# Twigraph: Discovering and Visualizing Influential Words between Twitter Profiles

## TL;DR

Twigraph is a novel method that visualizes influential words connecting Twitter profiles, enabling detailed analysis of profile similarities and domain clustering through word graphs and influence metrics.

## Contribution

The paper introduces Twigraph, a hybrid approach combining influence metrics and clustering to discover and visualize influential words linking Twitter profiles across domains.

## Key findings

- Influential words effectively represent profile relationships.
- Profiles clustered accurately by domain using the proposed method.
- Word graphs reveal detailed connections between profiles.

## Abstract

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

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Source: https://tomesphere.com/paper/1706.05361