Online Social Networks (OSN)[OSN] are experiencing an explosive growth rate and are becoming an increasingly important part of people’s lives. There is an increasing desire to aid online users in identifying potential friends, interesting groups, and compelling products to users. These networks have offered researchers almost total access to large corpora of data. An interesting goal in utilizing this data is to analyze user profiles and identify how similar subsets of users are. The current techniques for comparing users are limited as they require common terms to be shared by users. We present a simple and novel extension to a word-comparison algorithm [6], entitled Inter-Profile Similarity (IPS), which allows comparison of short text phrases even if they share no common terms. The output of Inter-Profile Similarity (IPS) is simply a scalar value in [0,1], with 1 denoting complete similarity and 0 the opposite. Therefore it is easy to understand and can provide a total ordering of users. We, first, evaluated the effectiveness of Inter-Profile Similarity (IPS) with a user-study, and then applied it to datasets from Facebook and Orkut verifying and extending earlier results. We show that Inter-Profile Similarity (IPS) yields both a larger range for the similarity value and obtains a higher value than intersection-based mechanisms. Both Inter-Profile Similarity (IPS) and the output from the analysis of the two Online Social Networks (OSN)[OSN] should help to predict and classify social links, make recommendations, and annotate friends relations for social network analysis.