Crowd Mining Applied to Preservation of Digital Cultural Heritage

Abstract

Accessible systems, in digital heritage as elsewhere, should 'speak the user's language'. However, over long time periods, this may change significantly, and the system must still keep track of it. Conceptualising and tracking change in a population may be achieved using a functional and computable model based on representative datasets. Such a model must encompass relevant characteristics in that population and support pre-defined functionality, such as the ability to track current trends in language use. Individual published viewpoints on any given platform may be observed in aggregate by means of a large-scale text mining approach. We have made use of social media platforms such as Twitter and Tumblr to collect statistical information about anonymous users' perspectives on cultural heritage items and institutions. Through longitudinal studies, it is possible to identify indicators pointing to an evolution of discourse surrounding cultural heritage items, and provide an estimate of trends relating to represented items and creators. We describe a functional approach to building useful models of shift in contemporary language use, using data collection across social networks. This approach is informed by existing theoretical approaches to modelling of semantic change. As a case study, we present a means by which such ongoing user modelling processes drawing on contemporary resources can support 'just-in-time' pre-emptive review of material to be presented to the public. We also show that this approach can feed into enhancement of the data retrieval processes.

Lost in data: recognizing type of time series sensor data using signal pattern classification

“…Furthermore, all that data have to be stored in datacentres in predefined and consistent format as inconsistent storage may potentially lead to digital dark age [7], i.e. data from the digital age can be lost irrevocably [8]. Digital preservation attempts to mitigate such risks.…”