Empirical Analysis of Factors Influencing Twitter Hashtag Recommendation on Detected Communities

Research output: Chapter in Book/Conference paperConference paper

3 Citations (Scopus)

Abstract

Due to the limited length of tweets, hashtags are often used by users in their tweets. Thus, hashtag recommendation is highly desirable for users in Twitter to find useful hashtags when they type in tweets. However, there are many factors that may affect the effectiveness of hashtag recommendation, which includes social relationships, textual information and user profiling based on hashtag preference. In this paper, we aim to analyse the effect of these factors in hashtag recommendation on the detected communities in Twitter. In details, we seek answers to the two questions: What is the most significant factor in recommending hashtags in the context of detected communities? How the different community detection algorithms and the size of the communities affect the performance of hashtag recommendation?

To answer these questions, we detect the communities using two algorithms: Breadth First Search (BFS) and Clique Percolation Method (CPM). On the randomly detected communities, we investigate the quality and the behaviour of the recommended hashtags people consumed. From the extensive experimental results, we have the following conclusions. First, social factor is the most significant factor along with the textual factor for hashtag recommendation. Second, we find that the quality of the hashtag recommendation in the community detected using CPM clearly outperforms that using BFS. Third, incorporating user profiling increases the quality of the recommended hashtags.
Original languageEnglish
Title of host publicationAdvanced Data Mining and Applications
Subtitle of host publication13th International Conference, ADMA 2017 Singapore, November 5–6, 2017 Proceedings
EditorsGao Cong, Wei-Chih Peng, Wei Emma Zhang, Chengliang Li, Aixin Sun
Place of PublicationCham
PublisherSpringer
Pages119-131
Number of pages13
ISBN (Electronic)9783319691794
ISBN (Print)9783319691787
DOIs
Publication statusPublished - 2017
Event13th International Conference on Advanced Data Mining and Applications, ADMA 2017 - Singapore, Singapore
Duration: 5 Nov 20176 Nov 2017

Publication series

NameLecture Notes in Artificial Intelligence
PublisherSpringer Nature
Volume10604
ISSN (Print)0302-9743

Conference

Conference13th International Conference on Advanced Data Mining and Applications, ADMA 2017
CountrySingapore
CitySingapore
Period5/11/176/11/17

Cite this

Alsini, A., Datta, A., Li, J., & Huynh, D. (2017). Empirical Analysis of Factors Influencing Twitter Hashtag Recommendation on Detected Communities. In G. Cong, W-C. Peng, W. E. Zhang, C. Li, & A. Sun (Eds.), Advanced Data Mining and Applications : 13th International Conference, ADMA 2017 Singapore, November 5–6, 2017 Proceedings (pp. 119-131). (Lecture Notes in Artificial Intelligence; Vol. 10604). Cham: Springer. https://doi.org/10.1007/978-3-319-69179-4_9
Alsini, Areej ; Datta, Amitava ; Li, Jianxin ; Huynh, Du. / Empirical Analysis of Factors Influencing Twitter Hashtag Recommendation on Detected Communities. Advanced Data Mining and Applications : 13th International Conference, ADMA 2017 Singapore, November 5–6, 2017 Proceedings. editor / Gao Cong ; Wei-Chih Peng ; Wei Emma Zhang ; Chengliang Li ; Aixin Sun. Cham : Springer, 2017. pp. 119-131 (Lecture Notes in Artificial Intelligence).
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title = "Empirical Analysis of Factors Influencing Twitter Hashtag Recommendation on Detected Communities",
abstract = "Due to the limited length of tweets, hashtags are often used by users in their tweets. Thus, hashtag recommendation is highly desirable for users in Twitter to find useful hashtags when they type in tweets. However, there are many factors that may affect the effectiveness of hashtag recommendation, which includes social relationships, textual information and user profiling based on hashtag preference. In this paper, we aim to analyse the effect of these factors in hashtag recommendation on the detected communities in Twitter. In details, we seek answers to the two questions: What is the most significant factor in recommending hashtags in the context of detected communities? How the different community detection algorithms and the size of the communities affect the performance of hashtag recommendation?To answer these questions, we detect the communities using two algorithms: Breadth First Search (BFS) and Clique Percolation Method (CPM). On the randomly detected communities, we investigate the quality and the behaviour of the recommended hashtags people consumed. From the extensive experimental results, we have the following conclusions. First, social factor is the most significant factor along with the textual factor for hashtag recommendation. Second, we find that the quality of the hashtag recommendation in the community detected using CPM clearly outperforms that using BFS. Third, incorporating user profiling increases the quality of the recommended hashtags.",
author = "Areej Alsini and Amitava Datta and Jianxin Li and Du Huynh",
year = "2017",
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Alsini, A, Datta, A, Li, J & Huynh, D 2017, Empirical Analysis of Factors Influencing Twitter Hashtag Recommendation on Detected Communities. in G Cong, W-C Peng, WE Zhang, C Li & A Sun (eds), Advanced Data Mining and Applications : 13th International Conference, ADMA 2017 Singapore, November 5–6, 2017 Proceedings. Lecture Notes in Artificial Intelligence, vol. 10604, Springer, Cham, pp. 119-131, 13th International Conference on Advanced Data Mining and Applications, ADMA 2017, Singapore, Singapore, 5/11/17. https://doi.org/10.1007/978-3-319-69179-4_9

