Content-Based Geolocation Prediction of Canadian Twitter Users and Their Tweets

FieldValue
dc.contributor.authorMetin, Ali Mert
dc.date.accessioned2019-08-13T18:51:43Z
dc.date.available2019-08-13T18:51:43Z
dc.date.issued2019-08-13
dc.identifier.urihttp://hdl.handle.net/10393/39508
dc.identifier.urihttp://dx.doi.org/10.20381/ruor-23751
dc.description.abstractLast decade witnessed the rise of online social networks, especially Twitter. Today, Twitteris a giant social platform with over 250 million users |who produce massive amounts of data everyday. This creates many research opportunities, speci cally for Natural Language Processing (NLP) in which text is utilized to extract information that could be used in many applications. One problem NLP might help solving is geolocation inference or geolocation detection from online social networks. Detecting the location of Twitter users based on the text of their tweets is useful since not many users publicly declare their locations or geotag their tweets. Location information is crucial for a variety of applications such as event detection, disease and illness tracking and user pro ling. These tasks are not trivial, because online content is often noisy; it includes misspellings, incomplete words or phrases, idiomatic expressions, abbreviations, acronyms, and Twitter-speci c literature. In this work, we attempted to detect the location of Canadian users |and tweets sent from Canada |at metropolitan areas and province level; this was not done before, to the best of our knowledge. In order to do this, we collected two di erent datasets, and applied a variety of machine learning, including deep learning methods. Besides, we also attempted to geolocate users based on their social graph (i.e., user's friends and followers) as a novel approach.
dc.language.isoen
dc.publisherUniversité d'Ottawa / University of Ottawa
dc.subjectGeolocation
dc.subjectCanadian Twitter users
dc.subjectTwitter
dc.subjectNatural language processing
dc.titleContent-Based Geolocation Prediction of Canadian Twitter Users and Their Tweets
dc.typeThesis
dc.contributor.supervisorInkpen, Diana
dc.contributor.supervisorZhu, Xiaodan
thesis.degree.nameMSc
thesis.degree.levelMasters
thesis.degree.disciplineGénie / Engineering
uottawa.departmentScience informatique et génie électrique / Electrical Engineering and Computer Science
CollectionThèses, 2011 - // Theses, 2011 -

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