@article{4850,
  author = {Sandra M K, Rupesh Kumar A},
  title = {Supporting, Contrasting, and Mentioning Citations in Computational Linguistics: Insights from Scite and Scopus},
  journal = {Journal of Science &Technology Metrics},
  year = {2026},
  volume = {7},
  number = {3},
  doi = {https://doi.org/10.6025/jstm/2026/7/3/150-160},
  url = {https://www.dline.info/jstm/fulltext/v7n3/jstmv7n3_3.pdf},
  abstract = {Purpose: This study examines the contextual and sectional relevance of citations received by scholarly
publications in computational linguistics using Scite. It further investigates the relationships between
supporting, contrasting and mentioning citations and the corresponding Scopus citation counts.
Methodology: Both conventional and contextual citation-analysis techniques were employed. Scientific
publications on computational linguistics were retrieved from the Scopus database. Contextual citation
data were subsequently obtained from the smart citation index Scite.ai, an emerging classified citation
database built with machine learning, artificial intelligence and natural language processing. A total of
15,607 documents containing classified citation statements were included. Data were analysed in Microsoft
Excel and statistical tests were performed with SPSS.
Results: The analysis reveals a steady rise in both publications and citations. The 15,607 scholarly works
collectively received 3,872 supporting, 375 contrasting and 343,855 mentioning citations corresponding to
1.09 % agreement, 0.11 % disagreement and 97.23 % neutral research engagement. Mentioning citations
dominate every year, reflecting a tendency among scholars to cite neutrally without explicit endorsement
or critique. Although contrasting citations are far fewer than supporting or mentioning citations, they
remain significant because they signal critical scrutiny and inconsistencies in the published literature. Among
the ten journals with the highest publication counts, Transactions of the Association for Computational
Linguistics leads in both supporting and contrasting citations. Spearman’s rank-correlation tests show a
strong positive association between mentioning citations and Scopus citations ( = .839, p = .000), a weak
positive association with supporting citations (  = .336,  = .000) and a still weaker positive association
with contrasting citations ( = .153, p = .000). Sectional citation analysis indicates that the largest number
of citations (6,031) fall into the “Other” category, underscoring the diversity of structural conventions in
scientific communication.
Conclusion: The investigation demonstrates the value of contextual citation analysis applied to
computational linguistics research. The approach can be extended to author-, journal- and institutionallevel
assessments to reveal genuine research impact. Contextual citation analysis offers a novel perspective
that moves beyond the traditional assumption that “all that glitters is gold.”},
}