Narratives are fundamental to our perception of the world and are pervasive in all activities that involve the representation of events in time. Yet, modern online information systems do not incorporate narratives in their representation of events occurring over time. This article aims to bridge this gap, combining the theory of narrative representations with the data from modern online systems. We make three key contributions: a theory-driven computational representation of narratives, a novel extraction algorithm to obtain these representations from data, and an evaluation of our approach. In particular, given the effectiveness of visual metaphors, we employ a route map metaphor to design a narrative map representation. The narrative map representation illustrates the events and stories in the narrative as a series of landmarks and routes on the map. Each element of our representation is backed by a corresponding element from formal narrative theory, thus providing a solid theoretical background to our method. Our approach extracts the underlying graph structure of the narrative map using a novel optimization technique focused on maximizing coherence while respecting structural and coverage constraints. We showcase the effectiveness of our approach by performing a user evaluation to assess the quality of the representation, metaphor, and visualization. Evaluation results indicate that the Narrative Map representation is a powerful method to communicate complex narratives to individuals. Our findings have implications for intelligence analysts, computational journalists, and misinformation researchers.

The aim of this work was to establish the intervention requirements of 30 tailings deposits located in the region of Antofagasta, Chile, identified in the National Tailings Survey carried out by the National Service of Geology and Mining of the Chilean government in 2017. For the evaluation, the data of 49 geochemical characterizations of the samples registered as of October 2017 were considered. The methodology used for evaluation was based on the Dutch Soil Regulation Circular, from which two ways of assessment were developed. The first approach uses a simple graphic method that depends only on the concentration of the elements in the solid phase of tailings and the percentage of clay in the soil. The second approach considers that the information related to the clay percentage is not available, therefore, this work defines the concepts of threshold factor and adjusted threshold factor. Using the proposed method, this work presents the analysis carried out on the elements of environmental significance related to mining activity in Chile: Cu, Cr, Ni, Zn, Pb, As, Cd and Hg. The methodology allows delimiting areas of foreseeable risks for people and/or environment, resulting in three criteria for each element: 1) requires intervention, 2) requires conditional intervention, 3) does not require intervention. These results are summarized by means of a final score that determines the level of priority assigned to each sample. The analysis of results indicates that Hg intervention is not required, there being only one case requiring conditional intervention. Regarding Cu and Zn, 100 % of the measurements indicated at least the requirement of a conditional intervention. Within the region, the commune of Sierra Gorda contains 67 % of the tailings whose intervention must be prioritized according to the score obtained (greater than four out of a possible maximum of eight). On the other hand, the two tailings of greater tonnage (the sites of Talabre and Laguna Seca) do not present a requirement for priority intervention at the moment.

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Narratives are fundamental to our understanding of the world, providing us with a natural structure for knowledge representation over time. Computational narrative extraction is a subfield of artificial intelligence that makes heavy use of information retrieval and natural language processing techniques. Despite the importance of computational narrative extraction, relatively little scholarly work exists on synthesizing previous research and strategizing future research in the area. In particular, this article focuses on extracting news narratives from an event-centric perspective. Extracting narratives from news data has multiple applications in understanding the evolving information landscape. This survey presents an extensive study of research in the area of event-based news narrative extraction. In particular, we screened over 900 articles that yielded 54 relevant articles. These articles are synthesized and organized by representation model, extraction criteria, and evaluation approaches. Based on the reviewed studies, we identify recent trends, open challenges, and potential research lines.

Making sense of large collections of images is difficult. Dimension reductions (DR) assist by organizing images in a 2D space based on similarities, but provide little support for explaining why images were placed together or apart in the 2D space. Additionally, they do not provide support for modifying and updating the 2D space to explore new relationships and organizations of images. To address these problems, we present an interactive DR method for images that uses visual features extracted by a deep neural network to project the images into 2D space and provides visual explanations of image features that contributed to the 2D location. In addition, it allows people to directly manipulate the 2D projection space to define alternative relationships and explore subsequent projections of the images. With an iterative cycle of semantic interaction and explainable-AI feedback, people can explore complex visual relationships in image data. Our approach to human-AI interaction integrates visual knowledge from both human mental models and pre-trained deep neural models to explore image data. Two usage scenarios are provided to demonstrate that our method is able to capture human feedback and incorporate it into the model. Our visual explanations help bridge the gap between the feature space and the original images to illustrate the knowledge learned by the model, creating a synergy between human and machine that facilitates a more complete analysis experience. Firstly, I would like to thank my advisor Dr. Christopher North for his continued support in my research study. He was and remains my best role model for a teacher, mentor, and researcher.