Interactive Search and Exploration in Online Discussion Forums Using Multimodal Embeddings

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. We show these representations to be useful not only for categorizing users, but also for automatically generating user and community profiles. Inspired by traditional summarization approaches, we create the profiles by selecting diverse and representative content from all available modalities, i.e. the text, image and user modality. 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, to compare different embedding strategies, and to determine the importance of different modalities. We demonstrate the capabilities of the proposed approach on two different multimedia collections originating from the violent online extremism forum Stormfront and the microblogging platform Twitter, which are particularly interesting due to the high semantic level of the discussions they feature

Interactive Multimodal Learning for Venue Recommendation

In this paper, we propose City Melange, an interactive and multimodal content-based venue explorer. Our framework matches the interacting user to the users of social media platforms exhibiting similar taste. The data collection integrates location-based social networks such as Foursquare with general multimedia sharing platforms such as Flickr or Picasa. In City Melange, the user interacts with a set of images and thus implicitly with the underlying semantics. The semantic information is captured through convolutional deep net features in the visual domain and latent topics extracted using Latent Dirichlet allocation in the text domain. These are further clustered to provide representative user and venue topics. A linear SVM model learns the interacting user's preferences and determines similar users. The experiments show that our content-based approach outperforms the user-activity-based and popular vote baselines even from the early phases of interaction, while also being able to recommend mainstream venues to mainstream users and off-the-beaten-track venues to afficionados. City Melange is shown to be a well-performing venue exploration approach