Summarizing social media content via bio-inspired influence maximization algorithms
Christian Esposito, Vincenzo Moscato, Giancarlo Sperlí, Chang Choi · 2020
In this paper, we describe a multimedia summarization technique for Online Social Networks (OSNs) using a bio-inspired influence maximization algorithm. As first step, we model each OSN using an hypergraph based approach that the authors have presented in some previous works. Then, we leverage an influence analysis methodology based on the bees' behaviors within an hive to determine the most important multimedia objects with respect to one or more topics of interest. Finally, a summarization technique is exploited to determine from the list of candidates a multimedia summary in according to a model that favors priority (w.r.t. some user keywords), continuity, variety and not repetitiveness features. Several preliminary experiments on Flickr dataset show the effectiveness of the proposed summarization approach and encourage future work.