The predominant approach of various data privacy regulations around the world is to stop or limit organizational arbitrariness in collecting personal data on the internet. The aim of these data privacy regulations is to address the privacy concerns of users and enable a safe online space where they can disclose their data. Some studies find the intended effect, i.e. more regulation leads to a reduction in privacy concerns of users. However, more regulation does not simply mean that people are less concerned. Research also shows the effect of regulation to be dependent on user characteristics or the strength of the regulation determines whether the effect is positive or negative. Further, there is not the one regulation but depending on what aspect of regulation we look at, for example degree of governmental involvement or restrictiveness of the laws, regulation can have a different form. Due to this variety of regulation and its effects it is necessary to take a closer look at the multiple forms of regulation and their specific interplay with individual behavior to gain a better understanding of the underlying mechanisms that provoke the differences in previous findings. This will then help to adjust regulations according to their intended effects on user behavior, i.e. disclosure of data. To achieve this, the thesis examines the different types of regulatory measures in data privacy regulations and their individual effect on user behavior. This thesis consists of five essays that make use of multiple research methods, i.e. a structured literature review, taxonomy development and quantitative studies. The findings of the thesis contribute to the understanding of the impact of data privacy regulations on individual disclosure behavior by 1) identifying the construct of regulation to be multifaceted with each type of regulation impacting individual behavior differently, 2) ascribing contextual as well as personality based preconditions to the different types of regulatory measures that determine their effectiveness to provide privacy to users, and 3) relating regulatory impact on users with cultural influences, i.e. variations in the impact of the regulatory types depending on the cultural believes of an individual.

This dissertation addresses new computer-assisted methodology for historical linguistics. These methods are directly linked to the comparative method, a set of nineteenth-century principles that combine methodological rigor with an implicit theory of how languages evolve. The computer-assisted make it possible to automize and formalize those principles. Understanding language history offers insight into human history more broadly, particularly in regions where other historical records are not available. The central problem motivating this work is that the empirical groundwork for such historical work, the systematic comparison of language data, has traditionally been carried out manually, in labor-intensive processes, while computational and traditional approaches have often been perceived as contradictory, partly because earlier quantitative methods lacked the scrutiny of manual comparative practice. The implementation of computer-assisted methods further faciliates the comparison of the cross-linguistic diversity in data, since generalizable theories of language change and cognition require evidence from diverse languages rather than a narrow selection of well-documented ones. To address these issues, the dissertation pursues two research questions. First, how the regularity of sound correspondences, the core principle of the comparative method, can be implemented computationally, and what this teaches us about regularity in language change generally. Second, how quantitative analyses of cross-linguistic datasets, using methods related to typology and machine learning, can illuminate broader processes of language evolution. These questions are explored through five studies organized into three parts. The first lays theoretical groundwork, introducing the comparative method, computer-assisted language comparison, and standardized data formats, concluding with a study on constructing standardized comparative wordlists. The second part contains two case studies examining the regularity of correspondence patterns from qualitative and quantitative perspectives, addressing the first research question by showing how this principle can be operationalized computationally. The third part turns to the second research question, presenting analyses extending beyond the comparative method into quantitative typology and machine learning, testing and generating hypotheses about evolutionary processes in language, including language affiliation. Together, these studies show that computational tools, grounded in established comparative principles, can scale up traditional workflows to large datasets, strengthening the empirical foundation of historical linguistics rather than working against it. The thesis demonstrates how formalized implementations of regularity and correspondence patterns can complement manual practice while enabling connections to adjacent fields through interoperable data standards, which in turn facilitate the assembly of large, diverse datasets needed to test hypotheses about language change at scale. In conclusion, the dissertation argues that combining computational rigor with the theoretical insights of the comparative method advances both practical workflows and theoretical understanding of language evolution. The outlook emphasizes building further on standardized data infrastructures and computational models, ultimately contributing to a more comprehensive, empirically grounded theory of how languages change, and how this connects to the broader history of human societies.

This doctoral dissertation investigates the use of 360º technology as a journalistic tool for covering the Colombian armed conflict and supporting narratives of peacebuilding. Using a qualitative multiple-case study approach, it examines four immersive media projects that document the post-conflict experiences of victims and former combatants. Data collection involved interviews with content creators and experts in immersive journalism, document analysis, and in-depth analysis of immersive scenes. The study identifies two primary purposes behind the adoption of immersive technologies in these contexts. It also critically assesses the strengths and limitations of 360º technology when used in conflict reporting. Particular attention is paid to the ethical dimensions of immersive storytelling in conflict settings, including questions of emotional impact, image manipulation, and compromising narrative independence. By outlining good practices and highlighting key concerns, this research contributes to a broader understanding of the role immersive media can play in journalism, particularly when reporting on complex and sensitive issues such as armed conflict.

