This article addresses the issue of social image search result diversification. We propose a novel perspective for the diversification problem via Relevance Feedback (RF). Traditional RF introduces the user in the processing loop by harvesting feedback about the relevance of the search results. This information is used for recomputing a better representation of the data needed. The novelty of our work is in exploiting this concept in a completely automated manner via pseudo-relevance, while pushing in priority the diversification of the results, rather than relevance. User feedback is simulated automatically by selecting positive and negative examples with regard to relevance, from the initial query results. Unsupervised hierarchical clustering is used to re-group images according to their content. Diversification is finally achieved with a re-ranking approach. Experimental validation on Flickr data shows the advantages of this approach.