Paper short abstract:

We aim at analyzing how new research areas are born and perceived by society. We use automated data collection and data mining techniques for analyzing scientific and social information (articles and tweets). We find that its crucial to pay more attention to the social perception of scientific research.

Paper long abstract:

The aim of this paper is to study how new research areas born and how these research areas are perceived by society. We construct a methodology, "knowledge genealogies", which is built using diverse automated data collection and data mining techniques for analyzing scientific and social information. We analyze the scientific biotechnology community and how social media perceives it. We choose this field because of its diversity (in research terms, techniques and products) and possible "tensions" between different applications (e.g. genetic engineering for agriculture vs. health applications).

Indicators of scientific production include: the total number of articles published in high impact journals (308,079 articles in 205 journals downloaded from the "Biotechnology and Microbiology Applied" JRC subject category (ISI) from 1997 to 2011) and a subset of keywords of those articles (a total 187,218 for 2011) that allow building clusters and networks. Social information for each theme includes 375,660 tweets containing any keywords of a subset that are classified by sentiment (positive, negative and neutral) for a total of 33,900 tweets for 2012.

We show that biotechnology was an active research field with eight well-defined themes and promising from a social point of view. We show that our method is able to provide social and scientific information across themes and components. The sentiment analysis indicates that the social perception of our subset is perceived mainly in a neutral manner skewed towards a negative influence, indicating that more attention needs to be place towards the social perception of scientific research.