How to find people who can help to answer a question? - Analyses of metrics and machine learning in online communities

The Core component has similar results to the whole network.The number of answers is the best metric to discover the user reputation.The users' attributes in the Core component is a better approach than graph metrics.A previously trained neural network may be applied to other community.Around 90% of the users were correctly identified using neural networks. Online communities have become important places for users to exchange information and build knowledge. In these communities, people ask and answer questions, learn with each other, but some problems may occur such as not getting an answer or getting contradictory ones. In order to increase the responsiveness of the communities, it would be important to identify people who are willing to help and who provide good answers in such communities, whom we call reliable users. We investigated various components of online communities and users' attributes looking for a correlation between these characteristics and the users' reputation in these communities. After that, we proposed the usage of two machine learning techniques, artificial neural network and clustering algorithm, with the users' attributes for finding reliable sources. The results show that the usage of an artificial neural network is a good approach as around 90% of the users were correctly identified while the clustering algorithm makes to find groups of reliable users more easily.

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