What Differences Make a Difference? , 2005, Psychological science in the public interest : a journal of the American Psychological Society.

[12]  Rajeev Motwani,et al.  The PageRank Citation Ranking : Bringing Order to the Web , 1999, WWW 1999.

[13]  Francisco Iacobelli,et al.  Tell me more, not just "more of the same" , 2010, IUI '10.