Incremental and adaptive topic detection over social media

Konstantinos Giannakopoulos; Lei Chen

doi:10.1007/978-3-319-91452-7_30

Incremental and adaptive topic detection over social media

Konstantinos Giannakopoulos^*, Lei Chen

^*Corresponding author for this work

Department of Computer Science and Engineering

Research output: Chapter in Book/Conference Proceeding/Report › Conference Paper published in a book › peer-review

5 Citations (Scopus)

Abstract

Social media like Twitter and Facebook are very popular nowadays for sharing users’ interests. However, the existing solutions on topic detection over social media overlook time and location factors, which are quite important and useful. Moreover, social media are frequently updated. Thus, the proposed detection model should handle the dynamic updates. In this paper, we introduce a topic model for topic detection that combines time and location. Our model is equipped with incremental estimation of the parameters of the topic model and adaptive window length according to the correlation of consecutive windows and their density. We have conducted extensive experiments to verify the effectiveness and efficiency of our proposed Incremental Adaptive Time Location (IncrAdapTL) model.

Original language	English
Title of host publication	Database Systems for Advanced Applications - 23rd International Conference, DASFAA 2018, Proceedings
Editors	Yannis Manolopoulos, Jianxin Li, Shazia Sadiq, Jian Pei
Publisher	Springer Verlag
Pages	460-473
Number of pages	14
ISBN (Print)	9783319914510
DOIs	https://doi.org/10.1007/978-3-319-91452-7_30
Publication status	Published - 2018
Event	23rd International Conference on Database Systems for Advanced Applications, DASFAA 2018 - Gold Coast, Australia Duration: 21 May 2018 → 24 May 2018

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10827 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	23rd International Conference on Database Systems for Advanced Applications, DASFAA 2018
Country/Territory	Australia
City	Gold Coast
Period	21/05/18 → 24/05/18

Bibliographical note

Publisher Copyright:
© Springer International Publishing AG, part of Springer Nature 2018.

Access to Document

10.1007/978-3-319-91452-7_30

Cite this

Giannakopoulos, K., & Chen, L. (2018). Incremental and adaptive topic detection over social media. In Y. Manolopoulos, J. Li, S. Sadiq, & J. Pei (Eds.), Database Systems for Advanced Applications - 23rd International Conference, DASFAA 2018, Proceedings (pp. 460-473). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10827 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-91452-7_30

Giannakopoulos, Konstantinos ; Chen, Lei. / Incremental and adaptive topic detection over social media. Database Systems for Advanced Applications - 23rd International Conference, DASFAA 2018, Proceedings. editor / Yannis Manolopoulos ; Jianxin Li ; Shazia Sadiq ; Jian Pei. Springer Verlag, 2018. pp. 460-473 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Incremental and adaptive topic detection over social media",

abstract = "Social media like Twitter and Facebook are very popular nowadays for sharing users{\textquoteright} interests. However, the existing solutions on topic detection over social media overlook time and location factors, which are quite important and useful. Moreover, social media are frequently updated. Thus, the proposed detection model should handle the dynamic updates. In this paper, we introduce a topic model for topic detection that combines time and location. Our model is equipped with incremental estimation of the parameters of the topic model and adaptive window length according to the correlation of consecutive windows and their density. We have conducted extensive experiments to verify the effectiveness and efficiency of our proposed Incremental Adaptive Time Location (IncrAdapTL) model.",

author = "Konstantinos Giannakopoulos and Lei Chen",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG, part of Springer Nature 2018.; 23rd International Conference on Database Systems for Advanced Applications, DASFAA 2018 ; Conference date: 21-05-2018 Through 24-05-2018",

year = "2018",

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Giannakopoulos, K & Chen, L 2018, Incremental and adaptive topic detection over social media. in Y Manolopoulos, J Li, S Sadiq & J Pei (eds), Database Systems for Advanced Applications - 23rd International Conference, DASFAA 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10827 LNCS, Springer Verlag, pp. 460-473, 23rd International Conference on Database Systems for Advanced Applications, DASFAA 2018, Gold Coast, Australia, 21/05/18. https://doi.org/10.1007/978-3-319-91452-7_30

Incremental and adaptive topic detection over social media. / Giannakopoulos, Konstantinos; Chen, Lei.
Database Systems for Advanced Applications - 23rd International Conference, DASFAA 2018, Proceedings. ed. / Yannis Manolopoulos; Jianxin Li; Shazia Sadiq; Jian Pei. Springer Verlag, 2018. p. 460-473 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10827 LNCS).

Research output: Chapter in Book/Conference Proceeding/Report › Conference Paper published in a book › peer-review

TY - GEN

T1 - Incremental and adaptive topic detection over social media

AU - Giannakopoulos, Konstantinos

AU - Chen, Lei

N1 - Publisher Copyright: © Springer International Publishing AG, part of Springer Nature 2018.

PY - 2018

Y1 - 2018

N2 - Social media like Twitter and Facebook are very popular nowadays for sharing users’ interests. However, the existing solutions on topic detection over social media overlook time and location factors, which are quite important and useful. Moreover, social media are frequently updated. Thus, the proposed detection model should handle the dynamic updates. In this paper, we introduce a topic model for topic detection that combines time and location. Our model is equipped with incremental estimation of the parameters of the topic model and adaptive window length according to the correlation of consecutive windows and their density. We have conducted extensive experiments to verify the effectiveness and efficiency of our proposed Incremental Adaptive Time Location (IncrAdapTL) model.

AB - Social media like Twitter and Facebook are very popular nowadays for sharing users’ interests. However, the existing solutions on topic detection over social media overlook time and location factors, which are quite important and useful. Moreover, social media are frequently updated. Thus, the proposed detection model should handle the dynamic updates. In this paper, we introduce a topic model for topic detection that combines time and location. Our model is equipped with incremental estimation of the parameters of the topic model and adaptive window length according to the correlation of consecutive windows and their density. We have conducted extensive experiments to verify the effectiveness and efficiency of our proposed Incremental Adaptive Time Location (IncrAdapTL) model.

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UR - https://openalex.org/W2799611686

UR - https://www.scopus.com/pages/publications/85048056742

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M3 - Conference Paper published in a book

SN - 9783319914510

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 460

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BT - Database Systems for Advanced Applications - 23rd International Conference, DASFAA 2018, Proceedings

A2 - Manolopoulos, Yannis

A2 - Li, Jianxin

A2 - Sadiq, Shazia

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PB - Springer Verlag

T2 - 23rd International Conference on Database Systems for Advanced Applications, DASFAA 2018

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Giannakopoulos K, Chen L. Incremental and adaptive topic detection over social media. In Manolopoulos Y, Li J, Sadiq S, Pei J, editors, Database Systems for Advanced Applications - 23rd International Conference, DASFAA 2018, Proceedings. Springer Verlag. 2018. p. 460-473. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-91452-7_30

Incremental and adaptive topic detection over social media

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