Fine-grained geolocation of tweets in temporal proximity

In fine-grained tweet geolocation, tweets are linked to the specific venues (e.g., restaurants, shops) fromwhich they were posted. This explicitly recovers the venue context that is essential for applications such aslocation-based advertising or user profiling. For this geolocation task, we focus on...

Full description

Saved in:
Bibliographic Details
Main Authors: CHONG, Wen Haw, LIM, Ee Peng
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2019
Subjects:
Online Access:https://ink.library.smu.edu.sg/sis_research/4325
https://ink.library.smu.edu.sg/context/sis_research/article/5328/viewcontent/Fine_grained_Tweets_afv.pdf
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Singapore Management University
Language: English
id sg-smu-ink.sis_research-5328
record_format dspace
spelling sg-smu-ink.sis_research-53282023-07-19T07:54:48Z Fine-grained geolocation of tweets in temporal proximity CHONG, Wen Haw LIM, Ee Peng In fine-grained tweet geolocation, tweets are linked to the specific venues (e.g., restaurants, shops) fromwhich they were posted. This explicitly recovers the venue context that is essential for applications such aslocation-based advertising or user profiling. For this geolocation task, we focus on geolocating tweets that arecontained in tweet sequences. In a tweet sequence, tweets are posted from some latent venue(s) by the sameuser and within a short time interval. This scenario arises from two observations: (1) It is quite common thatusers post multiple tweets in a short time and (2) most tweets are not geocoded. To more accurately geolocatea tweet, we propose a model that performs query expansion on the tweet (query) using two novel approaches.The first approachtemporal query expansionconsiders users’ staying behavior around venues. The secondapproachvisitation query expansionleverages on user revisiting the same or similar venues in the past. Wecombine both query expansion approaches via a novel fusion framework and overlay them on a HiddenMarkov Model to account for sequential information. In our comprehensive experiments across multipledatasets and metrics, we show our proposed model to be more robust and accurate than other baselines. 2019-03-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/4325 info:doi/10.1145/3291059 https://ink.library.smu.edu.sg/context/sis_research/article/5328/viewcontent/Fine_grained_Tweets_afv.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Tweet geolocation temporal proximity staying behavior Databases and Information Systems Social Media
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Tweet geolocation
temporal proximity
staying behavior
Databases and Information Systems
Social Media
spellingShingle Tweet geolocation
temporal proximity
staying behavior
Databases and Information Systems
Social Media
CHONG, Wen Haw
LIM, Ee Peng
Fine-grained geolocation of tweets in temporal proximity
description In fine-grained tweet geolocation, tweets are linked to the specific venues (e.g., restaurants, shops) fromwhich they were posted. This explicitly recovers the venue context that is essential for applications such aslocation-based advertising or user profiling. For this geolocation task, we focus on geolocating tweets that arecontained in tweet sequences. In a tweet sequence, tweets are posted from some latent venue(s) by the sameuser and within a short time interval. This scenario arises from two observations: (1) It is quite common thatusers post multiple tweets in a short time and (2) most tweets are not geocoded. To more accurately geolocatea tweet, we propose a model that performs query expansion on the tweet (query) using two novel approaches.The first approachtemporal query expansionconsiders users’ staying behavior around venues. The secondapproachvisitation query expansionleverages on user revisiting the same or similar venues in the past. Wecombine both query expansion approaches via a novel fusion framework and overlay them on a HiddenMarkov Model to account for sequential information. In our comprehensive experiments across multipledatasets and metrics, we show our proposed model to be more robust and accurate than other baselines.
format text
author CHONG, Wen Haw
LIM, Ee Peng
author_facet CHONG, Wen Haw
LIM, Ee Peng
author_sort CHONG, Wen Haw
title Fine-grained geolocation of tweets in temporal proximity
title_short Fine-grained geolocation of tweets in temporal proximity
title_full Fine-grained geolocation of tweets in temporal proximity
title_fullStr Fine-grained geolocation of tweets in temporal proximity
title_full_unstemmed Fine-grained geolocation of tweets in temporal proximity
title_sort fine-grained geolocation of tweets in temporal proximity
publisher Institutional Knowledge at Singapore Management University
publishDate 2019
url https://ink.library.smu.edu.sg/sis_research/4325
https://ink.library.smu.edu.sg/context/sis_research/article/5328/viewcontent/Fine_grained_Tweets_afv.pdf
_version_ 1772829243727675392