VacationFinder: A tool for collecting, analyzing, and visualizing geotagged Twitter data to find top vacation spots

Jalal S. Alowibdi, Sohaib Ghani, Mohamed Mokbel

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Citations (Scopus)

Abstract

Choosing a location for vacations and weekends usually confuses many people. This concern has attracted considerable attention in recent years as currently there is no application based on actual visitors that helps people in finding out the top places for vacations. Online social networks such as Twitter are becoming very popular in last few years and can help in this regard. People nowadays generally do check-ins at new places. Also, analysis of tweets tagged with geolocation and time can provide trends of top vacation spots. In this paper, we present VacationFinder; a novel locationbased application that uses geotagged tweets to help people in where they should spend their holidays and weekends. We use real Twitter data crawled since October 2013. We apply indexing, spatio-temporal querying, and machine learning techniques to check, analyze, and filter the user activities in a particular country before and after a specific holiday. We then visualize the results and give our recommendations of top vacation spots for a particular holiday. The paper includes use cases on top vacation spots for Saudis in spring break of 2014 both inside as well as outside Saudi Arabia. Our application can not only help people but can also give direction to governmental agencies about promoting tourism in the country. It can also help law enforcement agencies, advertisement industry, and various businesses such as restaurants and shopping stores about where to focus during a particular holiday.

Original languageEnglish
Title of host publicationProceedings of the 7th ACM SIGSPATIAL International Workshop on Location-Based Social Networks, LBSN 2014 - Held in conjunction with the 22nd ACM SIGSPATIAL GIS 2014
PublisherAssociation for Computing Machinery, Inc
Pages9-12
Number of pages4
ISBN (Electronic)9781450331401
DOIs
Publication statusPublished - 4 Nov 2014
Externally publishedYes
Event7th ACM SIGSPATIAL International Workshop on Location-Based Social Networks, LBSN 2014 - Dallas, United States
Duration: 4 Nov 2014 → …

Other

Other7th ACM SIGSPATIAL International Workshop on Location-Based Social Networks, LBSN 2014
CountryUnited States
CityDallas
Period4/11/14 → …

Fingerprint

Law enforcement
Learning systems
Industry

ASJC Scopus subject areas

  • Computer Networks and Communications

Cite this

Alowibdi, J. S., Ghani, S., & Mokbel, M. (2014). VacationFinder: A tool for collecting, analyzing, and visualizing geotagged Twitter data to find top vacation spots. In Proceedings of the 7th ACM SIGSPATIAL International Workshop on Location-Based Social Networks, LBSN 2014 - Held in conjunction with the 22nd ACM SIGSPATIAL GIS 2014 (pp. 9-12). Association for Computing Machinery, Inc. https://doi.org/10.1145/2755492.2755495

VacationFinder : A tool for collecting, analyzing, and visualizing geotagged Twitter data to find top vacation spots. / Alowibdi, Jalal S.; Ghani, Sohaib; Mokbel, Mohamed.

Proceedings of the 7th ACM SIGSPATIAL International Workshop on Location-Based Social Networks, LBSN 2014 - Held in conjunction with the 22nd ACM SIGSPATIAL GIS 2014. Association for Computing Machinery, Inc, 2014. p. 9-12.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Alowibdi, JS, Ghani, S & Mokbel, M 2014, VacationFinder: A tool for collecting, analyzing, and visualizing geotagged Twitter data to find top vacation spots. in Proceedings of the 7th ACM SIGSPATIAL International Workshop on Location-Based Social Networks, LBSN 2014 - Held in conjunction with the 22nd ACM SIGSPATIAL GIS 2014. Association for Computing Machinery, Inc, pp. 9-12, 7th ACM SIGSPATIAL International Workshop on Location-Based Social Networks, LBSN 2014, Dallas, United States, 4/11/14. https://doi.org/10.1145/2755492.2755495
Alowibdi JS, Ghani S, Mokbel M. VacationFinder: A tool for collecting, analyzing, and visualizing geotagged Twitter data to find top vacation spots. In Proceedings of the 7th ACM SIGSPATIAL International Workshop on Location-Based Social Networks, LBSN 2014 - Held in conjunction with the 22nd ACM SIGSPATIAL GIS 2014. Association for Computing Machinery, Inc. 2014. p. 9-12 https://doi.org/10.1145/2755492.2755495
Alowibdi, Jalal S. ; Ghani, Sohaib ; Mokbel, Mohamed. / VacationFinder : A tool for collecting, analyzing, and visualizing geotagged Twitter data to find top vacation spots. Proceedings of the 7th ACM SIGSPATIAL International Workshop on Location-Based Social Networks, LBSN 2014 - Held in conjunction with the 22nd ACM SIGSPATIAL GIS 2014. Association for Computing Machinery, Inc, 2014. pp. 9-12
@inproceedings{0144d3a026ce400da58d786a5a800f83,
title = "VacationFinder: A tool for collecting, analyzing, and visualizing geotagged Twitter data to find top vacation spots",
abstract = "Choosing a location for vacations and weekends usually confuses many people. This concern has attracted considerable attention in recent years as currently there is no application based on actual visitors that helps people in finding out the top places for vacations. Online social networks such as Twitter are becoming very popular in last few years and can help in this regard. People nowadays generally do check-ins at new places. Also, analysis of tweets tagged with geolocation and time can provide trends of top vacation spots. In this paper, we present VacationFinder; a novel locationbased application that uses geotagged tweets to help people in where they should spend their holidays and weekends. We use real Twitter data crawled since October 2013. We apply indexing, spatio-temporal querying, and machine learning techniques to check, analyze, and filter the user activities in a particular country before and after a specific holiday. We then visualize the results and give our recommendations of top vacation spots for a particular holiday. The paper includes use cases on top vacation spots for Saudis in spring break of 2014 both inside as well as outside Saudi Arabia. Our application can not only help people but can also give direction to governmental agencies about promoting tourism in the country. It can also help law enforcement agencies, advertisement industry, and various businesses such as restaurants and shopping stores about where to focus during a particular holiday.",
author = "Alowibdi, {Jalal S.} and Sohaib Ghani and Mohamed Mokbel",
year = "2014",
month = "11",
day = "4",
doi = "10.1145/2755492.2755495",
language = "English",
pages = "9--12",
booktitle = "Proceedings of the 7th ACM SIGSPATIAL International Workshop on Location-Based Social Networks, LBSN 2014 - Held in conjunction with the 22nd ACM SIGSPATIAL GIS 2014",
publisher = "Association for Computing Machinery, Inc",

