Computational Linguistics and Media News: Linguistic Features and Lexical Choices with Time-frequency Statistical Analysis

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dc.contributor.authorDursun, Ahu Dereli
dc.date.accessioned2026-04-04T18:55:20Z
dc.date.available2026-04-04T18:55:20Z
dc.date.issued2026
dc.departmentİstanbul Bilgi Üniversitesi
dc.description25th International Conference on Computational Science and Applications-ICCSA-Annual -- JUN 30-JUL 03, 2025 -- Galatasaray University, Istanbul, TURKIYE
dc.description.abstractMedia has a critical role in shaping attitudes and public understanding toward mental health. The handling of news on mental health conditions in media comes with intrinsic challenges, some of which are due to the use of language. Language, lexical choices and contexts which the lexical items are attributed to can bring about different effects like stigmatizing and otherizing perceptions or trivializing severe mental health conditions. Hence, language used in media is influential, particularly in health communication which is targeted toward public with the source being represented by health communicators and journalists. In view of these aspects, the present study aims to examine the use of language in print media based on the lexical analyses, including the use of adjectives, nominalizations and connotations, based on theoretical and quantitative dimensions, which are content analysis regarding linguistic features and time-frequency statistical analysis. The dataset contains 725 newspaper articles on mental health and psychological problems, published in six different Turkish national newspapers over a six-year period. The results of the computational linguistic-based analyses belonging to the proposed model show that negative descriptions, attributions and connotations regarding people with mental health disorders outnumber the positive ones. Another aim of the study concerns whether reporting language on mental health conditions is consistent with what has been suggested by the American Psychiatric Association (APA). The results obtained reveal that some lexical choices, particularly certain adjectives and nominalized phrases used in the reporting language, are not in agreement with APA's recommendations. In brief, it has been aimed at pinpointing reporting language in journalistic practices using accurate, responsible and empathic language for mental health reporting and more balanced press coverage concerning mental problems and individuals with mental conditions. This aim can further be oriented towards other media where various genres and styles of language are used across different disciplines.
dc.description.sponsorshipInstitute of Electrical and Electronics Engineers Inc,Springer New Zealand,University of Massachusetts Inc,University of Perugia,University of Basilicata,Monash University,Kyushu Sangyo University,Universidade do Minho
dc.identifier.doi10.1007/978-3-031-97576-9_15
dc.identifier.doi10.1007/978-3-031-97576-9_15
dc.identifier.endpage242
dc.identifier.isbn978-3-031-97575-2
dc.identifier.isbn978-3-031-97576-9
dc.identifier.issn0302-9743
dc.identifier.issn1611-3349
dc.identifier.scopus2-s2.0-105010828356
dc.identifier.scopusqualityQ3
dc.identifier.startpage226
dc.identifier.urihttps://doi.org/10.1007/978-3-031-97576-9_15
dc.identifier.urihttps://hdl.handle.net/11411/10371
dc.identifier.volume15886
dc.identifier.wosWOS:001563938300015
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer International Publishing Ag
dc.relation.ispartofComputational Science and Its Applications-Iccsa 2025 Workshops, Pt I
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WoS_20260402
dc.snmzKA_Scopus_20260402
dc.subjectMedia Language
dc.subjectComputational Linguistics
dc.subjectMental Health News
dc.subjectMental Health Reporting
dc.subjectMedia Texts
dc.subjectLinguistic Features
dc.subjectStigmatizing And Derogatory Language
dc.subjectLexical Choices
dc.subjectLexical Variations
dc.subjectJournalistic Practice
dc.subjectTime-Frequency Statistical Analysis
dc.subjectObservation Probability
dc.subjectPsycholinguistic Features
dc.subjectContent Analysis
dc.titleComputational Linguistics and Media News: Linguistic Features and Lexical Choices with Time-frequency Statistical Analysis
dc.typeConference Object

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