The present thesis is situated within the debate on the relationship between mental health and social media, with the aim of describing the contribution of social networking platforms to the generation and maintenance of discursive configurations related to the diagnostic categories through which “mental disorders” have been classified in currently used manuals. These configurations will hereafter be referred to as the discursive configuration of the “psychiatric disorder syndrome.” On the basis of a review of the state of the art, critical aspects characterizing the interactive structure of social media platforms in relation to the configuration under investigation were identified, outlining the need to observe how the discursive productions of members of online communities contribute to its generation and maintenance.To address this aim, the study adopts Dialogical Science as its theoretical and methodological framework, a discipline that allows for the formalization of the rules governing the use of ordinary language through which community members discursively configure what, in common sense, is defined as reality. Given the relevance of the object of investigation to public health issues and to healthcare professions, the observation of the “psychiatric disorder syndrome” configuration is conducted with reference to the construct of “Health.” The thesis employs the M.A.D.I.T. methodology (Methodology for the Analysis of Computerized Textual Data) to describe the contribution of texts produced on the social media platform Instagram, in relation to mental health, to the generation of discursive configurations of Health. Overall, the thesis aims to contribute to the understanding of the discursive modalities through which the “psychiatric disorder syndrome” is configured within social media platforms.
L’elaborato si colloca nel dibattito sul rapporto tra salute mentale e social media, con il proposito di descrivere il contributo dei social network alla generazione e al mantenimento di quelle che sono le configurazioni discorsive relative alle categorie diagnostiche con cui i “disturbi mentali” sono stati classificati all’interno dei manuali correntemente in uso, e a cui ci riferiremo in questa sede con la dicitura di configurazione discorsiva “sindrome da disturbo psichiatrico”. Alla luce di un’analisi dello stato dell’arte, sono stati individuati aspetti critici che caratterizzano l’assetto interattivo delle piattaforme social in relazione alla configurazione oggetto d’indagine, permettendo di delineare l’esigenza di osservare in che modo le produzioni discorsive dei membri della comunità online concorrano alla generazione e al mantenimento della stessa. Al fine di rispondere a tale esigenza, la ricerca adotta come cornice teorico-metodologica di riferimento la Scienza Dialogica, disciplina che consente di formalizzare le regole d’uso del linguaggio ordinario attraverso cui i membri della comunità configurano discorsivamente ciò che, nel senso comune, viene definita come realtà. Considerata l’appartenenza dell’oggetto di indagine alle questioni di salute pubblica, ad appannaggio delle professioni sanitarie, l’osservazione della configurazione “sindrome da disturbo psichiatrico” viene condotta facendo riferimento al costrutto di “Salute”. Il presente elaborato di tesi si avvale della metodologia M.A.D.I.T. (Metodologia per l’Analisi dei Dati Informatizzati Testuali) per descrivere il contributo che i testi prodotti sulla piattaforma social Instagram, in relazione al tema della salute mentale, portano alla generazione di configurazioni discorsive di Salute. Nel suo complesso, l’elaborato si colloca come contributo conoscitivo volto alla comprensione delle modalità discorsive attraverso cui la “sindrome da disturbo psichiatrico” viene configurata all’interno delle piattaforme social.
La sindrome da disturbo psichiatrico come configurazione discorsiva nei social media: un’analisi delle produzioni testuali sulla piattaforma Instagram
BURLIN, FILIPPO
2025/2026
Abstract
The present thesis is situated within the debate on the relationship between mental health and social media, with the aim of describing the contribution of social networking platforms to the generation and maintenance of discursive configurations related to the diagnostic categories through which “mental disorders” have been classified in currently used manuals. These configurations will hereafter be referred to as the discursive configuration of the “psychiatric disorder syndrome.” On the basis of a review of the state of the art, critical aspects characterizing the interactive structure of social media platforms in relation to the configuration under investigation were identified, outlining the need to observe how the discursive productions of members of online communities contribute to its generation and maintenance.To address this aim, the study adopts Dialogical Science as its theoretical and methodological framework, a discipline that allows for the formalization of the rules governing the use of ordinary language through which community members discursively configure what, in common sense, is defined as reality. Given the relevance of the object of investigation to public health issues and to healthcare professions, the observation of the “psychiatric disorder syndrome” configuration is conducted with reference to the construct of “Health.” The thesis employs the M.A.D.I.T. methodology (Methodology for the Analysis of Computerized Textual Data) to describe the contribution of texts produced on the social media platform Instagram, in relation to mental health, to the generation of discursive configurations of Health. Overall, the thesis aims to contribute to the understanding of the discursive modalities through which the “psychiatric disorder syndrome” is configured within social media platforms.| File | Dimensione | Formato | |
|---|---|---|---|
|
Tesi magistrale Filippo Burlin.pdf
accesso aperto
Dimensione
2.64 MB
Formato
Adobe PDF
|
2.64 MB | Adobe PDF | Visualizza/Apri |
The text of this website © Università degli studi di Padova. Full Text are published under a non-exclusive license. Metadata are under a CC0 License
https://hdl.handle.net/20.500.12608/107796