How the Scientific Community Reacts to Newly Submitted Preprints: Article Downloads, Twitter Mentions, and Citations

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.

[1]  Stevan Harnad,et al.  Earlier Web Usage Statistics as Predictors of Later Citation Impact , 2005, J. Assoc. Inf. Sci. Technol..

[2]  Katrin Weller,et al.  Twitter for Scientific Communication: How Can Citations/References be Identified and Measured? , 2011 .

[3]  Bradley M. Hemminger,et al.  Scientometrics 2.0: New metrics of scholarly impact on the social Web , 2010, First Monday.

[4]  Alberto Pepe,et al.  Political Protest Italian-style: The Blogosphere and Mainstream Media in the Promotion and Coverage of Beppe Grillo's V-day , 2009, First Monday.

[5]  Johan Bollen,et al.  Towards usage-based impact metrics: first results from the mesur project. , 2008, JCDL '08.

[6]  Katrin Weller,et al.  Citation Analysis in Twitter: Approaches for Defining and Measuring Information Flows within Tweets during Scientific Conferences , 2011, #MSM.

[7]  Alessandro Vespignani,et al.  Modeling Users' Activity on Twitter Networks: Validation of Dunbar's Number , 2011, PloS one.

[8]  Dan Cosley,et al.  Inferring social ties from geographic coincidences , 2010, Proceedings of the National Academy of Sciences.

[9]  Gunther Eysenbach,et al.  Can Tweets Predict Citations? Metrics of Social Impact Based on Twitter and Correlation with Traditional Metrics of Scientific Impact , 2011, Journal of medical Internet research.

[10]  Jason Priem,et al.  How and why scholars cite on Twitter , 2010, ASIST.

[11]  Scott A. Golder,et al.  Diurnal and Seasonal Mood Vary with Work, Sleep, and Daylength Across Diverse Cultures , 2011 .

[12]  Johan Bollen,et al.  Usage bibliometrics , 2011, Annu. Rev. Inf. Sci. Technol..

[13]  Johan Bollen,et al.  Modeling Public Mood and Emotion: Twitter Sentiment and Socio-Economic Phenomena , 2009, ICWSM.

[14]  John G. Breslin,et al.  Understanding how Twitter is used to spread scientific messages , 2010 .

[15]  Lydia Eato Harris Foundations of library and information science, 2nd edition , 2006, J. Assoc. Inf. Sci. Technol..

[16]  Michael A. Rodriguez,et al.  Clickstream Data Yields High-Resolution Maps of Science , 2009, PloS one.