<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="moriceau-etal-2022-automatic">
    <titleInfo>
        <title>Automatic Detection of Stigmatizing Uses of Psychiatric Terms on Twitter</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Véronique</namePart>
        <namePart type="family">Moriceau</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Farah</namePart>
        <namePart type="family">Benamara</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Abdelmoumene</namePart>
        <namePart type="family">Boumadane</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2022-06</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the Thirteenth Language Resources and Evaluation Conference</title>
        </titleInfo>
        <originInfo>
            <publisher>European Language Resources Association</publisher>
            <place>
                <placeTerm type="text">Marseille, France</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Psychiatry and people suffering from mental disorders have often been given a pejorative label that induces social rejection. Many studies have addressed discourse content about psychiatry on social media, suggesting that they convey stigmatizingrepresentations of mental health disorders. In this paper, we focus for the first time on the use of psychiatric terms in tweetsin French. We first describe the annotated dataset that we use. Then we propose several deep learning models to detectautomatically (1) the different types of use of psychiatric terms (medical use, misuse or irrelevant use), and (2) the polarityof the tweet. We show that polarity detection can be improved when done in a multitask framework in combination with typeof use detection. This confirms the observations made manually on several datasets, namely that the polarity of a tweet iscorrelated to the type of term use (misuses are mostly negative whereas medical uses are neutral). The results are interesting forboth tasks and it allows to consider the possibility for performant automatic approaches in order to conduct real-time surveyson social media, larger and less expensive than existing manual ones</abstract>
    <identifier type="citekey">moriceau-etal-2022-automatic</identifier>
    <location>
        <url>https://aclanthology.org/2022.lrec-1.25</url>
    </location>
    <part>
        <date>2022-06</date>
        <extent unit="page">
            <start>237</start>
            <end>243</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Automatic Detection of Stigmatizing Uses of Psychiatric Terms on Twitter
%A Moriceau, Véronique
%A Benamara, Farah
%A Boumadane, Abdelmoumene
%S Proceedings of the Thirteenth Language Resources and Evaluation Conference
%D 2022
%8 June
%I European Language Resources Association
%C Marseille, France
%F moriceau-etal-2022-automatic
%X Psychiatry and people suffering from mental disorders have often been given a pejorative label that induces social rejection. Many studies have addressed discourse content about psychiatry on social media, suggesting that they convey stigmatizingrepresentations of mental health disorders. In this paper, we focus for the first time on the use of psychiatric terms in tweetsin French. We first describe the annotated dataset that we use. Then we propose several deep learning models to detectautomatically (1) the different types of use of psychiatric terms (medical use, misuse or irrelevant use), and (2) the polarityof the tweet. We show that polarity detection can be improved when done in a multitask framework in combination with typeof use detection. This confirms the observations made manually on several datasets, namely that the polarity of a tweet iscorrelated to the type of term use (misuses are mostly negative whereas medical uses are neutral). The results are interesting forboth tasks and it allows to consider the possibility for performant automatic approaches in order to conduct real-time surveyson social media, larger and less expensive than existing manual ones
%U https://aclanthology.org/2022.lrec-1.25
%P 237-243

Markdown (Informal)

[Automatic Detection of Stigmatizing Uses of Psychiatric Terms on Twitter](https://aclanthology.org/2022.lrec-1.25) (Moriceau et al., LREC 2022)

• Automatic Detection of Stigmatizing Uses of Psychiatric Terms on Twitter (Moriceau et al., LREC 2022)

ACL

• Véronique Moriceau, Farah Benamara, and Abdelmoumene Boumadane. 2022. Automatic Detection of Stigmatizing Uses of Psychiatric Terms on Twitter. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 237–243, Marseille, France. European Language Resources Association.