Text Data Mining from the Author’s Perspective: Whose Text, Whose Mining, and to Whose Benefit?

Authors : Christine L. Borgman

Given the many technical, social, and policy shifts in access to scholarly content since the early days of text data mining, it is time to expand the conversation about text data mining from concerns of the researcher wishing to mine data to include concerns of researcher-authors about how their data are mined, by whom, for what purposes, and to whose benefits.

URL : https://arxiv.org/abs/1803.04552

Le temps des SIC

Auteurs/Authors : Gabriel Gallezot, Marty Emmanuel

Pour rendre compte du temps des Sciences de l’Information et de la Communication (SIC), nous avons choisi d’analyser le lexique des chercheurs. Notre étude s’appuie sur les textes librement déposés par les auteurs sur la plateforme HAL/@sic.

La fouille de texte s’effectue par une série d’analyses lexicométriques afin de répondre à deux objectifs : appréhender les notions liées au temps dans les recherches en SIC, d’une part, d’autre part rendre compte de l’évolution dans le temps des champs et questions de recherche en SIC.

URL : https://archivesic.ccsd.cnrs.fr/sic_01599944

Bibliometric methods for detecting and analysing emerging research topics

This study gives an overview of the process of clustering scientific disciplines using hybrid methods, detecting and labelling emerging topics and analysing the results using bibliometrics methods.

The hybrid clustering techniques are based on biblographic coupling and text-mining and ‘core documents’, and cross-citation links are used to identify emerging fields.

The collaboration network of those countries that proved to be most active in the underlying disciplines, in combination with a set of standard indicators, form the groundwork for the bibliometric analysis of the detected emerging research topics.

URL : http://hdl.handle.net/10760/16947

Value and benefits of text mining Vast…

Value and benefits of text mining :

“Vast amounts of new information and data are generated everyday through economic, academic and social activities. This sea of data, predicted to increase at a rate of 40% p.a., has significant potential economic and societal value. Techniques such as text and data mining and analytics are required to exploit this potential.
Businesses use such techniques to analyse customer and competitor data to improve competitiveness; the pharmaceutical industry mines patents and research articles to improve drug discovery; within academic research, mining and analytics of large datasets are delivering efficiencies and new knowledge in areas as diverse as biological science, particle physics and media and communications.
We have explored the costs, benefits, barriers and risks associated with text mining within UKFHE research using the approach to welfare economics laid out in the UK Treasury best practice guidelines for evaluation.”

URL : http://www.jisc.ac.uk/publications/reports/2012/value-and-benefits-of-text-mining.aspx