research-article

On the locality of keywords in Twitter streams

Authors:

Hamed Abdelhaq,

Michael GertzAuthors Info & Claims

IWGS '14: Proceedings of the 5th ACM SIGSPATIAL International Workshop on GeoStreaming

Pages 12 - 20

https://doi.org/10.1145/2676552.2676554

Published: 04 November 2014 Publication History

Abstract

The continuously increasing popularity of social media sites such as Twitter and Facebook has recently led to a number of approaches to detect and extract event information from social media streams. Such events play an important role, e.g., in supporting location-based services and improving situational awareness. Moreover, the introduction of GPS-equipped communication devises has led to an increase in the percentage of geo-tagged messages. These help to detect localized events, i.e., events occurring at a certain location, such as sport events or accidents. The main entities that indicate a localized event are local keywords that exhibit a surge in usage at the event location.

In this paper, we propose an approach to extract local keywords from a Twitter stream by (1) identifying local keywords, and (2) estimating the central location of each keyword. This extraction process is performed in an online fashion using a sliding window on the Twitter stream. In addition, we address the problem of spatial outliers that adversely affect a proper identification of local keywords. Outliers occur when people far away from an event location use related keywords in their Tweets. We handle this problem by adjusting the spatial distribution of keywords based on their co-occurrence with place names that may refer to the location of an event. We evaluate the performance of our framework to reliably and efficiently extracting local keywords and estimating their central locations using a Twitter dataset.

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Cited By

Al Jawarneh IBellavista PCorradi AFoschini LMontanari R(2020)Big Spatial Data Management for the Internet of Things: A SurveyJournal of Network and Systems Management10.1007/s10922-020-09549-6Online publication date: 27-Jul-2020
https://doi.org/10.1007/s10922-020-09549-6
Chen LShang SJensen CXu JKalnis PYao BShao L(2020)Top-k term publish/subscribe for geo-textual data streamsThe VLDB Journal10.1007/s00778-020-00607-8Online publication date: 9-Mar-2020
https://doi.org/10.1007/s00778-020-00607-8
Brandt TGrawunder M(2018)GeoStreamsACM Computing Surveys10.1145/317784851:3(1-37)Online publication date: 23-May-2018
https://dl.acm.org/doi/10.1145/3177848
Show More Cited By

Index Terms

On the locality of keywords in Twitter streams
1. Human-centered computing
  1. Visualization
    1. Visualization application domains
      1. Geographic visualization
2. Information systems
  1. Information systems applications
    1. Spatial-temporal systems

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Published In

cover image ACM Conferences

IWGS '14: Proceedings of the 5th ACM SIGSPATIAL International Workshop on GeoStreaming

November 2014

100 pages

ISBN:9781450331395

DOI:10.1145/2676552

Conference Chair:
Phuc Nguyen
University of North Texas
,
Editors:
Chengyang Zhang
Teradata
,
Anas Basalamah
Umm Al Qura University
,
Abdeltawab Hendawi
University of Washington Tacoma

Copyright © 2014 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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ESRI
Yandex
Mapbox: Mapbox
Google Inc.
NVIDIA
University of North Texas: University of North Texas
Microsoft: Microsoft
ORACLE: ORACLE
Facebook: Facebook
SIGSPATIAL: ACM Special Interest Group on Spatial Information

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 04 November 2014

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SIGSPATIAL '14

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Mapbox
University of North Texas
Microsoft
ORACLE
Facebook
SIGSPATIAL

SIGSPATIAL '14: 22nd SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 4, 2014

Texas, Dallas

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Overall Acceptance Rate 7 of 9 submissions, 78%

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Citations

Cited By

Al Jawarneh IBellavista PCorradi AFoschini LMontanari R(2020)Big Spatial Data Management for the Internet of Things: A SurveyJournal of Network and Systems Management10.1007/s10922-020-09549-6Online publication date: 27-Jul-2020
https://doi.org/10.1007/s10922-020-09549-6
Chen LShang SJensen CXu JKalnis PYao BShao L(2020)Top-k term publish/subscribe for geo-textual data streamsThe VLDB Journal10.1007/s00778-020-00607-8Online publication date: 9-Mar-2020
https://doi.org/10.1007/s00778-020-00607-8
Brandt TGrawunder M(2018)GeoStreamsACM Computing Surveys10.1145/317784851:3(1-37)Online publication date: 23-May-2018
https://dl.acm.org/doi/10.1145/3177848
Abdelhaq HGertz MArmiti A(2018)Efficient online extraction of keywords for localized events in twitterGeoinformatica10.1007/s10707-016-0258-x21:2(365-388)Online publication date: 24-Dec-2018
https://dl.acm.org/doi/10.1007/s10707-016-0258-x
Wei HSankaranarayanan JSamet H(2017)Finding and Tracking Local Twitter Users for News DetectionProceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems10.1145/3139958.3141797(1-4)Online publication date: 7-Nov-2017
https://dl.acm.org/doi/10.1145/3139958.3141797
Wang XZhang YZhang WLin X(2016)Efficient Identification of Local Keyword Patterns in Microblogging PlatformsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2016.257833028:10(2621-2634)Online publication date: 1-Oct-2016
https://dl.acm.org/doi/10.1109/TKDE.2016.2578330
Zhu ZShou LChen K(2016)Get into the spirit of a location by mining user-generated traveloguesNeurocomputing10.1016/j.neucom.2015.04.129204:C(61-69)Online publication date: 5-Sep-2016
https://dl.acm.org/doi/10.1016/j.neucom.2015.04.129
Jongman BWagemaker JRomero Bde Perez E(2015)Early Flood Detection for Rapid Humanitarian Response: Harnessing Near Real-Time Satellite and Twitter SignalsISPRS International Journal of Geo-Information10.3390/ijgi40422464:4(2246-2266)Online publication date: 23-Oct-2015
https://doi.org/10.3390/ijgi4042246

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