Empirical Analysis of Factors Influencing Twitter Hashtag Recommendation on Detected Communities. / Alsini, Areej; Datta, Amitava; Li, Jianxin; Huynh, Du.

Advanced Data Mining and Applications : 13th International Conference, ADMA 2017 Singapore, November 5–6, 2017 Proceedings. ed. / Gao Cong; Wei-Chih Peng; Wei Emma Zhang; Chengliang Li; Aixin Sun. Cham : Springer, 2017. p. 119-131 (Lecture Notes in Artificial Intelligence; Vol. 10604).

Research output: Chapter in Book/Conference paperConference paper

TY - GEN

T1 - Empirical Analysis of Factors Influencing Twitter Hashtag Recommendation on Detected Communities

AU - Alsini, Areej

AU - Datta, Amitava

AU - Li, Jianxin

AU - Huynh, Du

PY - 2017

Y1 - 2017

N2 - Due to the limited length of tweets, hashtags are often used by users in their tweets. Thus, hashtag recommendation is highly desirable for users in Twitter to find useful hashtags when they type in tweets. However, there are many factors that may affect the effectiveness of hashtag recommendation, which includes social relationships, textual information and user profiling based on hashtag preference. In this paper, we aim to analyse the effect of these factors in hashtag recommendation on the detected communities in Twitter. In details, we seek answers to the two questions: What is the most significant factor in recommending hashtags in the context of detected communities? How the different community detection algorithms and the size of the communities affect the performance of hashtag recommendation?To answer these questions, we detect the communities using two algorithms: Breadth First Search (BFS) and Clique Percolation Method (CPM). On the randomly detected communities, we investigate the quality and the behaviour of the recommended hashtags people consumed. From the extensive experimental results, we have the following conclusions. First, social factor is the most significant factor along with the textual factor for hashtag recommendation. Second, we find that the quality of the hashtag recommendation in the community detected using CPM clearly outperforms that using BFS. Third, incorporating user profiling increases the quality of the recommended hashtags.

AB - Due to the limited length of tweets, hashtags are often used by users in their tweets. Thus, hashtag recommendation is highly desirable for users in Twitter to find useful hashtags when they type in tweets. However, there are many factors that may affect the effectiveness of hashtag recommendation, which includes social relationships, textual information and user profiling based on hashtag preference. In this paper, we aim to analyse the effect of these factors in hashtag recommendation on the detected communities in Twitter. In details, we seek answers to the two questions: What is the most significant factor in recommending hashtags in the context of detected communities? How the different community detection algorithms and the size of the communities affect the performance of hashtag recommendation?To answer these questions, we detect the communities using two algorithms: Breadth First Search (BFS) and Clique Percolation Method (CPM). On the randomly detected communities, we investigate the quality and the behaviour of the recommended hashtags people consumed. From the extensive experimental results, we have the following conclusions. First, social factor is the most significant factor along with the textual factor for hashtag recommendation. Second, we find that the quality of the hashtag recommendation in the community detected using CPM clearly outperforms that using BFS. Third, incorporating user profiling increases the quality of the recommended hashtags.

UR - https://link.springer.com/book/10.1007/978-3-319-69179-4

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DO - 10.1007/978-3-319-69179-4_9

M3 - Conference paper

SN - 9783319691787

T3 - Lecture Notes in Artificial Intelligence

SP - 119

EP - 131

BT - Advanced Data Mining and Applications

A2 - Cong, Gao

A2 - Peng, Wei-Chih

A2 - Zhang, Wei Emma

A2 - Li, Chengliang

A2 - Sun, Aixin

PB - Springer

CY - Cham

ER -

Alsini A, Datta A, Li J, Huynh D. Empirical Analysis of Factors Influencing Twitter Hashtag Recommendation on Detected Communities. In Cong G, Peng W-C, Zhang WE, Li C, Sun A, editors, Advanced Data Mining and Applications : 13th International Conference, ADMA 2017 Singapore, November 5–6, 2017 Proceedings. Cham: Springer. 2017. p. 119-131. (Lecture Notes in Artificial Intelligence). https://doi.org/10.1007/978-3-319-69179-4_9