Authors : Matthew L Williams, Pete Burnap, Luke Sloan
New and emerging forms of data, including posts harvested from social media sites such as Twitter, have become part of the sociologist’s data diet. In particular, some researchers see an advantage in the perceived ‘public’ nature of Twitter posts, representing them in publications without seeking informed consent.
While such practice may not be at odds with Twitter’s terms of service, we argue there is a need to interpret these through the lens of social science research methods that imply a more reflexive ethical approach than provided in ‘legal’ accounts of the permissible use of these data in research publications.
To challenge some existing practice in Twitter-based research, this article brings to the fore: (1) views of Twitter users through analysis of online survey data; (2) the effect of context collapse and online disinhibition on the behaviours of users; and (3) the publication of identifiable sensitive classifications derived from algorithms.
URL : Towards an Ethical Framework for Publishing Twitter Data in Social Research: Taking into Account Users’ Views, Online Context and Algorithmic Estimation
DOI : http://dx.doi.org/10.1177%2F0038038517708140
The research community needs reliable, standard ways to make the data produced by scientific research available to the community, while getting credit as data authors. As a result, a new form of scholarly publication is emerging: data publishing. Data pubishing – or making data long-term accessible, reusable and citable – is more involved than simply providing a link to a data file or posting the data to the researchers web site.
In this paper, we define what is needed for proper data publishing and describe how the open-source Dataverse software helps define, enable and enhance data publishing for all.
URL : http://scholar.harvard.edu/mercecrosas/publications/dataverse-4-defining-data-publishing
« The purpose of this paper is to contribute to the challenge of transferring know-how, theories and methods from design research to the design processes in information science and technologies. More specifically, we shall consider a domain, namely data-science, that is becoming rapidly a globally invested research and development axis with strong imperatives for innovation given the data deluge we are currently facing. We argue that, in order to rise to the data-related challenges that the society is facing, data-science initiatives should ensure a renewal of traditional research methodologies that are still largely based on trial-error processes depending on the talent and insights of a single (or a restricted group of) researchers. It is our claim that design theories and methods can provide, at least to some extent, the much-needed framework. We will use a worldwide data-science challenge organized to study a technical problem in physics, namely the detection of Higgs boson, as a use case to demonstrate some of the ways in which design theory and methods can help in analyzing and shaping the innovation dynamics in such projects. »
URL : http://arxiv.org/abs/1503.06201