Library and Information Science Commons^™

Veracity Roadmap: Is Big Data Objective, Truthful And Credible?, Victoria Rubin, Tatiana Lukoianova

FIMS Publications

This paper argues that big data can possess different characteristics, which affect its quality. Depending on its origin, data processing technologies, and methodologies used for data collection and scientific discoveries, big data can have biases, ambiguities, and inaccuracies which need to be identified and accounted for to reduce inference errors and improve the accuracy of generated insights. Big data veracity is now being recognized as a necessary property for its utilization, complementing the three previously established quality dimensions (volume, variety, and velocity), But there has been little discussion of the concept of veracity thus far. This paper provides a roadmap …

Using Ontology-Based Approaches To Representing Speech Transcripts For Automated Speech Scoring, Miao Chen

School of Information Studies - Dissertations

Text representation is a process of transforming text into some formats that computer systems can use for subsequent information-related tasks such as text classification. Representing text faces two main challenges: meaningfulness of representation and unknown terms. Research has shown evidence that these challenges can be resolved by using the rich semantics in ontologies. This study aims to address these challenges by using ontology-based representation and unknown term reasoning approaches in the context of content scoring of speech, which is a less explored area compared to some common ones such as categorizing text corpus (e.g. 20 newsgroups and Reuters).

From the …

Comparative Stylistic Fanfiction Analysis: Popular And Unpopular Fics Across Eleven Fandoms, Victoria L. Rubin, Vanessa Girouard

FIMS Publications

Abstract: This study analyses 545 sample fanfiction stories (fics) in their stylistic feature variation by popularity and across eleven ‘fandoms’ in creative writing forums. Lexical richness, average sentence and paragraph lengths are isolated as promising measures for a text classifier to use in predicting a fic’s likely popularity in its fandom. Résumé: Cette étude analyse un échantillon de 545 chapitres d‘œuvres de fanfiction (fics) selon leur variation stylistique et leur popularité dans onze ‘fandoms’ différents. La richesse lexicale, longueur moyenne de phrase et longueur moyenne de paragraphe ont été choisis comme traits stylistiques propres à différencier les fics populaires des …