Overweight and obesity affect billions of adults. Limiting the scope to nutrition, help must offer diet guidance that fits easily into daily life. Health-aware meal plan recommendations must integrate user preferences, nutrition goals, and everyday context, like time and date. However, existing recipe data sets and recommender systems lack general health guidelines, they miss capturing user-recipe interactions, or do not provide explanations or other persuasion methods to improve on the recommendations effectiveness. This thesis closes these gaps and evaluatesproposed systems for health-aware meal planning. We give an extensive background on nutrition and persuasion, an overview of LLMs, and introduce related work in terms of data sets and recommendation approaches. Then, our contribution can be split into three parts. Our first contribution is HUMMUS, a large, linked food graph with approximately 500 000 recipes, 300 000 users, 1.9 million interactions, nutrition scores, and semantic links to FoodOn (a food product hierarchy) and FoodData (nutritional ingredient and food product data). It enables reasoning over ingredient classes and nutrient checks. Its size, presence of interactions, and diverse nutrition information improve usability when compared to related data sets. We discuss sparsity, preprocessing, and filtering options that support different experiments. Our second contribution is a study of food-centric behaviour. We collected questionnaire data and meal logs with an online app to explore correlations between food skills (FS), cooking skills (CS), and intake context. The sample includes 78 participants. Results show that FS and CS correlate. These findings and the app help to explore the domain of food behaviour and to improve further studies and recommender systems. Our third contribution is a framework for a daily meal plan recommendation, including explanations. We design and implement two approaches that accept natural language input.Our first approach, a KBQA system, recommends daily meal plans, supports multiple nutrient constraints, and produces path-grounded explanations. It expands its baseline PFoodReq by those features, but also inherits scalability issues and enforces soft constraints. We then propose the FoodRAG architecture, an LLM-based, agentic, modular pipeline that performs recipe retrieval, validation, ingredient substitution, and step-wise explanation. Despite a higher computational cost than the baselines, its extensive adaptability produces more targeted meal-plan recommendations. Both systems are evaluated and compared to baseline recommendation approaches (collaborative and content-based). Persuasion is addressed by integrating fitting gamification methods, providing explanation generation as well as generic UI aids. As collaborative methods underperform on sparse data like HUMMUS, we evaluate simple content-based baselines, the KBQA system, and three FoodRAG variants. The Text-to-andas retriever (an LLM with natural language input questions to generate Pandas queries used on the FoodRAG data set) gives the strongest recipe retrieval results. Keywords: Health-aware recommendation, Meal planning, Knowledge graphs, Recipe data set, Explainability, Persuasion, KBQA, RAG.

Routing problems typically assume deterministic parameters and independent vehicle operations. Many real-world logistics systems, however, involve synchronization requirements among resources and uncertainty in system parameters — challenges that are both practically relevant and theoretically difficult. This dissertation addresses both dimensions through a series of complementary contributions. We begin with a literature review of specimen logistics, surveying strategic, tactical, and operational routing problems in laboratory supply chains. We then develop a two-index formulation for the specimen collection problem with synchronized multiple trips and one lab, which solves 55 out of 56 small instances where the state-of-the-art model finds none, proves optimality in up to 30% of larger instances, and outperforms the state-of-the-art ALNS in 8 out of 12 settings with an average gap of 1.12%. In the third chapter, we introduce a compact model for the pickup-and-delivery problem with transfers, strengthened by novel valid inequalities, and extended to a novel branch-and-cut approach, which outperforms existing methods by solving 68 of 90 large benchmark instances and, for the first time, solves instances with up to 50 requests. The fourth chapter addresses a truck-and-drone TSP under vehicle synchronization and edge-traversal uncertainty in disaster relief settings; we derive competitive ratios for common policies, validate them in simulation, and propose an improved hybrid policy exploiting uncertainty through strategic surveillance. Finally, we study a production routing problem with stochastic driver availability. Our new deterministic heterogeneous-vehicle reformulation (HetPRP) outperforms our customized Benders decomposition approach, achieving average optimality gaps of 0.11%–0.97% on benchmark instances with up to 50 retailers and nine periods. We further quantify the value of stochastic solutions — up to 5.23% in non-urban settings — and show through a case study that integrating crowd-sourced drivers can yield cost savings of up to 21.71%. Taken together, these contributions advance the state of the art in synchronized and stochastic routing, offering both theoretical guarantees and practically efficient solution methods.