Logo
Logo

Goldsmiths - University of London

Measuring Discursive Influence Across Scholarship

Gerow, Aaron; Hu, Yuening; Boyd-Graber, Jordan; Blei, David and Evans, James. 2018. Measuring Discursive Influence Across Scholarship. Proceedings of the National Academy of Sciences, ISSN 0027-8424 [Article]

[img]
Preview
Text
1719792115.full.pdf - Published Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.

Download (1MB) | Preview
[img]
Preview
Text
gerow-influence-si.pdf - Supplemental Material
Available under License Creative Commons Attribution Non-commercial No Derivatives.

Download (9MB) | Preview

Abstract or Description

Assessing scholarly influence is critical for researchers surveying literature, institutions seeking to credit and support scholarly work, and understanding the history of academic inquiry. Influence is multi-faceted and citations reveal only part of it. Citation counts exhibit preferential attachment and follow a rigid ``news cycle’’ that can miss sustained and indirect forms of influence. Building on dynamic topic models that track distributional shifts in discourse over time, we introduce a novel variant that incorporates features such as authorship, affiliation, and publication venue to assess how these contexts interact with content to shape future scholarship. We perform in-depth analyses on collections of physics research (500K abstracts; 102 years) and scholarship generally (JSTOR: 2M full-text articles; 130 years). Our measure of document influence helps predict citations, and shows how outcomes such as winning a Nobel Prize or affiliation with a highly ranked institution boost influence. Analysis of citations alongside discursive influence reveals that citations tend to credit authors who persist in their fields over time, and discount credit for works that are influential over many topics or are ``ahead of their time’’. In this way, our measures provide a way to acknowledge diverse contributions that take longer and travel further to achieve scholarly appreciation, enabling us to correct citation biases and enhance sensitivity to the full spectrum of scholarly impact.

Item Type: Article

Identification Number (DOI):

https://doi.org/10.1073/pnas.1719792115

Keywords:

scholarly influence, science of science, probabilistic modeling

Departments, Centres and Research Units:

Computing

Dates:

DateEvent
16 February 2018Accepted
12 March 2018Published Online

Item ID:

22951

Date Deposited:

02 Mar 2018 10:36

Last Modified:

01 Aug 2018 03:25

Peer Reviewed:

Yes, this version has been peer-reviewed.

URI: http://research.gold.ac.uk/id/eprint/22951

View statistics for this item...

Edit Record Edit Record (login required)