Classification of Message Spreading in a Heterogeneous Social Network

TL;DR

This paper introduces a belief function-based classifier to analyze and categorize message spreading in heterogeneous social networks, capturing complex interactions and types of links.

Contribution

It presents a novel classifier that considers heterogeneity and uncertainty in social networks for message classification, using belief functions.

Findings

Effective classification on real Twitter data

Demonstrated the classifier's performance in complex network scenarios

Enhanced understanding of message spreading dynamics

Abstract

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.