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  Successive Cohorts of Twitter Users Show Increasing Activity and Shrinking Content Horizons

Wolf, F., Lehmann, S., & Lorenz-Spreen, P. (2022). Successive Cohorts of Twitter Users Show Increasing Activity and Shrinking Content Horizons. Journal of Quantitative Description, 2. doi:10.51685/jqd.2022.014.

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資料種別: 学術論文

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27215oa.pdf (出版社版), 2MB
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27215oa.pdf
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 作成者:
Wolf, Frederik1, 著者              
Lehmann, Sune2, 著者
Lorenz-Spreen, Philipp 2, 著者
所属:
1Potsdam Institute for Climate Impact Research, ou_persistent13              
2External Organizations, ou_persistent22              

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 要旨: The global public sphere has changed dramatically over the past decades: A significant part of public discourse now takes place on algorithmically driven platforms. Despite its growing importance, there is scant large-scale academic research on the long-term evolution of user behaviour on these platforms. Here, we evaluate the behaviour of 600,000 individual Twitter users between 2012 and 2019 and find empirical evidence for a cohort-level acceleration of the way Twitter is used. Across time, we observe changing user-level behaviours: more tweets per time, denser interactions with others via retweets, and shorter content horizons, expressed as an individual's decaying autocorrelation of topics over time. We show that the change in usage patterns is not simply caused by a growing user base. While behaviour remains remarkably stable within each cohort over time, we relate these observations to changing compositions of new users with each new cohort containing increasingly active individuals. Our findings complement recent empirical work on social acceleration by tracking cohorts over time, controlling for cohort size, and analyzing their behavioural composition.

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言語: eng - 英語
 日付: 2022-06-152022-07-022022-07-02
 出版の状態: Finally published
 ページ: 37
 出版情報: -
 目次: -
 査読: 査読あり
 識別子(DOI, ISBNなど): MDB-ID: yes - 3351
PIKDOMAIN: RD4 - Complexity Science
Organisational keyword: RD4 - Complexity Science
Research topic keyword: Complex Networks
Model / method: Quantitative Methods
Model / method: Nonlinear Data Analysis
Regional keyword: Global
Working Group: Network- and machine-learning-based prediction of extreme events
OATYPE: Gold Open Access
DOI: 10.51685/jqd.2022.014
 学位: -

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出版物 1

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出版物名: Journal of Quantitative Description
種別: 学術雑誌, other
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出版社, 出版地: -
ページ: - 巻号: 2 通巻号: - 開始・終了ページ: - 識別子(ISBN, ISSN, DOIなど): CoNE: https://publications.pik-potsdam.de/cone/journals/resource/journal-of-quantitative-description
Publisher: Universität Zürich