The big five: Discovering linguistic characteristics that typify distinct personality traits across Yahoo! answers members

Indexación: Scopus.This work was partially supported by the project FONDECYT “Bridging the Gap between Askers and Answers in Community Question Answering Services” (11130094) funded by the Chilean Government.In psychology, it is widely believed that there are five big factors that determine the different personality traits: Extraversion, Agreeableness, Conscientiousness and Neuroticism as well as Openness. In the last years, researchers have started to examine how these factors are manifested across several social networks like Facebook and Twitter. However, to the best of our knowledge, other kinds of social networks such as social/informational question-answering communities (e.g., Yahoo! Answers) have been left unexplored. Therefore, this work explores several predictive models to automatically recognize these factors across Yahoo! Answers members. As a means of devising powerful generalizations, these models were combined with assorted linguistic features. Since we do not have access to ask community members to volunteer for taking the personality test, we built a study corpus by conducting a discourse analysis based on deconstructing the test into 112 adjectives. Our results reveal that it is plausible to lessen the dependency upon answered tests and that effective models across distinct factors are sharply different. Also, sentiment analysis and dependency parsing proven to be fundamental to deal with extraversion, agreeableness and conscientiousness. Furthermore, medium and low levels of neuroticism were found to be related to initial stages of depression and anxiety disorders. © 2018 Lithuanian Institute of Philosophy and Sociology. All rights reserved.https://www.cys.cic.ipn.mx/ojs/index.php/CyS/article/view/275

Supporting organisational learning: an overview of the ENRICH approach

Traditional training separates learning from the work context in which the newly acquired knowledge is to be applied. This requires the worker themselves to apply imparted theoretical knowledge to knowledge in practice, a process that is grossly inefficient. The ENRICH approach builds on organisational learning theory to intertwine working and learning. The ENRICH methodology incorporates theories of learning at the individual, group and organisational level. Individual level learning is supported through the provision of semantically related resources to support problem reframing and to challenge assumptions. Group learning is supported through the evolution of domain concepts through work documents and representations linked to formal models of group knowledge, and the development of group practices and perspectives through enhanced sharing and collaboration. Organisational learning is supported through exposure to customs and conventions of other groups through shared best practices and knowledge models. The approach is being investigated in a range of industrial settings and applications

Quality assurance for digital learning object repositories: issues for the metadata creation process

Metadata enables users to find the resources they require, therefore it is an important component of any digital learning object repository. Much work has already been done within the learning technology community to assure metadata quality, focused on the development of metadata standards, specifications and vocabularies and their implementation within repositories. The metadata creation process has thus far been largely overlooked. There has been an assumption that metadata creation will be straightforward and that where machines cannot generate metadata effectively, authors of learning materials will be the most appropriate metadata creators. However, repositories are reporting difficulties in obtaining good quality metadata from their contributors, and it is becoming apparent that the issue of metadata creation warrants attention. This paper surveys the growing body of evidence, including three UK-based case studies, scopes the issues surrounding human-generated metadata creation and identifies questions for further investigation. Collaborative creation of metadata by resource authors and metadata specialists, and the design of tools and processes, are emerging as key areas for deeper research. Research is also needed into how end users will search learning object repositories

BlogForever D3.2: Interoperability Prospects

This report evaluates the interoperability prospects of the BlogForever platform. Therefore, existing interoperability models are reviewed, a Delphi study to identify crucial aspects for the interoperability of web archives and digital libraries is conducted, technical interoperability standards and protocols are reviewed regarding their relevance for BlogForever, a simple approach to consider interoperability in specific usage scenarios is proposed, and a tangible approach to develop a succession plan that would allow a reliable transfer of content from the current digital archive to other digital repositories is presented

Bridging the gap within text-data analytics: A computer environment for data analysis in linguistic research

[EN] Since computer technology became widespread available at universities during the last quarter of the twentieth century, language researchers have been successfully employing software to analyse usage patterns in corpora. However, although there has been a proliferation of software for different disciplines within text-data analytics, e.g. corpus linguistics, statistics, natural language processing and text mining, this article demonstrates that any computer environment intended to support advanced linguistic research more effectively should be grounded on a user-centred approach to holistically integrate cross-disciplinary methods and techniques in a linguist-friendly manner. To this end, I examine not only the tasks that are derived from linguists' needs and goals but also the technologies that appropriately deal with the properties of linguistic data. This research results in the implementation of DAMIEN, an online workbench designed to conduct linguistic experiments on corpora.Financial support for this research has been provided by the DGI, Spanish Ministry of Education and Science, grant FFI2014-53788-C3-1-P.Periñán Pascual, C. (2017). Bridging the gap within text-data analytics: A computer environment for data analysis in linguistic research. LFE. Revista de Lenguas para Fines Específicos. 23(2):111-132. https://doi.org/10.20420/rlfe.2017.175S11113223

Bridging the gap within text-data analytics: a computer environment for data analysis in linguistic research

Since computer technology became widespread available at universities during the last quarter of the twentieth century, language researchers have been successfully employing software to analyse usage patterns in corpora. However, although there has been a proliferation of software for different disciplines within text-data analytics, e.g. corpus linguistics, statistics, natural language processing and text mining, this article demonstrates that any computer environment intended to support advanced linguistic research more effectively should be grounded on a user-centred approach to holistically integrate cross-disciplinary methods and techniques in a linguist-friendly manner. To this end, I examine not only the tasks that are derived from linguists' needs and goals but also the technologies that appropriately deal with the properties of linguistic data. This research results in the implementation of DAMIEN, an online workbench designed to conduct linguistic experiments on corpora

Investigating the influences of context on the information seeking behaviour of first-generation students

This paper reports on a qualitative study investigating the information seeking behaviour of first-generation students enrolled at the University of Johannesburg, South Africa, in the Humanities’ Mastering Academic and Professional Skills programme. The purpose is to determine the way in which their socio-economic situations influenced their ability to solve information problems. The target population come from low-income families with low social and cultural capacity in their home environment. A qualitative phenomenological research approach was followed. A purposive convenience sample was drawn from the target population. A total of 17 students participated in the study. Qualitative data analysis procedures were used with inductive reasoning as an open-ended approach. The data were collected using interviews with an open- and close-ended interview schedule. Both contextual (environmental) and personal experiences influence the information seeking behaviour of first-generation students. Information literacy as an intervention served as a catalyst to change the students’ information seeking behaviour. An understanding of first-generation students’ information seeking behaviour can provide insights for librarians on the way in which to meet these students’ information needs.Peer Reviewe