Time Series Based Frequency Analysis of Violence and Criminalization Related Dynamic Mental Health Media News

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dc.contributor.authorDursun, Ahu Dereli
dc.date.accessioned2024-07-18T20:40:05Z
dc.date.available2024-07-18T20:40:05Z
dc.date.issued2022
dc.departmentİstanbul Bilgi Üniversitesien_US
dc.description22nd International Conference on Computational Science and its Applications (ICCSA) -- JUL 04-07, 2022 -- Malaga, SPAINen_US
dc.description.abstractViolence, displaying a dynamic and multivariate nature with different biological, psychodynamic, social and individual factors, is reflected in media texts related to mental health and mental illness which embody numerous variables that are not simple to address and assess. The problematic issues around mental health are not only concerned with the medical aspect of mental problems, including the definition, diagnosis and treatment thereof, but also with respect to negative attitudes of people towards those with psychological problems, which may bring about cases of stigmatization. Media can play a significant role in criminalization of the mentally ill and be instrumental in shaping and changing attitudes towards mental illness. Accordingly, the current study aims to assess the trends in national discourse through media texts by looking into the volume and content of a sample of 496 news stories about mental illness, with a particular focus on bipolar disorder, from 2014 to 2019. Three national daily newspapers constitute the sample addressed in the study where frequency analysis has been performed on the media text dataset in accordance with the changes over the years. The frequency analyses based on time series conducted in the study demonstrate that violence and criminalization have been found to be the most frequently addressed topic among the ones mentioned across the study period. Based on the results derived from the analyses, it is seen that focus of the news media on violence is not proportional to actual rates of violence among the mentally ill. Thus, the research suggests that such a continued emphasis on violence and criminalization may intensify social stigmatization and hinder attempts to seek professional help. All these mental health-related considerations and raising public awareness point to ultimate strategies in public health domain regarding the socially constructed model which significantly affects the benefits of and barriers to action in health-promoting attitudes and behaviors.en_US
dc.description.sponsorshipSpringer Int Publishing AG,Comp Open Access Journal,Computat Open Access Journal,Univ Malaga,Univ Perugia,Univ Basilicata,Monash Univ,Kyushu Sangyo Univ,Univ Minho,Univ Cagliarien_US
dc.identifier.doi10.1007/978-3-031-10450-3_36
dc.identifier.endpage424en_US
dc.identifier.isbn978-3-031-10450-3
dc.identifier.isbn978-3-031-10449-7
dc.identifier.issn0302-9743
dc.identifier.issn1611-3349
dc.identifier.scopus2-s2.0-85135006043en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage416en_US
dc.identifier.urihttps://doi.org/10.1007/978-3-031-10450-3_36
dc.identifier.urihttps://hdl.handle.net/11411/6969
dc.identifier.volume13376en_US
dc.identifier.wosWOS:000916455600035en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer International Publishing Agen_US
dc.relation.ispartofComputational Science and Its Applications, Iccsa 2022, Pt Iien_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectDynamic Mental Health Media Newsen_US
dc.subjectTime Seriesen_US
dc.subjectFrequency Analysisen_US
dc.subjectCriminalizationen_US
dc.subjectViolenceen_US
dc.subjectPublic Healthen_US
dc.subjectPublic Awarenessen_US
dc.subjectBipolar Disorderen_US
dc.subjectAffective Disorderen_US
dc.subjectIllnessen_US
dc.subjectCoverageen_US
dc.titleTime Series Based Frequency Analysis of Violence and Criminalization Related Dynamic Mental Health Media News
dc.typeConference Object

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