302 research outputs found
On the Accuracy of Hyper-local Geotagging of Social Media Content
Social media users share billions of items per year, only a small fraction of
which is geotagged. We present a data- driven approach for identifying
non-geotagged content items that can be associated with a hyper-local
geographic area by modeling the location distributions of hyper-local n-grams
that appear in the text. We explore the trade-off between accuracy, precision
and coverage of this method. Further, we explore differences across content
received from multiple platforms and devices, and show, for example, that
content shared via different sources and applications produces significantly
different geographic distributions, and that it is best to model and predict
location for items according to their source. Our findings show the potential
and the bounds of a data-driven approach to geotag short social media texts,
and offer implications for all applications that use data-driven approaches to
locate content.Comment: 10 page
Social media and GIScience: Collection, analysis, and visualization of user-generated spatial data
Over the last decade, social media platforms have eclipsed the height of popular culture and communication technology, which, in combination with widespread access to GIS-enabled hardware (i.e. mobile phones), has resulted in the continuous creation of massive amounts of user-generated spatial data. This thesis explores how social media data have been utilized in GIS research and provides a commentary on the impacts of this next iteration of technological change with respect to GIScience. First, the roots of GIS technology are traced to set the stage for the examination of social media as a technological catalyst for change in GIScience. Next, a scoping review is conducted to gather and synthesize a summary of methods used to collect, analyze, and visualize this data. Finally, a case study exploring the spatio-temporality of crowdfunding behaviours in Canada during the COVID-19 pandemic is presented to demonstrate the utility of social media data in spatial research
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Projects in Geospatial Data Analysis: Spring 2016
This document contains semester projects for students in CSCI 4380/7000 Geospatial Data Analysis (GSA). The course explores the technical aspects of programmatic geospatial data analysis with a focus on GIS concepts, custom GIS programming, analytical and statistical methods, and open source tools and frameworks
Geolocated Social Media Posts are Happier: Understanding the Characteristics of Check-in Posts on Twitter
The increasing prevalence of location-sharing features on social media has
enabled researchers to ground computational social science research using
geolocated data, affording opportunities to study human mobility, the impact of
real-world events, and more. This paper analyzes what crucially separates posts
with geotags from those without. We find that users who share location are not
representative of the social media user population at large, jeopardizing the
generalizability of research that uses only geolocated data.We consider three
aspects: affect -- sentiment and emotions, content -- textual and non-textual,
and audience engagement. By comparing a dataset of 1.3 million geotagged tweets
with a random dataset of the same size, we show that geotagged posts on Twitter
exhibit significantly more positivity, are often about joyous and special
events such as weddings or graduations, convey more collectivism rather than
individualism, and contain more additional features such as hashtags or objects
in images, but at the same time generate substantially less engagement. These
findings suggest there exist significant differences in the messages conveyed
in geotagged posts. Our research carries important implications for future
research utilizing geolocation social media data.Comment: 11 pages, 10 figures, 2 table
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