The aim of this project is to characterize, study and model the sources of bias that emerge from the complex network structure of the Web and from the use of search engines. The feedback loops between users searching information, users creating content, and the ranking algorithms of search engines that mediate between them, lead to surprising results. We are studying how all these systems and communities influence and feed on each other in a dynamic information ecology, and how these interactions affect their evolution and their impact on the global processes of information discovery, retrieval, and utilization.
For example, studying the relationship between Web traffic and PageRank, we have shown that given the heterogeneity of topical interests expressed by search queries, search engines mitigate the popularity bias generated by the rich-get-richer structure of the Web graph. These results, dispelling the feared Googlearchy affect, have been published in Proc. Natl. Acad. Sci. USA, presented at the WAW 2006 keynote (slides), and generated some media attention. You can see some movies demonstrating the finding. The result also inspired a robust rank-based model of scale-free network growth, published in Phys. Rev. Lett. (press release).
We also study sources of bias that stem from legal, political, or economic factors. The CENSEARCHIP tool visualizes the differences between results obtained from different search engines, or different country versions of a search engine. This tool, based on a technique described in this paper in First Monday, generated a lot of reactions in the media and the blogosphere (press release).
Project Participants
Fil Menczer
Sandro Flammini
Alex Vespignani
Santo Fortunato
Mark Meiss
Support
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Mark Meiss is supported by the Advanced Network Management Laboratory, which is one of the Pervasive Technology Labs established at Indiana University with the assistance of the Lilly Endowment. |
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Santo Fortunato was supported by a Volkswagen Foundation grant. |
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This research is also supported in part by the National Science Foundation under awards 0348940, 0513650, and 0705676. |
Opinions, findings, conclusions, recommendations or points of view of this group are those of the authors and do not necessarily represent the official position of the National Science Foundation, the Volkswagen Foundation, or Indiana University.
