Let's Talk about Sex Education: A Corpus Linguistic Analysis of Advice Column Discourses

This thesis examines sex education discourses in magazines in an attempt to understand how language helps uphold and/or challenge attitudes in sex education. Specifically, it investigates the advice columns of Dolly, an Australian fashion, beauty, lifestyle and celebrity magazine aimed at teenage girls. The data are taken from two time periods, the mid-1990s and mid-2010s, to allow comparison of the discourses of the past twenty years. Corpus linguistics and Appraisal are used to identify these discourses and the linguistic resources used to construct them. The data are also analysed dialogically, or across the question and answer, to examine how these discourses are negotiated in interaction. This analysis reveals the linguistic strategies used to reproduce or challenge the discourse of the question in the corresponding answer, with certain discourses being ‘mirrored’ (i.e. reproduced) and others being ‘shifted’ (i.e. challenged). This thesis extends existing work on Appraisal to examine evaluation in a large corpus of written dialogic texts. It also extends existing research in corpus linguistics which is primarily concerned with patterns across a number of texts (intertextual analysis) rather than within texts (intratextual analysis). In this way, it offers methodological innovations in addition to important findings on the linguistic construction of sex education discourses

The curious case of the school textbook: pedagogical support or covert tool of manipulation?

This ‘think piece’ re-casts the school textbook as ontological envelopes which enfold an unresolved dialectic between constituting and constituted power into the education of every school pupil. Informed by the writings of Deleuze and Guattari (1987) the authors reveal that textbooks act as ‘majoritan’ to categorise, contain and constrain societal conceptions of the ‘Other’. The authors propose that educators must reinvent notions of freedom, authority and ethical responsibility. The authors conclude that to undermine the pernicious power of the ‘normalising’ textbook we need the production of a counter signifying semiotic to overcome pathologies existing in schools today

Normalisation of imprecise temporal expressions extracted from text

Information extraction systems and techniques have been largely used to deal with the increasing amount of unstructured data available nowadays. Time is among the different kinds of information that may be extracted from such unstructured data sources, including text documents. However, the inability to correctly identify and extract temporal information from text makes it difficult to understand how the extracted events are organised in a chronological order. Furthermore, in many situations, the meaning of temporal expressions (timexes) is imprecise, such as in “less than 2 years” and “several weeks”, and cannot be accurately normalised, leading to interpretation errors. Although there are some approaches that enable representing imprecise timexes, they are not designed to be applied to specific scenarios and difficult to generalise. This paper presents a novel methodology to analyse and normalise imprecise temporal expressions by representing temporal imprecision in the form of membership functions, based on human interpretation of time in two different languages (Portuguese and English). Each resulting model is a generalisation of probability distributions in the form of trapezoidal and hexagonal fuzzy membership functions. We use an adapted F1-score to guide the choice of the best models for each kind of imprecise timex and a weighted F1-score (F1 3 D ) as a complementary metric in order to identify relevant differences when comparing two normalisation models. We apply the proposed methodology for three distinct classes of imprecise timexes, and the resulting models give distinct insights in the way each kind of temporal expression is interpreted

A framework to extract biomedical knowledge from gluten-related tweets: the case of dietary concerns in digital era

Journal pre proofBig data importance and potential are becoming more and more relevant nowadays, enhanced by the explosive growth of information volume that is being generated on the Internet in the last years. In this sense, many experts agree that social media networks are one of the internet areas with higher growth in recent years and one of the fields that are expected to have a more significant increment in the coming years. Similarly, social media sites are quickly becoming one of the most popular platforms to discuss health issues and exchange social support with others. In this context, this work presents a new methodology to process, classify, visualise and analyse the big data knowledge produced by the sociome on social media platforms. This work proposes a methodology that combines natural language processing techniques, ontology-based named entity recognition methods, machine learning algorithms and graph mining techniques to: (i) reduce the irrelevant messages by identifying and focusing the analysis only on individuals and patient experiences from the public discussion; (ii) reduce the lexical noise produced by the different ways in how users express themselves through the use of domain ontologies; (iii) infer the demographic data of the individuals through the combined analysis of textual, geographical and visual profile information; (iv) perform a community detection and evaluate the health topic study combining the semantic processing of the public discourse with knowledge graph representation techniques; and (v) gain information about the shared resources combining the social media statistics with the semantical analysis of the web contents. The practical relevance of the proposed methodology has been proven in the study of 1.1 million unique messages from more than 400,000 distinct users related to one of the most popular dietary fads that evolve into a multibillion-dollar industry, i.e., gluten-free food. Besides, this work analysed one of the least research fields studied on Twitter concerning public health (i.e., the allergies or immunology diseases as celiac disease), discovering a wide range of health-related conclusions.SING group thanks CITI (Centro de Investigacion, Transferencia e Innovacion) from the University of Vigo for hosting its IT infrastructure. This work was supported by: the Associate Laboratory for Green Chemistry-LAQV, which is financed by national funds from and the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of [UIDB/50006/2020] and [UIDB/04469/2020] units, and BioTecNorte operation [NORTE010145FEDER000004] funded by the European Regional Development Fund under the scope of Norte2020Programa Operacional Regional do Norte, the Xunta de Galicia (Centro singular de investigacion de Galicia accreditation 2019-2022) and the European Union (European Regional Development Fund - ERDF)- Ref. [ED431G2019/06] , and Conselleria de Educacion, Universidades e Formacion Profesional (Xunta de Galicia) under the scope of the strategic funding of [ED431C2018/55GRC] Competitive Reference Group. The authors also acknowledge the post-doctoral fellowship [ED481B2019032] of Martin PerezPerez, funded by the Xunta de Galicia. Funding for open access charge: Universidade de Vigo/CISUGinfo:eu-repo/semantics/publishedVersio