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In this paper we present a novel interactive multimodal learning system,which facilitates search and exploration in large networks of social multimediausers. It allows the analyst to identify and select users of interest, and tofind similar users in an interactive learning setting. Our approach is based onnovel multimodal representations of users, words and concepts, which wesimultaneously learn by deploying a general-purpose neural embedding model. Weshow these representations to be useful not only for categorizing users, butalso for automatically generating user and community profiles. Inspired bytraditional summarization approaches, we create the profiles by selectingdiverse and representative content from all available modalities, i.e. thetext, image and user modality. The usefulness of the approach is evaluatedusing artificial actors, which simulate user behavior in a relevance feedbackscenario. Multiple experiments were conducted in order to evaluate the qualityof our multimodal representations, to compare different embedding strategies,and to determine the importance of different modalities. We demonstrate thecapabilities of the proposed approach on two different multimedia collectionsoriginating from the violent online extremism forum Stormfront and themicroblogging platform Twitter, which are particularly interesting due to thehigh semantic level of the discussions they feature.

Interactive Search and Exploration in Online Discussion Forums Using Multimodal Embeddings