Modeling Twitter as weighted complex networks using retweets
Social Networks are evolving rapidly to have a significant number of users using or joining their platforms daily, and a huge volume of information exchange and flow continuously. As human is the center of all information traffic, the information propagation became subjected to the relations and influences between the sources of these information and their followers or fans, these influences might be difficult to understand and model directly. While observing Twitter network, we emphasize on the importance of the retweet as a primary method for information propagation inside Twitter network. We present a detailed analysis for the behavior of the retweet functionality in the Twitter platform, and then we show how our findings could shape the diffusion of the information in a network according to the relations and influences between users. Moreover, we propose measures to form a fully weighted directed graph where links and nodes have calculated values, and links are only assigned at the existence of communication transactions; the new measures can be used to predict influences and information diffusions. Finally, we demonstrate how using a retweet network for specific users, we can allocate key players and hidden identities who are potentially having the same ideology or maybe belong to the same group of interest.
2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)