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\section{Introduction}  The view from the ``ivory tower'' is that scholars make rational, expert decisions on what to publish, what to read and what to cite. In fact, the use of citation statistics to assess scholarly impact is to a large degree premised on the very notion that citation data represent an explicit, objective expression of impact by expert authors \cite{rubin}. Yet, scholarship is increasingly becoming an online process, and social media are becoming an increasingly important part of the online scholarly ecology. As a result, the citation behavior of scholars may be affected by their increasing use of social media. Practices and considerations that go beyond traditional notions of scholarly impact may thus influence what scholars cite.   Recent efforts have investigated the effect of the use of social media environments on scholarly practice. For example, some research has looked at how scientists use the microblogging platform Twitter during conferences by analyzing tweets containing conference hashtags \cite{julie,weller:2011}. Other research has explored the ways by which scholars use Twitter and related platforms to cite scientific articles \cite{priem,weller}. More recent work has shown that Twitter article mentions predict future citations \cite{Eysenbach_2011}. \cite{Eysenbach2011}.  This article falls within, and extends, these lines of research by examining the temporal relations between quantitative measures of readership, Twitter mentions, and subsequent citations for a cohort of scientific preprints. We study how the scientific community and the public at large respond to a cohort of preprints that were submitted to the arXiv database (\url{http://arxiv.org}), a service managed by Cornell University Library, which has become the premier pre-print publishing platform in physics, computer science, astronomy, and related domains. We examine the relations between three types of responses to the submissions of this cohort of pre-prints, namely the number of Twitter posts (tweets) that specifically mention these pre-prints, downloads of these pre-prints from the arXiv.org web site, and the number of early citations that the 70 most Twitter-mentioned preprints in our cohort received after their submission. In each case, we measure total volume of responses, as well as the delay and span of their temporal distribution. We perform a comparative analysis of how these indicators are related to each other, both in magnitude and time.