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A longitudinal assessment of the persistence of twitter datasets

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  • Arkaitz Zubiaga

Abstract

Social media datasets are not always completely replicable. Having to adhere to requirements of platforms such as Twitter, researchers can only release a list of unique identifiers, which others can then use to recollect the data themselves. This leads to subsets of the data no longer being available, as content can be deleted or user accounts deactivated. To quantify the long‐term impact of this in the replicability of datasets, we perform a longitudinal analysis of the persistence of 30 Twitter datasets, which include more than 147 million tweets. By recollecting Twitter datasets ranging from 0 to 4 years old by using the tweet IDs, we look at four different factors quantifying the extent to which recollected datasets resemble original ones: completeness, representativity, similarity, and changingness. Although the ratio of available tweets keeps decreasing as the dataset gets older, we find that the textual content of the recollected subset is still largely representative of the original dataset. The representativity of the metadata, however, keeps fading over time, both because the dataset shrinks and because certain metadata, such as the users' number of followers, keeps changing. Our study has important implications for researchers sharing and using publicly shared Twitter datasets in their research.

Suggested Citation

  • 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.
  • Handle: RePEc:bla:jinfst:v:69:y:2018:i:8:p:974-984
    DOI: 10.1002/asi.24026
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    Cited by:

    1. Muhammad Yasir & Sitara Afzal & Khalid Latif & Ghulam Mujtaba Chaudhary & Nazish Yameen Malik & Farhan Shahzad & Oh-young Song, 2020. "An Efficient Deep Learning Based Model to Predict Interest Rate Using Twitter Sentiment," Sustainability, MDPI, vol. 12(4), pages 1-16, February.
    2. Fahd A. Ghanem & M. C. Padma & Ramez Alkhatib, 2023. "Automatic Short Text Summarization Techniques in Social Media Platforms," Future Internet, MDPI, vol. 15(9), pages 1-27, September.
    3. 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.
    4. Libby Hemphill & Margaret L. Hedstrom & Susan Hautaniemi Leonard, 2021. "Saving social media data: Understanding data management practices among social media researchers and their implications for archives," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 72(1), pages 97-109, January.

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