Information-centred research (ICR) is an information science e-research methodology that focuses on new information sources and particularly big data research for the social sciences and humanities. The ICR approach is an attempt to efficiently exploit new information sources for social science and humanities research purposes. ICR is a big data theory in the sense that it is for new information sources that are typically much larger than those analysed before for similar research issues, even if the data are not large enough to be computationally challenging.
Although the ICR task is to direct new information souces to relevant fields and problems, an ICR research approach can also involve developing methods to effectively extract useful information from the new data source(e.g., the Alternative Document Model counting methods used in link analysis). ICR researchers can work in five different ways.
|Thelwall, M. (2008). Fk yea I swear: Cursing and gender in a corpus of MySpace pages, Corpora, 3(1), 83-107.||This is the result of an open-ended investigation into MySpace text, which lead to the observation of plentify swearing and triggered a solo ICR paper about swearing, research that is relevant to linguistics and was published (without a domain expert) in a linguistics journal.||ICR1|
|Thelwall, M., Sud, P., & Vis, F. (2012). Commenting on YouTube videos: From Guatemalan rock to El Big Bang. Journal of the American Society for Information Science and Technology,63(3), 616–629.||This was a descriptive investigation into comments in YouTube in conjunction with a video analysis expert, Farida Vis.||ICR2|
|-||There are no known examples of ICR3: although it seems like a good idea in the abstract, there are human factors involved which mean that it is difficult for a researcher to suggest research topics to other researchers within a different field.||ICR3|
|Thelwall, M. (2008). Social networks, gender and friending: An analysis of MySpace member profiles, Journal of the American Society for Information Science and Technology, 59(8), 1321-1330.||This is a descriptive analysis of MySpace member profiles - the analysis is relativley light on theory and is an attempt to give an overview of MySpace member profiles to aid future disciplinary research (e.g., in communication studies).||ICR4|
|Thelwall, M. & Hasler, L. (2007). Blog search engines. Online Information Review, 31(4), 467-479.||This is the result of open-ended investigations into blogs and blog searching, producing a general approach for exploiting blogs in social science research and advocating that other social scientists use the simple techniques to assess whether blog searching could be useful in their research area.||ICR4|
|Park, H. W. & Thelwall, M. (2008). Web linkage pattern and social structure using politicians’ websites in South Korea. Quality & Quantity, 42(6), 687-697.||This paper was the result of an approach by a political communication specialist (who was already an expert in hyperlink analysis).||ICR5|
|Thelwall, M., & Harries, G. (2004). Do better scholars’ web publications have significantly higher online impact? Journal of the American Society for Information Science and Technology, 55(2), 149-159.||This is a link analysis study, part of a series of attempts to determine whether hyperlink counts could be used to measure the online impact of academic research.||PCR|
|Thelwall, M. & Prabowo, R. (2007). Identifying and characterinsing public science-related fears from RSS feeds. Journal of the American Society for Information Science and Technology, 58(3), 379-390.||This is a blog analysis study, assessing whether blog analysis could reveal public science fears.||PCR|
Thelwall, M., Wouters, P., & Fry, J. (2008). Information-centred research for large-scale analysis of new information sources, Journal of the American Society for Information Science and Technology, 59(9), 1523-1527.
Thelwall, M. & Wouters, P. (2005). What’s the deal with the web/Blogs/the next big technology: A key role for information science in e-social science research? CoLIS 2005, Lecture Notes in Computer Science 3507, 187-199.