Classification of Message Spreading in a Heterogeneous Social Network
This work addresses the classification of message spreading for social network analysis, but it is incremental as it extends existing methods to heterogeneous networks.
The paper tackles the problem of classifying message spreading in heterogeneous social networks, which include multiple node and link types, by proposing a classifier based on belief functions that interprets message spread, crossed paths, and link types, and tests it on a real Twitter network to show performance.
Nowadays, social networks such as Twitter, Facebook and LinkedIn become increasingly popular. In fact, they introduced new habits, new ways of communication and they collect every day several information that have different sources. Most existing research works fo-cus on the analysis of homogeneous social networks, i.e. we have a single type of node and link in the network. However, in the real world, social networks offer several types of nodes and links. Hence, with a view to preserve as much information as possible, it is important to consider so-cial networks as heterogeneous and uncertain. The goal of our paper is to classify the social message based on its spreading in the network and the theory of belief functions. The proposed classifier interprets the spread of messages on the network, crossed paths and types of links. We tested our classifier on a real word network that we collected from Twitter, and our experiments show the performance of our belief classifier.