Interactive Search and Exploration in Discussion Forums Using Multimodal Embeddings

Abstract

In this paper we present a novel interactive multimodal learning system, which facilitates search and exploration in large networks of social multimedia users. It allows the analyst to identify and select users of interest, and to find similar users in an interactive learning setting. Our approach is based on novel multimodal representations of users, words and concepts, which we simultaneously learn by deploying a general-purpose neural embedding model. The usefulness of the approach is evaluated using artificial actors, which simulate user behavior in a relevance feedback scenario. Multiple experiments were conducted in order to evaluate the quality of our multimodal representations and compare different embedding strategies. We demonstrate the capabilities of the proposed approach on a multimedia collection originating from the violent online extremism forum Stormfront, which is particularly interesting due to the high semantic level of the discussions it features.