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Psychiatry on Twitter: Content Analysis of the Use of Psychiatric Terms in French
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In: ISSN: 2561-326X ; JMIR Formative Research ; https://hal.archives-ouvertes.fr/hal-03614832 ; JMIR Formative Research, JMIR Publications 2022, 6 (2), pp.e18539. ⟨10.2196/18539⟩ ; https://formative.jmir.org/2022/2/e18539 (2022)
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Abstract:
International audience ; Background With the advent of digital technology and specifically user-generated contents in social media, new ways emerged for studying possible stigma of people in relation with mental health. Several pieces of work studied the discourse conveyed about psychiatric pathologies on Twitter considering mostly tweets in English and a limited number of psychiatric disorders terms. This paper proposes the first study to analyze the use of a wide range of psychiatric terms in tweets in French. Objective Our aim is to study how generic, nosographic, and therapeutic psychiatric terms are used on Twitter in French. More specifically, our study has 3 complementary goals: (1) to analyze the types of psychiatric word use (medical, misuse, or irrelevant), (2) to analyze the polarity conveyed in the tweets that use these terms (positive, negative, or neural), and (3) to compare the frequency of these terms to those observed in related work (mainly in English). Methods Our study was conducted on a corpus of tweets in French posted from January 1, 2016, to December 31, 2018, and collected using dedicated keywords. The corpus was manually annotated by clinical psychiatrists following a multilayer annotation scheme that includes the type of word use and the opinion orientation of the tweet. A qualitative analysis was performed to measure the reliability of the produced manual annotation, and then a quantitative analysis was performed considering mainly term frequency in each layer and exploring the interactions between them. Results One of the first results is a resource as an annotated dataset. The initial dataset is composed of 22,579 tweets in French containing at least one of the selected psychiatric terms. From this set, experts in psychiatry randomly annotated 3040 tweets that corresponded to the resource resulting from our work. The second result is the analysis of the annotations showing that terms are misused in 45.33% (1378/3040) of the tweets and that their associated polarity is negative in 86.21% (1188/1378) of the cases. When considering the 3 types of term use, 52.14% (1585/3040) of the tweets are associated with a negative polarity. Misused terms related to psychotic disorders (721/1300, 55.46%) were more frequent to those related to depression (15/280, 5.4%). Conclusions Some psychiatric terms are misused in the corpora we studied, which is consistent with the results reported in related work in other languages. Thanks to the great diversity of studied terms, this work highlighted a disparity in the representations and ways of using psychiatric terms. Moreover, our study is important to help psychiatrists to be aware of the term use in new communication media such as social networks that are widely used. This study has the huge advantage to be reproducible thanks to the framework and guidelines we produced so that the study could be renewed in order to analyze the evolution of term usage. While the newly build dataset is a valuable resource for other analytical studies, it could also serve to train machine learning algorithms to automatically identify stigma in social media.
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Keyword:
[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]; [INFO.INFO-TT]Computer Science [cs]/Document and Text Processing; mental health; psychiatric term use; social media; social media analysis; social stigma
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URL: https://doi.org/10.2196/18539
https://hal.archives-ouvertes.fr/hal-03614832
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| 2 |
« “Twitta” “Intellectuelle” “Influenceuse” ? Être enseignante-chercheuse sur twitter »
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In: ISSN: 1763-0061 ; EISSN: 1963-1812 ; Tracés : Revue de Sciences Humaines ; https://hal.archives-ouvertes.fr/hal-03592945 ; Tracés : Revue de Sciences Humaines, ENS Éditions, A paraître (2022)
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| 3 |
Renouvellement paradigmatique dans l’analyse des discours numériques : le cas de la communication politique sur les RSN
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In: ISSN: 2116-1747 ; Etudes de stylistique anglaise ; https://hal-amu.archives-ouvertes.fr/hal-03584927 ; Etudes de stylistique anglaise, Société de stylistique anglaise, Lyon, 2022, Renaissance(s)/Rebirth(s), ⟨10.4000/esa.4816⟩ ; https://journals.openedition.org/esa/4816 (2022)
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| 4 |
Chapter 11. Consumer opinion about smoked bacon using Twitter and textual analysis: The challenge continues
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In: Sensory Analysis for the Development of Meat Products ; https://hal-agrosup-dijon.archives-ouvertes.fr/hal-03575175 ; Sensory Analysis for the Development of Meat Products, Elsevier, pp.181-196, 2022, 9780128228326. ⟨10.1016/B978-0-12-822832-6.00013-8⟩ (2022)
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| 5 |
#Bittersweet: Positive, negative, and mixed emotions in twitter posts ...
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| 6 |
A Multilingual Dataset of COVID-19 Vaccination Attitudes on Twitter ...
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| 7 |
A Multilingual Dataset of COVID-19 Vaccination Attitudes on Twitter ...
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| 10 |
MULDASA: Multifactor Lexical Sentiment Analysis of Social-Media Content in Nonstandard Arabic Social Media
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In: Applied Sciences; Volume 12; Issue 8; Pages: 3806 (2022)
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| 11 |
Extracting Disaster-Related Location Information through Social Media to Assist Remote Sensing for Disaster Analysis: The Case of the Flood Disaster in the Yangtze River Basin in China in 2020
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In: Remote Sensing; Volume 14; Issue 5; Pages: 1199 (2022)
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| 12 |
Analysis of the Full-Size Russian Corpus of Internet Drug Reviews with Complex NER Labeling Using Deep Learning Neural Networks and Language Models
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In: Applied Sciences; Volume 12; Issue 1; Pages: 491 (2022)
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| 13 |
Social Media and the Pandemic: Consumption Habits of the Spanish Population before and during the COVID-19 Lockdown
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In: Sustainability; Volume 14; Issue 9; Pages: 5490 (2022)
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| 14 |
Climate Change Sentiment Analysis Using Lexicon, Machine Learning and Hybrid Approaches
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In: Sustainability; Volume 14; Issue 8; Pages: 4723 (2022)
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| 15 |
Artificial Intelligent in Education
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In: Sustainability; Volume 14; Issue 5; Pages: 2862 (2022)
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| 16 |
eHealth Engagement on Facebook during COVID-19: Simplistic Computational Data Analysis
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In: International Journal of Environmental Research and Public Health; Volume 19; Issue 8; Pages: 4615 (2022)
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| 17 |
How Do Chinese People View Cyberbullying? A Text Analysis Based on Social Media
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In: International Journal of Environmental Research and Public Health; Volume 19; Issue 3; Pages: 1822 (2022)
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| 18 |
Knowledge Discovery from Large Amounts of Social Media Data
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In: Applied Sciences; Volume 12; Issue 3; Pages: 1209 (2022)
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| 19 |
Detecting Depression Signs on Social Media: A Systematic Literature Review
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In: Healthcare; Volume 10; Issue 2; Pages: 291 (2022)
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| 20 |
A Novel Method of Generating Geospatial Intelligence from Social Media Posts of Political Leaders
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In: Information; Volume 13; Issue 3; Pages: 120 (2022)
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