Modeling the Information Spreading in Online Blog Communities Using Learning Automata

Abstract

Today's online communities, as a multifaceted platform, have many applications in e-commerce, marketing and e-learning. Online blogging services are one of the most popular environment for user interactions. Users share their ideas, opinions, and information in this environment. The spread of information between users plays an essential role in the success of such online communities. However, these communities face challenges in post management and information spread. Modeling the life cycle of a post provides an opportunity to examine how information is disseminated among users. In these communities, each post after creation is reposted and transmitted by users. Depending on their content and online community structure, posts are spread in different ways in the network. Some posts are rapidly becoming epidemic and some are not welcomed by users. In this article, we are looking for a method that estimates the probability of an epidemic of a post. For this purpose, a learning method based on learning automata has been used. The evaluations show that this method is efficient in three evaluation datasets. Furthermore, we will introduce self-organized posts that facilitate the management of posts in online communities.