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Online Social Networks: Measurement, Analysis, and Applications to Distributed Information Systems

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Online Social Networks: Measurement, Analysis, and Applications to Distributed Information Systems ( online-social-networks-measurement-analysis-and-applications )

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210 During the course of the month, we observed 1,730 Google searches. While Google’s first result page contained an average of 9.45 results, our smaller PeerSpec- tive index resulted in an average of 5.17 results on the first page. Of the 1,730 queries, 1,079 (62.3%) resulted in clicks on one or more search result links, 307 (17.7%) were followed by a refined query, and after the remaining 344 (19.8%), the user gave up. We found that 933 (86.5%) of the clicked results were returned only by Google, 83 (7.7%) of the clicked results were returned only by PeerSpective, and 63 (5.7%) of the clicked results were returned by both. This amounts to a 9% improvement in search result clicks over Google alone, as 83 of the search result clicks would not have been possible without PeerSpective. It should be kept in mind that this 9% improvement over Google, considered by many to be the gold standard for Web search engineering, was achieved by a simple, very small, social network-based system quickly put together by three systems researchers over a period of a few days. Based on our early experience, we feel that these results suggest inherent advantages of using social links for search, which could be exploited better with more careful engineering. 9.3 Discussion To better understand the cases when PeerSpective search results outperform Google results, we manually analyzed the corresponding queries and result clicks. We show a random sample of the data we analyzed in Table 9.2. We observed that the reasons

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