Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/28144
Appears in Collections:Computing Science and Mathematics Conference Papers and Proceedings
Author(s): Ajao, Oluwaseun
Bhowmik, Deepayan
Zargari, Shahrzad
Title: Content-Aware Tweet Location Inference Using Quadtree Spatial Partitioning and Jaccard-Cosine Word Embedding
Citation: Ajao O, Bhowmik D & Zargari S (2018) Content-Aware Tweet Location Inference Using Quadtree Spatial Partitioning and Jaccard-Cosine Word Embedding. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), Barcelona, Spain, 28.08.2018-31.08.2018. Piscataway, NJ, USA: IEEE, pp. 1116-1123. https://doi.org/10.1109/asonam.2018.8508257
Issue Date: 25-Oct-2018
Conference Name: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)
Conference Dates: 2018-08-28 - 2018-08-31
Conference Location: Barcelona, Spain
Abstract: Inferring locations from user texts on social media platforms is a non-trivial and challenging problem relating to public safety. We propose a novel non-uniform grid-based approach for location inference from Twitter messages using Quadtree spatial partitions. The proposed algorithm uses natural language processing (NLP) for semantic understanding and incorporates Cosine similarity and Jaccard similarity measures for feature vector extraction and dimensionality reduction. We chose Twitter as our experimental social media platform due to its popularity and effectiveness for the dissemination of news and stories about recent events happening around the world. Our approach is the first of its kind to make location inference from tweets using Quadtree spatial partitions and NLP, in hybrid word-vector representations. The proposed algorithm achieved significant classification accuracy and outperformed state-of-the-art grid-based content-only location inference methods by up to 24% in correctly predicting tweet locations within a 161km radius and by 300km in median error distance on benchmark datasets.
Status: AM - Accepted Manuscript
Rights: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Files in This Item:
File Description SizeFormat 
RP-ASONAM_2018_paper_102.pdfFulltext - Accepted Version493.96 kBAdobe PDFView/Open



This item is protected by original copyright



Items in the Repository are protected by copyright, with all rights reserved, unless otherwise indicated.

If you believe that any material held in STORRE infringes copyright, please contact library@stir.ac.uk providing details and we will remove the Work from public display in STORRE and investigate your claim.