Local event discovery from tweets metadata

Mohammed Hasanuzzaman; Andy Way

doi:10.1145/3148011.3154477

Local event discovery from tweets metadata

Mohammed Hasanuzzaman^*, Andy Way

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

We present a two-step strategy that addresses fundamental deficiencies in social media-based event detection and achieves effective local event by taking advantage of geo-located data from Twitter. While previous work has mainly relied on an analysis of tweet text to identify local events, we show how to reliably detect events using meta-data analysis of geo-tagged tweets. The first step of the method identifies several spatio-temporal clusters within the dataset across both space and time using metadata to form potential candidate events. In the second step, it ranks all the candidates by the amount of hashtag/entity inequality. We used crowdsourcing to evaluate the proposed approach on a data set that contains millions of geo-tagged tweets. The results show that our framework performs reasonably well in terms of precision and discovers local events faster.

Original language	English
Title of host publication	K-CAP 2017: Proceedings of the Knowledge Capture Conference
Publisher	Association for Computing Machinery
Number of pages	4
ISBN (Electronic)	9781450355537
DOIs	https://doi.org/10.1145/3148011.3154477
Publication status	Published - 04 Dec 2017
Externally published	Yes
Event	9th International Conference on Knowledge Capture, K-CAP 2017 - Austin, United States Duration: 04 Dec 2017 → 06 Dec 2017

Publication series

Name	Proceedings of the Knowledge Capture Conference

Conference

Conference	9th International Conference on Knowledge Capture, K-CAP 2017
Country/Territory	United States
City	Austin
Period	04/12/2017 → 06/12/2017

Bibliographical note

Funding Information:
The ADAPT Centre for Digital Content Technology is funded under the SFI Research Centres Programme (Grant 13/RC/2106) and is co-funded under the European Regional Development Fund.

Publisher Copyright:
© 2017 Copyright held by the owner/author(s).

ASJC Scopus subject areas

Computational Theory and Mathematics
Software
Computer Science Applications
Information Systems

Access to Document

10.1145/3148011.3154477Licence: Unspecified

Cite this

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title = "Local event discovery from tweets metadata",

abstract = "We present a two-step strategy that addresses fundamental deficiencies in social media-based event detection and achieves effective local event by taking advantage of geo-located data from Twitter. While previous work has mainly relied on an analysis of tweet text to identify local events, we show how to reliably detect events using meta-data analysis of geo-tagged tweets. The first step of the method identifies several spatio-temporal clusters within the dataset across both space and time using metadata to form potential candidate events. In the second step, it ranks all the candidates by the amount of hashtag/entity inequality. We used crowdsourcing to evaluate the proposed approach on a data set that contains millions of geo-tagged tweets. The results show that our framework performs reasonably well in terms of precision and discovers local events faster.",

author = "Mohammed Hasanuzzaman and Andy Way",

note = "Funding Information: The ADAPT Centre for Digital Content Technology is funded under the SFI Research Centres Programme (Grant 13/RC/2106) and is co-funded under the European Regional Development Fund. Publisher Copyright: {\textcopyright} 2017 Copyright held by the owner/author(s).; 9th International Conference on Knowledge Capture, K-CAP 2017 ; Conference date: 04-12-2017 Through 06-12-2017",

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T1 - Local event discovery from tweets metadata

AU - Hasanuzzaman, Mohammed

AU - Way, Andy

N1 - Funding Information: The ADAPT Centre for Digital Content Technology is funded under the SFI Research Centres Programme (Grant 13/RC/2106) and is co-funded under the European Regional Development Fund. Publisher Copyright: © 2017 Copyright held by the owner/author(s).

PY - 2017/12/4

Y1 - 2017/12/4

N2 - We present a two-step strategy that addresses fundamental deficiencies in social media-based event detection and achieves effective local event by taking advantage of geo-located data from Twitter. While previous work has mainly relied on an analysis of tweet text to identify local events, we show how to reliably detect events using meta-data analysis of geo-tagged tweets. The first step of the method identifies several spatio-temporal clusters within the dataset across both space and time using metadata to form potential candidate events. In the second step, it ranks all the candidates by the amount of hashtag/entity inequality. We used crowdsourcing to evaluate the proposed approach on a data set that contains millions of geo-tagged tweets. The results show that our framework performs reasonably well in terms of precision and discovers local events faster.

AB - We present a two-step strategy that addresses fundamental deficiencies in social media-based event detection and achieves effective local event by taking advantage of geo-located data from Twitter. While previous work has mainly relied on an analysis of tweet text to identify local events, we show how to reliably detect events using meta-data analysis of geo-tagged tweets. The first step of the method identifies several spatio-temporal clusters within the dataset across both space and time using metadata to form potential candidate events. In the second step, it ranks all the candidates by the amount of hashtag/entity inequality. We used crowdsourcing to evaluate the proposed approach on a data set that contains millions of geo-tagged tweets. The results show that our framework performs reasonably well in terms of precision and discovers local events faster.

U2 - 10.1145/3148011.3154477

DO - 10.1145/3148011.3154477

M3 - Conference contribution

AN - SCOPUS:85040571127

T3 - Proceedings of the Knowledge Capture Conference

BT - K-CAP 2017: Proceedings of the Knowledge Capture Conference

PB - Association for Computing Machinery

T2 - 9th International Conference on Knowledge Capture, K-CAP 2017

Y2 - 4 December 2017 through 6 December 2017

ER -

Local event discovery from tweets metadata

Abstract

Publication series

Conference

Bibliographical note

ASJC Scopus subject areas

Access to Document

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Cite this