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Facing the volatility of tweets in altmetric research

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  • Zhichao Fang
  • Jonathan Dudek
  • Rodrigo Costas

Abstract

The data re‐collection for tweets from data snapshots is a common methodological step in Twitter‐based research. Understanding better the volatility of tweets over time is important for validating the reliability of metrics based on Twitter data. We tracked a set of 37,918 original scholarly tweets mentioning COVID‐19‐related research daily for 56 days and captured the reasons for the changes in their availability over time. Results show that the proportion of unavailable tweets increased from 1.6 to 2.6% in the time window observed. Of the 1,323 tweets that became unavailable at some point in the period observed, 30.5% became available again afterwards. “Revived” tweets resulted mainly from the unprotecting, reactivating, or unsuspending of users' accounts. Our findings highlight the importance of noting this dynamic nature of Twitter data in altmetric research and testify to the challenges that this poses for the retrieval, processing, and interpretation of Twitter data about scientific papers.

Suggested Citation

  • Zhichao Fang & Jonathan Dudek & Rodrigo Costas, 2022. "Facing the volatility of tweets in altmetric research," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 73(8), pages 1192-1195, August.
    • Handle: RePEc:bla:jinfst:v:73:y:2022:i:8:p:1192-1195
    DOI: 10.1002/asi.24624
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    References listed on IDEAS

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    1. Arkaitz Zubiaga, 2018. "A longitudinal assessment of the persistence of twitter datasets," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 69(8), pages 974-984, August.
    2. Zhichao Fang & Jonathan Dudek & Rodrigo Costas, 2020. "The stability of Twitter metrics: A study on unavailable Twitter mentions of scientific publications," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 71(12), pages 1455-1469, December.
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    Cited by:

    1. Zhichao Fang & Rodrigo Costas & Paul Wouters, 2022. "User engagement with scholarly tweets of scientific papers: a large-scale and cross-disciplinary analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(8), pages 4523-4546, August.
    2. Houqiang Yu & Yue Wang & Shah Hussain & Haoyang Song, 2023. "Towards a better understanding of Facebook Altmetrics in LIS field: assessing the characteristics of involved paper, user and post," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(5), pages 3147-3170, May.

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