The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
How the Scientific Community Reacts to Newly Submitted Preprints: Article Downloads, Twitter Mentions, and Citations
Open accessAbstract
We analyze the online response to the preprint publication of a cohort of 4,606 scientific articles submitted to the preprint database arXiv.org between October 2010 and May 2011. We study three forms of responses to these preprints: downloads on the arXiv.org site, mentions on the social media site Twitter, and early citations in the scholarly record. We perform two analyses. First, we analyze the delay and time span of article downloads and Twitter mentions following submission, to understand the temporal configuration of these reactions and whether one precedes or follows the other. Second, we run regression and correlation tests to investigate the relationship between Twitter mentions, arXiv downloads, and article citations. We find that Twitter mentions and arXiv downloads of scholarly articles follow two distinct temporal patterns of activity, with Twitter mentions having shorter delays and narrower time spans than arXiv downloads. We also find that the volume of Twitter mentions is statistically correlated with arXiv downloads and early citations just months after the publication of a preprint, with a possible bias that favors highly mentioned articles.
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Cites
Diurnal and seasonal mood vary with work, sleep, and daylength across diverse cultures
Golder, S. A., & Macy, M. W. (2011) · Science · doi:10.1126/science.1202775 · Cited by 1,155 · cited 1×
“l media data have recently been employed to infer social ties based on geographical proximity [1], study how diurnal and seasonal mood patterns match up with the eects of sleep and circadian rhythms [2], infer socio-economic patterns from public mood levels [3], validate Dunbar’s number [4], and even explore how onlin…”
BackgroundEysenbach, G. (2011) · Journal of Medical Internet Research · doi:10.2196/jmir.2012 · Cited by 1,083
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Bollen, J., Mao, H., & Pepe, A. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v5i1.14171 · Cited by 936
Self-citationModeling Users' Activity on Twitter Networks: Validation of Dunbar's Number
Gonçalves, B., Perra, N., & Vespignani, A. (2011) · PLoS ONE · doi:10.1371/journal.pone.0022656 · Cited by 638
Inferring social ties from geographic coincidences
Crandall, D. J., Backstrom, L., Cosley, D., Suri, S., Huttenlocher, D., & Kleinberg, J. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1006155107 · Cited by 473
Earlier Web usage statistics as predictors of later citation impact
Brody, T., Harnad, S., & Carr, L. (2006) · Journal of the American Society for Information Science and Technology · doi:10.1002/asi.20373 · Cited by 402
Scientometrics 2.0: New metrics of scholarly impact on the social Web
Priem, J., & Hemminger, B. H. (2010) · First Monday · doi:10.5210/fm.v15i7.2874 · Cited by 375
How and why scholars cite on Twitter
Priem, J., & Costello, K. L. (2010) · Proceedings of the American Society for Information Science and Technology · doi:10.1002/meet.14504701201 · Cited by 253
Clickstream Data Yields High-Resolution Maps of Science
Bollen, J., Van de Sompel, H., Hagberg, A., Bettencourt, L., Chute, R., Rodriguez, M. A., & Balakireva, L. (2009) · PLoS ONE · doi:10.1371/journal.pone.0004803 · Cited by 208
Self-citationUnderstanding how Twitter is used to spread scientific messages
Letierce, J., Passant, A., Breslin, J. G., & Decker, S. (2010) · Cited by 202
Cited by
Do Altmetrics Work? Twitter and Ten Other Social Web Services
Thelwall, M., Haustein, S., Larivière, V., & Sugimoto, C. R. (2013) · PLoS ONE · doi:10.1371/journal.pone.0064841 · Cited by 907
Tweeting biomedicine: An analysis of tweets and citations in the biomedical literature
Haustein, S., Peters, I., Sugimoto, C. R., Thelwall, M., & Larivière, V. (2014) · Journal of the Association for Information Science and Technology · doi:10.1002/asi.23101 · Cited by 434
Disciplinary differences in Twitter scholarly communication
Holmberg, K., & Thelwall, M. (2014) · Scientometrics · doi:10.1007/s11192-014-1229-3 · Cited by 289
Tweets as impact indicators: Examining the implications of automated “bot” accounts on Twitter
Haustein, S., Bowman, T. D., Holmberg, K., Tsou, A., Sugimoto, C. R., & Larivière, V. (2016) · Journal of the Association for Information Science and Technology · doi:10.1002/asi.23456 · Cited by 231
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Metadata
- Authors
- Shuai, X., Pepe, A., & Bollen, J.
- Venue
- PLoS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2012-11-01
- Volume
- 7
- Issue
- 11
- Pages
- e47523
- ISSN
- 1932-6203 (ISSN-L)
- OpenAlex
- W2159336657
- Cited by
- 293
- References
- 11
- FWCI
- 61.00
- Type
- journal article
- Language
- en
- Open access
- gold
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- Shuai, X., Pepe, A., & Bollen, J. (2012). How the Scientific Community Reacts to Newly Submitted Preprints: Article Downloads, Twitter Mentions, and Citations. PLoS ONE. https://doi.org/10.1371/journal.pone.0047523
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
