Leveraging structural-context similarity of Wikipedia links to predict twitter user locations
| dc.contributor.author | Huang, Chuanqi, author | |
| dc.contributor.author | Pallickara, Sangmi Lee, advisor | |
| dc.contributor.author | Pallickara, Shrideep, committee member | |
| dc.contributor.author | Hayne, Stephen C., committee member | |
| dc.date.accessioned | 2018-01-17T16:46:17Z | |
| dc.date.available | 2018-01-17T16:46:17Z | |
| dc.date.issued | 2017 | |
| dc.description.abstract | Twitter is a widely used social media service. Several efforts have targeted understanding the patterns of information dissemination underlying this social network. A user's location is one of the most important information items relative to analyzing content. However, location information tends to be unavailable because most users do not (want to) include geo-tags in their tweets. To predict a user's location, existing approaches require voluminous training data sets of geo-tagged tweets. However, some of the characteristics of tweets, such as compact, non-traditional linguistic expressions, have posed significant challenges when applying model-fitting approaches. In this thesis, we propose a novel framework for predicting the location of a social media user by leveraging structural-context similarity over Wikipedia links. We measure SimRanks between pages over the Wikipedia dump dataset and build a knowledge base, mapping location information (e.g., cities and states) to related vocabularies along with the likelihood for these mappings. Our results evolve as the users' tweet stream grows. We have implemented this framework using Apache Storm to observe real-time tweets. Finally, our framework provides a list of ranked "probable" cities based on the distances between candidate locations and their weights. This thesis includes empirical evaluations that demonstrate performance that is in line with current state-of-the-art location prediction approaches. | |
| dc.format.medium | born digital | |
| dc.format.medium | masters theses | |
| dc.identifier | Huang_colostate_0053N_14594.pdf | |
| dc.identifier.uri | https://hdl.handle.net/10217/185763 | |
| dc.language | English | |
| dc.language.iso | eng | |
| dc.publisher | Colorado State University. Libraries | |
| dc.relation.ispartof | 2000-2019 | |
| dc.rights | Copyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright. | |
| dc.subject | location prediction | |
| dc.subject | social media | |
| dc.subject | Wikpedia | |
| dc.subject | SimRank | |
| dc.subject | Apache Storm | |
| dc.subject | ||
| dc.title | Leveraging structural-context similarity of Wikipedia links to predict twitter user locations | |
| dc.type | Text | |
| dcterms.rights.dpla | This Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s). | |
| thesis.degree.discipline | Computer Science | |
| thesis.degree.grantor | Colorado State University | |
| thesis.degree.level | Masters | |
| thesis.degree.name | Master of Science (M.S.) |
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