Abstract
This paper addresses the problem of discovering hidden affinity relationships in online communities. Online discussions assemble people to talk about various types of topics and to share information. People progressively develop the affinity, and they get closer as frequently as they mention themselves in messages and they send positive messages to one another. We propose an algorithm, named HAR-search, for discovering hidden affinity relationships between individuals. Based on Markov Chain Models, we derive the affinity scores amongst individuals in an online community. We show that our method allows to track the evolution of the affinity over time and to predict affinity relationships arisen from the influence of certain community members. The comparison with the state-of-the-art method shows that our method results in robust discovery and considers minute details.
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