Powered by OpenAIRE graph
Found an issue? Give us feedback
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Conference object . 2025
License: CC BY
Data sources: ZENODO
ZENODO
Article . 2025
License: CC BY
Data sources: Datacite
ZENODO
Article . 2025
License: CC BY
Data sources: Datacite
versions View all 2 versions
addClaim

The dual ecology of academic success: connecting co-authorship networks to article title properties: Why titles matter?

Authors: Ruiz, Gonzalo; Divasón, Jose; Pérez-Llantada, Carmen;

The dual ecology of academic success: connecting co-authorship networks to article title properties: Why titles matter?

Abstract

Abstract: One of the most persistent challenges in understanding global research communication is the failure of most scientometric studies to systematically combine quantitative insights with the in-depth qualitative linguistic analysis required to interpret communication effectiveness. This article addresses this methodological gap by presenting a mixed-methods framework that simultaneously examines the structural success of researchers’ collaborative ecosystems and the communicative efficiency of their published output titles. We generated co-authorship networks for four highly successful researchers in STEM fields, utilizing established bibliometric algorithms on extensive publication data, co-author linkages, and institutional affiliations. The resulting networks were analyzed via a range of network metrics and community detection algorithms to identify distinctive structural characteristics, key influential nodes, and the overall significance of these collaborative structures within the academic community. Critically, this quantitative network analysis is robustly complemented by linguistic data mining applied to the titles of the articles forming these networks. This goes well beyond simple metrics to generate sophisticated measures of lexical and syntactic complexity. Our analysis profiles the titles using a specialized set of metrics relevant to corpus linguistics, including measures of lexical and syntactic complexity. Our main aim was to formally quantify the titles’ communicative efficiency—a critical factor in driving readership and initial research impact. The study’s main contribution is its combination of these two domains. In this presentation we explore the direct associations between these identified linguistic features and the articles’ bibliometric scores (e.g., citation performance). The resulting data offers novel insights not just for research design, but also for informing English for Academic Purposes (EAP) curricula. Specifically, the findings provide data-driven strategies for training researchers in strategically building effective collaborative ecosystems and, crucially, mastering the linguistic demands of high-stakes academic discourse, ultimately helping them maximize their visibility and research impact.

Keywords

publishing practices, genre analysis, attention economy, academic writing, network analysis, academic ecosystems

  • BIP!
    Impact byBIP!
    selected citations
    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    0
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average