}

TY - GEN

T1 - VacationFinder

T2 - A tool for collecting, analyzing, and visualizing geotagged Twitter data to find top vacation spots

AU - Alowibdi, Jalal S.

AU - Ghani, Sohaib

AU - Mokbel, Mohamed

PY - 2014/11/4

Y1 - 2014/11/4

N2 - Choosing a location for vacations and weekends usually confuses many people. This concern has attracted considerable attention in recent years as currently there is no application based on actual visitors that helps people in finding out the top places for vacations. Online social networks such as Twitter are becoming very popular in last few years and can help in this regard. People nowadays generally do check-ins at new places. Also, analysis of tweets tagged with geolocation and time can provide trends of top vacation spots. In this paper, we present VacationFinder; a novel locationbased application that uses geotagged tweets to help people in where they should spend their holidays and weekends. We use real Twitter data crawled since October 2013. We apply indexing, spatio-temporal querying, and machine learning techniques to check, analyze, and filter the user activities in a particular country before and after a specific holiday. We then visualize the results and give our recommendations of top vacation spots for a particular holiday. The paper includes use cases on top vacation spots for Saudis in spring break of 2014 both inside as well as outside Saudi Arabia. Our application can not only help people but can also give direction to governmental agencies about promoting tourism in the country. It can also help law enforcement agencies, advertisement industry, and various businesses such as restaurants and shopping stores about where to focus during a particular holiday.

AB - Choosing a location for vacations and weekends usually confuses many people. This concern has attracted considerable attention in recent years as currently there is no application based on actual visitors that helps people in finding out the top places for vacations. Online social networks such as Twitter are becoming very popular in last few years and can help in this regard. People nowadays generally do check-ins at new places. Also, analysis of tweets tagged with geolocation and time can provide trends of top vacation spots. In this paper, we present VacationFinder; a novel locationbased application that uses geotagged tweets to help people in where they should spend their holidays and weekends. We use real Twitter data crawled since October 2013. We apply indexing, spatio-temporal querying, and machine learning techniques to check, analyze, and filter the user activities in a particular country before and after a specific holiday. We then visualize the results and give our recommendations of top vacation spots for a particular holiday. The paper includes use cases on top vacation spots for Saudis in spring break of 2014 both inside as well as outside Saudi Arabia. Our application can not only help people but can also give direction to governmental agencies about promoting tourism in the country. It can also help law enforcement agencies, advertisement industry, and various businesses such as restaurants and shopping stores about where to focus during a particular holiday.

UR - http://www.scopus.com/inward/record.url?scp=84964072916&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84964072916&partnerID=8YFLogxK

U2 - 10.1145/2755492.2755495

DO - 10.1145/2755492.2755495

M3 - Conference contribution

AN - SCOPUS:84964072916

SP - 9

EP - 12

BT - Proceedings of the 7th ACM SIGSPATIAL International Workshop on Location-Based Social Networks, LBSN 2014 - Held in conjunction with the 22nd ACM SIGSPATIAL GIS 2014

PB - Association for Computing Machinery, Inc

ER -