Learning Social Image Embedding with Deep Multimodal Attention Networks

Learning social media data embedding by deep models has attracted extensive research interest as well as boomed a lot of applications, such as link prediction, classification, and cross-modal search. However, for social images which contain both link information and multimodal contents (e.g., text description, and visual content), simply employing the embedding learnt from network structure or data content results in sub-optimal social image representation. In this paper, we propose a novel social image embedding approach called Deep Multimodal Attention Networks (DMAN), which employs a deep model to jointly embed multimodal contents and link information. Specifically, to effectively capture the correlations between multimodal contents, we propose a multimodal attention network to encode the fine-granularity relation between image regions and textual words. To leverage the network structure for embedding learning, a novel Siamese-Triplet neural network is proposed to model the links among images. With the joint deep model, the learnt embedding can capture both the multimodal contents and the nonlinear network information. Extensive experiments are conducted to investigate the effectiveness of our approach in the applications of multi-label classification and cross-modal search. Compared to state-of-the-art image embeddings, our proposed DMAN achieves significant improvement in the tasks of multi-label classification and cross-modal search

On social networks and collaborative recommendation

Social network systems, like last.fm, play a significant role in Web 2.0, containing large amounts of multimedia-enriched data that are enhanced both by explicit user-provided annotations and implicit aggregated feedback describing the personal preferences of each user. It is also a common tendency for these systems to encourage the creation of virtual networks among their users by allowing them to establish bonds of friendship and thus provide a novel and direct medium for the exchange of data. We investigate the role of these additional relationships in developing a track recommendation system. Taking into account both the social annotation and friendships inherent in the social graph established among users, items and tags, we created a collaborative recommendation system that effectively adapts to the personal information needs of each user. We adopt the generic framework of Random Walk with Restarts in order to provide with a more natural and efficient way to represent social networks. In this work we collected a representative enough portion of the music social network last.fm, capturing explicitly expressed bonds of friendship of the user as well as social tags. We performed a series of comparison experiments between the Random Walk with Restarts model and a user-based collaborative filtering method using the Pearson Correlation similarity. The results show that the graph model system benefits from the additional information embedded in social knowledge. In addition, the graph model outperforms the standard collaborative filtering method.</p

A framework for interrogating social media images to reveal an emergent archive of war

The visual image has long been central to how war is seen, contested and legitimised, remembered and forgotten. Archives are pivotal to these ends as is their ownership and access, from state and other official repositories through to the countless photographs scattered and hidden from a collective understanding of what war looks like in individual collections and dusty attics. With the advent and rapid development of social media, however, the amateur and the professional, the illicit and the sanctioned, the personal and the official, and the past and the present, all seem to inhabit the same connected and chaotic space.However, to even begin to render intelligible the complexity, scale and volume of what war looks like in social media archives is a considerable task, given the limitations of any traditional human-based method of collection and analysis. We thus propose the production of a series of ‘snapshots’, using computer-aided extraction and identification techniques to try to offer an experimental way in to conceiving a new imaginary of war. We were particularly interested in testing to see if twentieth century wars, obviously initially captured via pre-digital means, had become more ‘settled’ over time in terms of their remediated presence today through their visual representations and connections on social media, compared with wars fought in digital media ecologies (i.e. those fought and initially represented amidst the volume and pervasiveness of social media images).To this end, we developed a framework for automatically extracting and analysing war images that appear in social media, using both the features of the images themselves, and the text and metadata associated with each image. The framework utilises a workflow comprising four core stages: (1) information retrieval, (2) data pre-processing, (3) feature extraction, and (4) machine learning. Our corpus was drawn from the social media platforms Facebook and Flickr

Online help-seeking in communities of practice

Interactive online help systems are considered to be a fruitful supplement to traditional IT helpdesks, which are often overloaded. They often comprise user-generated FAQ collections playing the role of technology-based conceptual artifacts. Two main questions arise: how the conceptual artifacts should be used, and which factors influence their acceptance in a community of practice (CoP). Firstly, this paper offers a theoretical frame and a usage scenario for technology-based conceptual artifacts against the theoretical background of the academic help-seeking and CoP approach. Each of the two approaches is extensively covered by psychological and educational research literature, however their combination is not yet sufficiently investigated. Secondly, the paper proposes a research model explaining the acceptance of conceptual artifacts. The model includes users’ expectations towards the artifact, perceived social influence and users’ roles in the CoP as predictors of artifact use intention and actual usage. A correlational study conducted in an academic software users’ CoP and involving structural equations modeling validates the model, suggesting thus a research line that is worth further pursuing. For educational practice, the study suggests three ways of supporting knowledge sharing in CoPs, i.e. use of technology-based conceptual artifacts, roles and division of labor, and purposeful communication in CoPs

Search Interfaces for Mathematicians

Access to mathematical knowledge has changed dramatically in recent years, therefore changing mathematical search practices. Our aim with this study is to scrutinize professional mathematicians' search behavior. With this understanding we want to be able to reason why mathematicians use which tool for what search problem in what phase of the search process. To gain these insights we conducted 24 repertory grid interviews with mathematically inclined people (ranging from senior professional mathematicians to non-mathematicians). From the interview data we elicited patterns for the user group "mathematicians" that can be applied when understanding design issues or creating new designs for mathematical search interfaces.Comment: conference article "CICM'14: International Conference on Computer Mathematics 2014", DML-Track: Digital Math Libraries 17 page

HiER 2015. Proceedings des 9. Hildesheimer Evaluierungs- und Retrievalworkshop

Die Digitalisierung formt unsere Informationsumwelten. Disruptive Technologien dringen verstärkt und immer schneller in unseren Alltag ein und verändern unser Informations- und Kommunikationsverhalten. Informationsmärkte wandeln sich. Der 9. Hildesheimer Evaluierungs- und Retrievalworkshop HIER 2015 thematisiert die Gestaltung und Evaluierung von Informationssystemen vor dem Hintergrund der sich beschleunigenden Digitalisierung. Im Fokus stehen die folgenden Themen: Digital Humanities, Internetsuche und Online Marketing, Information Seeking und nutzerzentrierte Entwicklung, E-Learning