9,548 research outputs found
Dataset for 'Data Mining Locative Thematic Narratives: Analysing Twitter and POI Data to Extract Precise Spatial Themes' PhD thesis
No full textCSV of tweet IDs covering all tweets used in the PhD thesis 'Data Mining Locative Thematic Narratives: Analysing Twitter and POI Data to Extract Precise Spatial Themes' by Nicholas Christopher Bennett in July 2020.</span
Recommended from our members
Letter from Pandelis N. Massouridis to Emmett L. Bennett Jr., July 21, 2003
No full textMassouridis introduces himself and his work, "Transcribing Nicholas A. Massouridis' Phonetic Values of the Linear B Signs, to Read the Linear B an Linear A Script" to Bennett, along with his own views of the script used on the Phaistos Disk.Classic
Photo of two postcards, one an iceberg and one St. Nicholas' Anglican church in Torbay
No full textPhoto of two postcards, one an iceberg and one St. Nicholas' Anglican church in Torbay
Interview with Nicholas Christopher, author of Somewhere in the Night: Film Noir and the American City
No full textInterview with Nicholas Christopher, author of Somewhere in the Night: Film Noir and the American Cit
Resurrecting the Author
No full textPresentation of Nicholas Wolterstorff\u27s Paper Resurrecting the Author with time after for questions beginning at 18:00
Data mining locative thematic narratives: analysing twitter and POI data to extract precise spatial themes
No full textThis thesis produces a novel framework for constructing thematic narratives of space. Existing research into extracting information from Twitter data is fraught with inconsistencies and a general lack of attention to metadata. This thesis conducts five experiments to delve deeply into how to extract precise location-based themes from Twitter data, with a focus on under-researched metadata fields and exploration of external APIs to enrich the location data of previously assumed ‘precisely’ geolocated tweets. The analysis is based on Twitter data from June 2016 to August 2018. A key argument is made for the analysis of third-party sources and the stratification of tweet granularity to better understand the scale of location-based narratives. These experiments form a framework that is tested in a case study, then validated and evaluated with qualitative interviews with HM Treasury. This framework is the main contribution of the thesis
Heritability and Linkage Analysis of Appendicitis Utilizing Age at Onset
No full textAppendicitis usually afflicts the young, but there is a large tail in the distribution of onset age. The genetics of this disease are still not well understood. A heritability analysis and genome wide linkage analysis of a large twin dataset was undertaken. Treating age of onset of appendicitis as a censored survival trait revealed a heritability of 0.21, and found evidence of linkage to Chromosome 1p37.3. Author(s): Christopher Oldmeadow 1 * | Kerrie Mengersen 2 | Nicholas Martin 3 | David L. Duffy
The Life and Adventures of Nicholas Nickleby
No full textThis is the playbill for Nicholas Nickleby directed by Barry Fuller with associate directors Jerry Chipman, Julia Ewing and Bennett Wood
Narrative extraction through the detection and characterisation of national and local events
No full textAssessing Twitter geocoding resolution
No full textUser-defined location privacy settings on Twitter cause geolocated tweets to be placed at four different resolutions: precise, point of interest (POI), neighbourhood and city levels. The latter two levels are not described by Twitter or the API, resulting in a risk that clustered tweets are unintentionally treated as real clusters in spatial analyses. This paper outlines a framework to address these differing spatial resolutions and highlight the impact they can have on cartographic representations. As part of this framework this paper also outlines a method of discovering sources (third-party applications) that produce geolocated tweets but do not reflect genuine human activity. We found that including tweets at all spatial resolutions created an artificially inflated importance of certain locations within a city. Discovering device-level geocoded tweets was straight forward, but querying Foursquare's API was required to differentiate between neighbourhood level clusters and POIs
- …
