InfoSearch: A Social Search Engine

The staggering growth of online social networking platforms has also propelled information sharing among users in the network. This has helped develop the user-to-content link structure in addition to the already present user-to-user link structure. These two data structures has provided us with a wealth of dataset that can be exploited to develop a social search engine and significantly improve our search for relevant information. Every user in a social networking platform has their own unique view of the network. Given this, the aim of a social search engine is to analyze the relationship shared between friends of an individual user and the information shared to compute the most socially relevant result set for a search query. In this work, we present InfoSearch: a social search engine. We focus on how we can retrieve and rank information shared by the direct friend of a user in a social search engine. We ask the question, within the boundary of only one hop in a social network topology, how can we rank the results shared by friends. We develop InfoSearch over the Facebook platform to leverage information shared by users in Facebook. We provide a comprehensive study of factors that may have a potential impact on social search engine results. We identify six different ranking factors and invite users to carry out search queries through InfoSearch. The ranking factors are: ‘diversity’, ‘degree’, ‘betweenness centrality’, ‘closeness centrality’, ‘clustering coefficient’ and ‘time’. In addition to the InfoSearch interface, we also conduct user studies to analyze the impact of ranking factors on the social value of result sets.