A retweet prediction of Thai tweets

Rangsipan Marukatat

doi:10.1109/IMCEC.2016.7867361

A retweet prediction of Thai tweets

Rangsipan Marukatat

Department of Computer Engineering

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

3 Citations (Scopus)

Abstract

Predicting whether a tweet will get a large number of retweets, i.e. to become viral, has been an interest in Twitter research. This paper presents the retweet prediction of tweets by Thai users. Sixteen attributes were used for the prediction. Based on experimental results, decision tree classifier performed better than neural network and Naïve Bayes. Its accuracy when using status-related attributes was 84.3% while that when using user-related attributes was as high as 98.1%. Individual attributes were further evaluated by using gain ratio and ReliefF criteria. They ranked the following to be helpful to the prediction: public list memberships, the number of followers, account longivity, day and time of tweeting, and embedded media.

Original language	English
Title of host publication	Proceedings of 2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2016
Editors	Bing Xu
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1000-1003
Number of pages	4
ISBN (Electronic)	9781467396127
DOIs	https://doi.org/10.1109/IMCEC.2016.7867361
Publication status	Published - 28 Feb 2017
Event	2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2016 - Xi'an, China Duration: 3 Oct 2016 → 5 Oct 2016

Publication series

Name	Proceedings of 2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2016

Conference

Conference	2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2016
Country/Territory	China
City	Xi'an
Period	3/10/16 → 5/10/16

Keywords

Prediction
Retweet
Social network
Twitter

Access to Document

10.1109/IMCEC.2016.7867361

Cite this

Marukatat, R. (2017). A retweet prediction of Thai tweets. In B. Xu (Ed.), Proceedings of 2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2016 (pp. 1000-1003). Article 7867361 (Proceedings of 2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IMCEC.2016.7867361

Marukatat, Rangsipan. / A retweet prediction of Thai tweets. Proceedings of 2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2016. editor / Bing Xu. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 1000-1003 (Proceedings of 2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2016).

@inproceedings{1e15b9d9f0804658b23633ddd4ce4ad4,

title = "A retweet prediction of Thai tweets",

abstract = "Predicting whether a tweet will get a large number of retweets, i.e. to become viral, has been an interest in Twitter research. This paper presents the retweet prediction of tweets by Thai users. Sixteen attributes were used for the prediction. Based on experimental results, decision tree classifier performed better than neural network and Na{\"i}ve Bayes. Its accuracy when using status-related attributes was 84.3\% while that when using user-related attributes was as high as 98.1\%. Individual attributes were further evaluated by using gain ratio and ReliefF criteria. They ranked the following to be helpful to the prediction: public list memberships, the number of followers, account longivity, day and time of tweeting, and embedded media.",

keywords = "Prediction, Retweet, Social network, Twitter",

author = "Rangsipan Marukatat",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2016 ; Conference date: 03-10-2016 Through 05-10-2016",

year = "2017",

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doi = "10.1109/IMCEC.2016.7867361",

language = "English",

series = "Proceedings of 2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2016",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1000--1003",

editor = "Bing Xu",

booktitle = "Proceedings of 2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2016",

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Marukatat, R 2017, A retweet prediction of Thai tweets. in B Xu (ed.), Proceedings of 2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2016., 7867361, Proceedings of 2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2016, Institute of Electrical and Electronics Engineers Inc., pp. 1000-1003, 2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2016, Xi'an, China, 3/10/16. https://doi.org/10.1109/IMCEC.2016.7867361

A retweet prediction of Thai tweets. / Marukatat, Rangsipan.
Proceedings of 2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2016. ed. / Bing Xu. Institute of Electrical and Electronics Engineers Inc., 2017. p. 1000-1003 7867361 (Proceedings of 2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2016).

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

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AU - Marukatat, Rangsipan

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N2 - Predicting whether a tweet will get a large number of retweets, i.e. to become viral, has been an interest in Twitter research. This paper presents the retweet prediction of tweets by Thai users. Sixteen attributes were used for the prediction. Based on experimental results, decision tree classifier performed better than neural network and Naïve Bayes. Its accuracy when using status-related attributes was 84.3% while that when using user-related attributes was as high as 98.1%. Individual attributes were further evaluated by using gain ratio and ReliefF criteria. They ranked the following to be helpful to the prediction: public list memberships, the number of followers, account longivity, day and time of tweeting, and embedded media.

AB - Predicting whether a tweet will get a large number of retweets, i.e. to become viral, has been an interest in Twitter research. This paper presents the retweet prediction of tweets by Thai users. Sixteen attributes were used for the prediction. Based on experimental results, decision tree classifier performed better than neural network and Naïve Bayes. Its accuracy when using status-related attributes was 84.3% while that when using user-related attributes was as high as 98.1%. Individual attributes were further evaluated by using gain ratio and ReliefF criteria. They ranked the following to be helpful to the prediction: public list memberships, the number of followers, account longivity, day and time of tweeting, and embedded media.

KW - Prediction

KW - Retweet

KW - Social network

KW - Twitter

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Marukatat R. A retweet prediction of Thai tweets. In Xu B, editor, Proceedings of 2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2016. Institute of Electrical and Electronics Engineers Inc. 2017. p. 1000-1003. 7867361. (Proceedings of 2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2016). doi: 10.1109/IMCEC.2016.7867361

A retweet prediction of Thai tweets

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Keywords

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