This dissertation seeks to understand how new meanings emerge in the context of institutional change. Existing research seeking to understand shifts in meaning has primarily accessed meaning, across numerous contexts, via the three key constructs of discourse, rhetoric, or framing. Within the context of the emergence of the local food movement in Canada, I employ a mixed methods approach using term frequencies, topic modelling and qualitative content analysis, within a computational grounded theory framework for Big Data analysis. My data consists of all articles containing any mention of the term “local food” in popular Canadian press over 37 years from 1978-2014, a database totalling 31,421 articles. My results show that firstly, new meanings pertaining to local food emerged rapidly over the 37-year period. The emergence of a new meaning for local food, associated with the politicization of food production occurred in the second half of my dataset, whereas the first half was marked by connotations of poverty and hunger, associated with the local food bank. Secondly, unexpected actors were found to significantly impact the propulsion of meaning change, by establishing new vocabularies surrounding the term “local food”. Finally, this dissertation shows that the new meanings associated with local food emerged as a result of discursive opportunities, momentarily arising through the confluence of discourse, rhetoric and framing. I propose an emergent process model of meaning change and, further, propose that discursive opportunity structures can be better understood through the metaphor of an emergent property. / Graduate / 2022-08-01

The amount of available text data has increased rapidly in the latest years, making it difficult for an everyday user to find relevant information. To solve this, NLP and visualisation methods have been developed for extracting valuable information from text and presenting it to the user. The aim of this project is to implement a proof-of-concept visualisation prototype for exploring a large amount of Swedish news articles with related metadata and investigate the temporal and relational aspects of the data. The project was divided into three major parts. In the first part, sketches of the visualisation were designed and evaluated through user tests. The second part consisted of designing and implementing a NLP pipeline, using BERTopic, where both Dynamic Topic Modeling (DTM) and Hierarchical Topic Modeling (HTM) were used. Some parameters of the pipeline were evaluated using evaluation metrics and through visual inspection, for instance a Swedish sentence transformer. The final part consisted of implementing and evaluating the visualisation prototype. The project resulted in a web-based visualisation, presenting the NLP results, with two different views: a top 10 topics view and a hierarchical view containing all topics. The prototype has various features, e.g., clicking and hovering for details-on-demand and options for changing and altering the view. The prototype was then evaluated through an internal case study and user tests. For the user tests, there were two groups of participants: people working in the journalism field and people working closely to the NLP field. Both groups thought there was more value in viewing the top 10 topics view than the hierarchical view. Furthermore, the quality of the top 10 topics view was considered higher overall compared to the hierarchical view. In the end, the result of this project is a proof-of-concept visualisation prototype presenting topics of Swedish news articles, over time and in relation to each other. A few possible improvement possibilities include improving the hierarchical relations between the topics and the run time of the topic model and prototype. Also, the prototype may be further improved with additional features, e.g., real-time data, a map, the full text of the articles and a search function. / <p>Examensarbetet är utfört vid Institutionen för teknik och naturvetenskap (ITN) vid Tekniska fakulteten, Linköpings universitet</p>

[pt] Como uma consequência natural da digitalização do sistema judiciário brasileiro, um grande e crescente número de documentos jurídicos tornou-se disponível na internet, especialmente decisões judiciais. Como ilustração, em 2020, o Judiciário brasileiro produziu 25 milhões de decisões. Neste mesmo ano, o Supremo Tribunal Federal (STF), a mais alta corte do judiciário brasileiro, produziu 99.5 mil decisões. Alinhados a esses valores, observamos uma demanda crescente por estudos voltados para a extração e exploração do conhecimento jurídico de grandes acervos de documentos legais. Porém, ao contrário do conteúdo de textos comuns (como por exemplo, livro, notícias e postagem de blog), o texto jurídico constitui um caso particular de uso de uma linguagem altamente convencionalizada. Infelizmente, pouca atenção é dada à extração de informações em domínios especializados, como textos legais. Do ponto de vista temporal, o Judiciário é uma instituição em constante evolução, que se molda para atender às demandas da sociedade. Com isso, o nosso objetivo é propor um processo confiável de extração de informações jurídicas de grandes acervos de documentos jurídicos, tomando como base o STF e as decisões monocráticas publicadas por este tribunal nos anos entre 2000 e 2018. Para tanto, pretendemos explorar a combinação de diferentes técnicas de Processamento de Linguagem Natural (PLN) e Extração de Informação (EI) no contexto jurídico. Da PLN, pretendemos explorar as estratégias automatizadas de reconhecimento de entidades nomeadas no domínio legal. Do ponto da EI, pretendemos explorar a modelagem dinâmica de tópicos utilizando a decomposição tensorial como ferramenta para investigar mudanças no raciocinio juridico presente nas decisões ao lonfo do tempo, a partir da evolução do textos e da presença de entidades nomeadas legais. Para avaliar a confiabilidade, exploramos a interpretabilidade do método empregado, e recursos visuais para facilitar a interpretação por parte de um especialista de domínio. Como resultado final, a proposta de um processo confiável e de baixo custo para subsidiar novos estudos no domínio jurídico e, também, propostas de novas estratégias de extração de informações em grandes acervos de documentos. / [en] As a natural consequence of the Brazilian Judicial System’s digitization, a large and increasing number of legal documents have become available on the Internet, especially judicial decisions. As an illustration, in 2020, 25 million decisions were produced by the Brazilian Judiciary. Meanwhile, the Brazilian Supreme Court (STF), the highest judicial body in Brazil, alone has produced 99.5 thousand decisions. In line with those numbers, we face a growing demand for studies focused on extracting and exploring the legal knowledge hidden in those large collections of legal documents. However, unlike typical textual content (e.g., book, news, and blog post), the legal text constitutes a particular case of highly conventionalized language. Little attention is paid to information extraction in specialized domains such as legal texts. From a temporal perspective, the Judiciary itself is a constantly evolving institution, which molds itself to cope with the demands of society. Therefore, our goal is to propose a reliable process for legal information extraction from large collections of legal documents, based on the STF scenario and the monocratic decisions published by it between 2000 and 2018. To do so, we intend to explore the combination of different Natural Language Processing (NLP) and Information Extraction (IE) techniques on legal domain. From NLP, we explore automated named entity recognition strategies in the legal domain. From IE, we explore dynamic topic modeling with tensor decomposition as a tool to investigate the legal reasoning changes embedded in those decisions over time through textual evolution and the presence of the legal named entities. For reliability, we explore the interpretability of the methods employed. Also, we add visual resources to facilitate interpretation by a domain specialist. As a final result, we expect to propose a reliable and cost-effective process to support further studies in the legal domain and, also, to propose new strategies for information extraction on a large collection of documents.