A new approach to recognizing the use of attitude markers by authors of academic journal articles

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dc.authoridDas, Resul/0000-0002-6113-4649
dc.authoridSOYLU, Mücahit/0000-0002-4114-1390
dc.authoridSOYLU, AYFER/0000-0002-0504-5879
dc.authorwosidDas, Resul/V-9202-2018
dc.authorwosidSOYLU, Mücahit/ACY-7460-2022
dc.contributor.authorSoylu, Mucahit
dc.contributor.authorSoylu, Ayfer
dc.contributor.authorDas, Resul
dc.date.accessioned2024-08-04T20:54:27Z
dc.date.available2024-08-04T20:54:27Z
dc.date.issued2023
dc.departmentİnönü Üniversitesien_US
dc.description.abstractThis study investigates the use of Attitude Markers(AMs) by native academic authors of English (NAAEs) and Turkish-speaking academic authors of English (TAAEs) in 100 academic articles on Teacher Education. The primary objectives are to examine the forms and functions of AMs used by both groups to indicate their stance in articles and to compare the frequency and variety of AMs used by each group. The study employs a corpus -based approach and adopts a graph visualization method to present the findings. The data were cleaned using a software-supported approach to improve the efficiency of corpus compilation. The data were analyzed using the Antconc text analysis tool (Anthony, 2011) and Log-likelihood statistics. The reliability of the analysis was tested by calculating the inter-rater reliability. To do this, the content coded by one of the researchers and an independent rater was compared using Cohen's Kappa coefficient. The results ranged from 0.81 to 0.92, indicating a high level of reliability. Later, the findings were visualized using a radial knowledge graph. The statistical analysis showed significant differences in the use of certain AMs between the two groups, including AMs related to assessment(-13,20 LL; p<.01) and significance(-82,64 LL; p<.01). The findings indicate that both NAAEs and TAAEs commonly use AMs to convey their stance, with 'significance' and 'assessment' being the most frequent functional categories, and 'adjective' being the most commonly used form of AMs in both corpora. The findings provide valuable insights into the use of AMs in academic writing and can inform the development of English for academic purposes (EAP) course materials to enhance the academic writing skills of novice writers. The results of the study suggest that future research could use graph visualization to carry our corpus studies and could explore the effectiveness of using Artificial Intelligence (AI) technologies to minimize human bias in qualitative analyses.en_US
dc.identifier.doi10.1016/j.eswa.2023.120538
dc.identifier.issn0957-4174
dc.identifier.issn1873-6793
dc.identifier.scopus2-s2.0-85161994426en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.eswa.2023.120538
dc.identifier.urihttps://hdl.handle.net/11616/101411
dc.identifier.volume230en_US
dc.identifier.wosWOS:001023588600001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofExpert Systems With Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectRadial knowledge graphen_US
dc.subjectGraph visualizationen_US
dc.subjectStance in academic writingen_US
dc.subjectAttitude markersen_US
dc.subjectNativeen_US
dc.subjectnonnative writersen_US
dc.titleA new approach to recognizing the use of attitude markers by authors of academic journal articlesen_US
dc.typeArticleen_US

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