Circadian Patterns in Twitter

M.C. ten Thij; P. Kampstra; S. Bhulai

Circadian Patterns in Twitter

M.C. ten Thij, P. Kampstra, S. Bhulai

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

Abstract

In this paper, we study activity on the microblogging platform Twitter. We analyse two separate aspects of activity on Twitter. First, we analyse the daily and weekly number of posts, through which we find clear circadian (daily) patterns emerging in the use of Twitter for multiple languages. We see that both the number of tweets and the daily and weekly activity patterns differ between languages. Second, we analyse the progression of individual tweets through retweets in the Twittersphere. We find that the size of these progressions follow a power-law distribution. Furthermore, we build an algorithm to analyse the actual structure of the progressions and use this algorithm on a limited set of tweets. We find that retweet trees show a star-like structure.

Original language	English
Title of host publication	3rd International Conference, IARIA Data Analytics 2014, Rome, Italy, August 24-28, 2014, Proceedings
Editors	F. Laux, P.M. Pardalos, A. Crolotte
Publisher	IARIA
Pages	12-17
ISBN (Print)	9781612083582
Publication status	Published - 2014
Event	IARIA DATA ANALYTICS 2014 - Duration: 24 Aug 2014 → 28 Aug 2014

Conference

Conference	IARIA DATA ANALYTICS 2014
Period	24/08/14 → 28/08/14

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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@inproceedings{8be26f1624d14ae4af42a530aaf315da,

title = "Circadian Patterns in Twitter",

abstract = "In this paper, we study activity on the microblogging platform Twitter. We analyse two separate aspects of activity on Twitter. First, we analyse the daily and weekly number of posts, through which we find clear circadian (daily) patterns emerging in the use of Twitter for multiple languages. We see that both the number of tweets and the daily and weekly activity patterns differ between languages. Second, we analyse the progression of individual tweets through retweets in the Twittersphere. We find that the size of these progressions follow a power-law distribution. Furthermore, we build an algorithm to analyse the actual structure of the progressions and use this algorithm on a limited set of tweets. We find that retweet trees show a star-like structure.",

author = "{ten Thij}, M.C. and P. Kampstra and S. Bhulai",

year = "2014",

language = "English",

isbn = "9781612083582",

pages = "12--17",

editor = "F. Laux and P.M. Pardalos and A. Crolotte",

booktitle = "3rd International Conference, IARIA Data Analytics 2014, Rome, Italy, August 24-28, 2014, Proceedings",

publisher = "IARIA",

note = "IARIA DATA ANALYTICS 2014 ; Conference date: 24-08-2014 Through 28-08-2014",

}

TY - GEN

T1 - Circadian Patterns in Twitter

AU - ten Thij, M.C.

AU - Kampstra, P.

AU - Bhulai, S.

PY - 2014

Y1 - 2014

N2 - In this paper, we study activity on the microblogging platform Twitter. We analyse two separate aspects of activity on Twitter. First, we analyse the daily and weekly number of posts, through which we find clear circadian (daily) patterns emerging in the use of Twitter for multiple languages. We see that both the number of tweets and the daily and weekly activity patterns differ between languages. Second, we analyse the progression of individual tweets through retweets in the Twittersphere. We find that the size of these progressions follow a power-law distribution. Furthermore, we build an algorithm to analyse the actual structure of the progressions and use this algorithm on a limited set of tweets. We find that retweet trees show a star-like structure.

AB - In this paper, we study activity on the microblogging platform Twitter. We analyse two separate aspects of activity on Twitter. First, we analyse the daily and weekly number of posts, through which we find clear circadian (daily) patterns emerging in the use of Twitter for multiple languages. We see that both the number of tweets and the daily and weekly activity patterns differ between languages. Second, we analyse the progression of individual tweets through retweets in the Twittersphere. We find that the size of these progressions follow a power-law distribution. Furthermore, we build an algorithm to analyse the actual structure of the progressions and use this algorithm on a limited set of tweets. We find that retweet trees show a star-like structure.

M3 - Conference contribution

SN - 9781612083582

SP - 12

EP - 17

BT - 3rd International Conference, IARIA Data Analytics 2014, Rome, Italy, August 24-28, 2014, Proceedings

A2 - Laux, F.

A2 - Pardalos, P.M.

A2 - Crolotte, A.

PB - IARIA

T2 - IARIA DATA ANALYTICS 2014

Y2 - 24 August 2014 through 28 August 2014

ER -

Circadian Patterns in Twitter

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Conference

UN SDGs

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