  The social media craze is on an ever increasing spree, and people are
connected with each other like never before, but these vast connections are
visually unexplored. We propose a methodology Twigraph to explore the
connections between persons using their Twitter profiles. First, we propose a
hybrid approach of recommending social media profiles, articles, and
advertisements to a user.The profiles are recommended based on the similarity
score between the user profile, and profile under evaluation. The similarity
between a set of profiles is investigated by finding the top influential words
thus causing a high similarity through an Influence Term Metric for each word.
Then, we group profiles of various domains such as politics, sports, and
entertainment based on the similarity score through a novel clustering
algorithm. The connectivity between profiles is envisaged using word graphs
that help in finding the words that connect a set of profiles and the profiles
that are connected to a word. Finally, we analyze the top influential words
over a set of profiles through clustering by finding the similarity of that
profiles enabling to break down a Twitter profile with a lot of followers to
fine level word connections using word graphs. The proposed method was
implemented on datasets comprising 1.1 M Tweets obtained from Twitter.
Experimental results show that the resultant influential words were highly
representative of the relationship between two profiles or a set of profiles
