User Modeling and User Profiling: A Comprehensive Survey

The integration of artificial intelligence (AI) into daily life, particularly through information retrieval and recommender systems, has necessitated advanced user modeling and profiling techniques to deliver personalized experiences. These techniques aim to construct accurate user representations based on the rich amounts of data generated through interactions with these systems. This paper presents a comprehensive survey of the current state, evolution, and future directions of user modeling and profiling research. We provide a historical overview, tracing the development from early stereotype models to the latest deep learning techniques, and propose a novel taxonomy that encompasses all active topics in this research area, including recent trends. Our survey highlights the paradigm shifts towards more sophisticated user profiling methods, emphasizing implicit data collection, multi-behavior modeling, and the integration of graph data structures. We also address the critical need for privacy-preserving techniques and the push towards explainability and fairness in user modeling approaches. By examining the definitions of core terminology, we aim to clarify ambiguities and foster a clearer understanding of the field by proposing two novel encyclopedic definitions of the main terms. Furthermore, we explore the application of user modeling in various domains, such as fake news detection, cybersecurity, and personalized education. This survey serves as a comprehensive resource for researchers and practitioners, offering insights into the evolution of user modeling and profiling and guiding the development of more personalized, ethical, and effective AI systems.Comment: 71 page

Social media as intelligence in disaster response: eyewitness classification using community detection

Disasters cause widespread devastation to both physical infrastructure and the lives of individuals residing in large geographic areas. The disruption caused by disaster events is further compounded by high levels of uncertainty and information scarcity, presenting significant challenges to disaster response organisations and impeding the effectiveness of coordinated response efforts. The increasing use of digital technologies, such as social media, presents valuable sources of information that are available in real-time from geographically-distributed networks of ‘humans as sensors’. The data generated by these technologies can supplement traditional sources of intelligence to build models of situational awareness and inform decision-making, resulting in more effective disaster response operations. This thesis proposes a method of curating social media data to enhance its usefulness as a source of intelligence for disaster response organisations during crisis events. The research was conducted in four phases: (i) An ethnographic study developed a conceptual framework of the values and challenges of social media intelligence as perceived by disaster response practitioners. High data volume and low rates of relevance were established as key factors impeding integration with existing intelligence sources. (ii) Empirical studies of Twitter discourse were conducted during eight disaster events to identify patterns of online behaviour and establish the informative potential of social media data as a rich source of eyewitness reports. (iii) Geoproximate preferential attachment (homophily) was identified in the structure of Twitter relationship networks. An eyewitness classification model integrated relationship features for data curation. The model was evaluated on temporally-partitioned subgraphs and shown to be effective in real-time environments. (iv) The classification model was validated in simulated disaster response scenarios conducted with emergency service practitioners. Feedback from participants confirmed the effectiveness of the approach to improving the practical value of social media data as a source of intelligence during disaster response operations