In our group we focus on modeling, instantiating and using social context for applications. Here, social context refers to all aspects of short-, medium-, or long-term human social interaction that are or may be related to, mediated by, or significant for IT systems. Respective scientific fields include models and applications of social signal processing, social network analysis, or social media analysis. Our current main field is natural language processing.
Examples for past and current projects include explanations and human-in-the-loop approaches for nlp models, ethical ai, opinion mining, social context in hate speech detection, social motivation and social recommendation for mobile lifestyle applications, characterizing social situations with interaction geometry and co-activity detection, models of topical influence in social networks, audio-based social situation assessment and availability management, social and geo-social information retrieval, privacy in social networking, collaborative music composition, geo-social networking and mobility prediction, or social networking techniques for open innovation management.
Ultimately working towards increasing user's utility via suitable applications, we mainly focus on design science methodologies supported by empirical / machine learning techniques using large, mostly text-based data-sets.