Abstract
Transthyretin-associated Familial Amyloid Polyneuropathy (TTR-FAP) is a chronic fatal disease with a high incidence in Portugal. It is therefore relevant to provide professionals and citizens with a tool that enables a detailed geographical and territorial study. For this reason, we have developed an web based application that brings together techniques applied to spatial data that allow the study of the historical progression and growth of cases in patients' residential areas and areas of origin as well as an epidemic forecast. The tool enables the exploration of geographical longitudinal data at national, district and county levels. High density regions and periods can be visually identified according to parameters selected by the user. The visual evaluation of the data and its comparison across different time spans of the disease era can have an impact on more informed decision making by those working with patients to improve their quality of life, treatment or follow-up. The tool is available online for data exploration and its code is available on GitHub for adaptation to other geospatial scenarios.

Abstract
In recent years, the amount of information generated, consumed and stored has grown at an astonishing rate, making it difficult for those seeking information to extract knowledge in good time. This has become even more important, as the average reader is not as willing to spare more time out of their already busy schedule as in the past, thus prioritizing news in a summarized format, which are faster to digest. On top of that, people tend to increasingly rely on strong visual components to help them understand the focal point of news articles in a less tiresome manner. This growing demand, focused on exploring information through visual aspects, urges the need for the emergence of alternative approaches concerned with text understanding and narrative exploration. This motivated us to propose Text2Storyline, a platform for generating and exploring enriched storylines from an input text, a URL or a user query. The latter is to be issued on the PortugueseWebArchive (Arquivo.pt), therefore giving users the chance to expand their knowledge and build up on information collected from web sources of the past. To fulfill this objective, we propose a system that makes use of the TimeMatters algorithm to filter out non-relevant dates and organize relevant content by means of different displays: `Annotated Text', `Entities', `Storyline', `Temporal Clustering' and `Word Cloud'. To extend the users' knowledge, we rely on entity linking to connect persons, events, locations and concepts found in the text to Wikipedia pages, a process also known as Wikification. Each of the entities is then illustrated by means of an image collected from the Arquivo.pt.

Abstract
News articles typically include reporting events to inform on what happened. These reporting events are not part of the story being told but are nonetheless a relevant part of the news and can pose a challenge to the computational processing of news narratives. They compose a reporting narrative, which is the present study's focus. This paper aims to demonstrate through selected use cases how a comprehensive annotation scheme with suitable tags and links can properly represent the reporting events and the way they relate to the events that make the story. In addition, we put forward a proposal for their visual representation that enables a systematic and detailed analysis of the importance of reporting events in the news structure. Finally, we describe some lexico-grammatical features of reporting events, which can contribute to their automatic detection. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

Abstract

Abstract

Abstract