Exploiting contextual information for fine-grained tweet geolocation

• Main Authors: CHONG, Wen Haw, LIM, Ee Peng
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2017
• Subjects: Building model; Contextual information; Fine grained; Geolocations; Homophily; Learning to rank; Location history; Databases and Information Systems; Social Media
• Online Access: https://ink.library.smu.edu.sg/sis_research/3656; https://ink.library.smu.edu.sg/context/sis_research/article/4658/viewcontent/17._May01_2017___Exploiting_Contextual_Information_for_Fine_grained_Tweet_Geolocation__ICWSM17_.pdf
• Institution: Singapore Management University

The problem of fine-grained tweet geolocation is to link tweets to their posting venues. We solve this in a learning to rank framework by ranking candidate venues given a test tweet. The problem is challenging as tweets are short and the vast majority are non-geocoded, meaning information is sparse...