Query Oriented Topical Clusters Detection for Top-k Trending Topics in Twitter

Md Shoaib Ahmed; Tanjim Taharat Aurpa; Md Musfique Anwar

doi:10.1109/R10-HTC49770.2020.9357047

Query Oriented Topical Clusters Detection for Top-k Trending Topics in Twitter

Md Shoaib Ahmed, Tanjim Taharat Aurpa, Md Musfique Anwar

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

2 Scopus citations

Abstract

This paper tackles the problem of detecting temporal query oriented topical clusters for top-k trending topics from Twitter in real time. There is an increasing demand to identify and cluster set of users who have similar topical interests as well as certain level of activeness on those topics. Most existing approaches focus on the contents generated by the social users and/or link structure of the underlying social network. However, the degree of users' topical activeness has not been thoroughly studied to identify its effect on the formation of topical clusters. This research investigates on how online social users' behaviors and topical activeness vary with time and how these parameters can be employed in order to improve the quality of the detected topical clusters for top-k trending topics at different time intervals. The effectiveness of our proposed activity biased weight methodology is justified using a benchmark Twitter dataset.

Original language	English
Title of host publication	Proceedings of 2020 IEEE 8th R10 Humanitarian Technology Conference, R10-HTC 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728111100
DOIs	https://doi.org/10.1109/R10-HTC49770.2020.9357047
State	Published - 1 Dec 2020
Externally published	Yes
Event	8th IEEE R10 Humanitarian Technology Conference, R10-HTC 2020 - Kuching, Malaysia Duration: 1 Dec 2020 → 3 Dec 2020

Publication series

Name	IEEE Region 10 Humanitarian Technology Conference, R10-HTC
Volume	2020-December
ISSN (Print)	2572-7621

Conference

Conference	8th IEEE R10 Humanitarian Technology Conference, R10-HTC 2020
Country/Territory	Malaysia
City	Kuching
Period	1/12/20 → 3/12/20

Bibliographical note

Publisher Copyright:
© 2020 IEEE.

Keywords

Active user
Topical clusters
Trending topics

ASJC Scopus subject areas

Waste Management and Disposal
Development
Geography, Planning and Development
Pollution
Renewable Energy, Sustainability and the Environment
Environmental Engineering

Access to Document

10.1109/R10-HTC49770.2020.9357047

Cite this

Ahmed, M. S., Aurpa, T. T., & Anwar, M. M. (2020). Query Oriented Topical Clusters Detection for Top-k Trending Topics in Twitter. In Proceedings of 2020 IEEE 8th R10 Humanitarian Technology Conference, R10-HTC 2020 Article 9357047 (IEEE Region 10 Humanitarian Technology Conference, R10-HTC; Vol. 2020-December). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/R10-HTC49770.2020.9357047

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title = "Query Oriented Topical Clusters Detection for Top-k Trending Topics in Twitter",

abstract = "This paper tackles the problem of detecting temporal query oriented topical clusters for top-k trending topics from Twitter in real time. There is an increasing demand to identify and cluster set of users who have similar topical interests as well as certain level of activeness on those topics. Most existing approaches focus on the contents generated by the social users and/or link structure of the underlying social network. However, the degree of users' topical activeness has not been thoroughly studied to identify its effect on the formation of topical clusters. This research investigates on how online social users' behaviors and topical activeness vary with time and how these parameters can be employed in order to improve the quality of the detected topical clusters for top-k trending topics at different time intervals. The effectiveness of our proposed activity biased weight methodology is justified using a benchmark Twitter dataset.",

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doi = "10.1109/R10-HTC49770.2020.9357047",

language = "English",

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Ahmed, MS, Aurpa, TT & Anwar, MM 2020, Query Oriented Topical Clusters Detection for Top-k Trending Topics in Twitter. in Proceedings of 2020 IEEE 8th R10 Humanitarian Technology Conference, R10-HTC 2020., 9357047, IEEE Region 10 Humanitarian Technology Conference, R10-HTC, vol. 2020-December, Institute of Electrical and Electronics Engineers Inc., 8th IEEE R10 Humanitarian Technology Conference, R10-HTC 2020, Kuching, Malaysia, 1/12/20. https://doi.org/10.1109/R10-HTC49770.2020.9357047

Query Oriented Topical Clusters Detection for Top-k Trending Topics in Twitter. / Ahmed, Md Shoaib; Aurpa, Tanjim Taharat; Anwar, Md Musfique.
Proceedings of 2020 IEEE 8th R10 Humanitarian Technology Conference, R10-HTC 2020. Institute of Electrical and Electronics Engineers Inc., 2020. 9357047 (IEEE Region 10 Humanitarian Technology Conference, R10-HTC; Vol. 2020-December).

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

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AB - This paper tackles the problem of detecting temporal query oriented topical clusters for top-k trending topics from Twitter in real time. There is an increasing demand to identify and cluster set of users who have similar topical interests as well as certain level of activeness on those topics. Most existing approaches focus on the contents generated by the social users and/or link structure of the underlying social network. However, the degree of users' topical activeness has not been thoroughly studied to identify its effect on the formation of topical clusters. This research investigates on how online social users' behaviors and topical activeness vary with time and how these parameters can be employed in order to improve the quality of the detected topical clusters for top-k trending topics at different time intervals. The effectiveness of our proposed activity biased weight methodology is justified using a benchmark Twitter dataset.

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Query Oriented Topical Clusters Detection for Top-k Trending Topics in Twitter

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this