A graph mining method for characterizing and measuring user engagement in Twitter

In the modern world, social media plays a crucial role in the interchange of information and socialization with users. Twitter is a known social media platform that allows users to make relationships with others and express their opinions. The current work aims to identify the level of user engagement on Twitter with the use of graph mining. User engagement concerns the number of user connections with a tweet and can be measured using different tweet attributes including retweets, replies, etc. Specifically, this study investigates the variety of edges strength that user connections can implement in Twitter networks. Next, we employed various weights in the graph mining models to evaluate the score of each connection. These tasks were followed by statistical analysis to measure the similarity between the two user profiles as well as attributes like friendship, following and interaction in the Twitter social network. Results indicate that closely linked groups can be revealed and thus, a need for examining both group and individual behavior, will arise.