Exploring place through Twitter text: an investigation into the use of ambient data for furthering geographic understandings of cities - Fitzpatrick, Iona (2019)
Personal digital technologies impact the way both individuals and communities exist in and interact with their surroundings. As these technologies are increasingly interwoven into urban landscapes, they play an integral role in the articulation of personal and shared identities. This thesis investigates the particular ways in which geographies intersect with online text-based narratives and asks what can be understood about place in the contemporary city through social media texts. An extensive review of existing literature from a number of disciplines suggested three salient attributes in the formulation of place-based identity in the contemporary city; experience, time and juxtaposition. These attributes were explored using seven tailored bodies of twitter text, ranging in size from 4,996 to 98,779,803 words. Using a combination of statistical analysis methods taken from Corpus Linguistics (CL) and a case-by-case inductive qualitative analysis of individual tweets, each corpus of tweet text was examined for the preidentified attributes (experience, time and juxtaposition), as well as locational reference, common words (or features) and recurring themes. This thesis develops existing research that posits place as a complex and ever-evolving concept, contingent on the overlaying of multiple voices and demonstrates that social media data can be effective in addressing questions about the relationships between people and the spaces they encounter in their digitally-mediated day-to-day lives. It articulates the key challenges facing researchers undertaking work in a burgeoning interdisciplinary field and presents a clear theoretical and methodological framework for developing studies in this context. Furthermore, it provides a body of evidence that clearly illustrates the utility of crowd-sourced and volunteered information in the uncovering of sense and understanding in fast-moving research contexts.