Online Topical Clusters Detection for Top-k Trending Topics in Twitter

Md Shoaib Ahmed; Tanjim Taharat Aurpa; Md Musfique Anwar

doi:10.1109/ASONAM49781.2020.9381305

Online 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

10 Scopus citations

Abstract

This paper tackles the problem of detecting temporal query oriented topical clusters for top-k trending topics from Twitter. 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 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 the 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 the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020
Editors	Martin Atzmuller, Michele Coscia, Rokia Missaoui
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	573-577
Number of pages	5
ISBN (Electronic)	9781728110561
DOIs	https://doi.org/10.1109/ASONAM49781.2020.9381305
State	Published - 7 Dec 2020
Externally published	Yes
Event	12th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020 - Virtual, Online, Netherlands Duration: 7 Dec 2020 → 10 Dec 2020

Publication series

Name	Proceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020

Conference

Conference	12th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020
Country/Territory	Netherlands
City	Virtual, Online
Period	7/12/20 → 10/12/20

Bibliographical note

Publisher Copyright:
© 2020 IEEE.

Keywords

Active user
Topical clusters
Trending topics

ASJC Scopus subject areas

Artificial Intelligence
Computer Networks and Communications
Information Systems and Management
Social Psychology
Communication

Access to Document

10.1109/ASONAM49781.2020.9381305

Cite this

Ahmed, M. S., Aurpa, T. T., & Anwar, M. M. (2020). Online Topical Clusters Detection for Top-k Trending Topics in Twitter. In M. Atzmuller, M. Coscia, & R. Missaoui (Eds.), Proceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020 (pp. 573-577). Article 9381305 (Proceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ASONAM49781.2020.9381305

Ahmed, Md Shoaib ; Aurpa, Tanjim Taharat ; Anwar, Md Musfique. / Online Topical Clusters Detection for Top-k Trending Topics in Twitter. Proceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020. editor / Martin Atzmuller ; Michele Coscia ; Rokia Missaoui. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 573-577 (Proceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020).

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title = "Online 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. 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 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 the 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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Ahmed, MS, Aurpa, TT & Anwar, MM 2020, Online Topical Clusters Detection for Top-k Trending Topics in Twitter. in M Atzmuller, M Coscia & R Missaoui (eds), Proceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020., 9381305, Proceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020, Institute of Electrical and Electronics Engineers Inc., pp. 573-577, 12th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020, Virtual, Online, Netherlands, 7/12/20. https://doi.org/10.1109/ASONAM49781.2020.9381305

Online Topical Clusters Detection for Top-k Trending Topics in Twitter. / Ahmed, Md Shoaib; Aurpa, Tanjim Taharat; Anwar, Md Musfique.
Proceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020. ed. / Martin Atzmuller; Michele Coscia; Rokia Missaoui. Institute of Electrical and Electronics Engineers Inc., 2020. p. 573-577 9381305 (Proceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020).

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. 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 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 the 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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Ahmed MS, Aurpa TT, Anwar MM. Online Topical Clusters Detection for Top-k Trending Topics in Twitter. In Atzmuller M, Coscia M, Missaoui R, editors, Proceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020. Institute of Electrical and Electronics Engineers Inc. 2020. p. 573-577. 9381305. (Proceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020). doi: 10.1109/ASONAM49781.2020.9381305

Online 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