@inproceedings{johannssen-etal-2022-classification,
title = "Classification of {G}erman Jungian Extraversion and Introversion Texts with Assessment of Changes During the {COVID}-19 Pandemic",
author = "Johann{\ss}en, Dirk and
Biemann, Chris and
Scheffer, David",
editor = "Kokkinakis, Dimitrios and
Themistocleous, Charalambos K. and
Fors, Kristina Lundholm and
Tsanas, Athanasios and
Fraser, Kathleen C.",
booktitle = "Proceedings of the RaPID Workshop - Resources and ProcessIng of linguistic, para-linguistic and extra-linguistic Data from people with various forms of cognitive/psychiatric/developmental impairments - within the 13th Language Resources and Evaluation Conference",
month = jun,
year = "2022",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2022.rapid-1.5",
pages = "31--40",
abstract = "The corona pandemic and countermeasures such as social distancing and lockdowns have confronted individuals with new challenges for their mental health and well-being. It can be assumed that the Jungian psychology types of extraverts and introverts react differently to these challenges. We propose a Bi-LSTM model with an attention mechanism for classifying introversion and extraversion from German tweets, which is trained on hand-labeled data created by 335 participants. With this work, we provide this novel dataset for free use and validation. The proposed model achieves solid performance with F1 = .72. Furthermore, we created a feature engineered logistic model tree (LMT) trained on hand-labeled tweets, to which the data is also made available with this work. With this second model, German tweets before and during the pandemic have been investigated. Extraverts display more positive emotions, whilst introverts show more insight and higher rates of anxiety. Even though such a model can not replace proper psychological diagnostics, it can help shed light on linguistic markers and to help understand introversion and extraversion better for a variety of applications and investigations.",
}
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<abstract>The corona pandemic and countermeasures such as social distancing and lockdowns have confronted individuals with new challenges for their mental health and well-being. It can be assumed that the Jungian psychology types of extraverts and introverts react differently to these challenges. We propose a Bi-LSTM model with an attention mechanism for classifying introversion and extraversion from German tweets, which is trained on hand-labeled data created by 335 participants. With this work, we provide this novel dataset for free use and validation. The proposed model achieves solid performance with F1 = .72. Furthermore, we created a feature engineered logistic model tree (LMT) trained on hand-labeled tweets, to which the data is also made available with this work. With this second model, German tweets before and during the pandemic have been investigated. Extraverts display more positive emotions, whilst introverts show more insight and higher rates of anxiety. Even though such a model can not replace proper psychological diagnostics, it can help shed light on linguistic markers and to help understand introversion and extraversion better for a variety of applications and investigations.</abstract>
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%0 Conference Proceedings
%T Classification of German Jungian Extraversion and Introversion Texts with Assessment of Changes During the COVID-19 Pandemic
%A Johannßen, Dirk
%A Biemann, Chris
%A Scheffer, David
%Y Kokkinakis, Dimitrios
%Y Themistocleous, Charalambos K.
%Y Fors, Kristina Lundholm
%Y Tsanas, Athanasios
%Y Fraser, Kathleen C.
%S Proceedings of the RaPID Workshop - Resources and ProcessIng of linguistic, para-linguistic and extra-linguistic Data from people with various forms of cognitive/psychiatric/developmental impairments - within the 13th Language Resources and Evaluation Conference
%D 2022
%8 June
%I European Language Resources Association
%C Marseille, France
%F johannssen-etal-2022-classification
%X The corona pandemic and countermeasures such as social distancing and lockdowns have confronted individuals with new challenges for their mental health and well-being. It can be assumed that the Jungian psychology types of extraverts and introverts react differently to these challenges. We propose a Bi-LSTM model with an attention mechanism for classifying introversion and extraversion from German tweets, which is trained on hand-labeled data created by 335 participants. With this work, we provide this novel dataset for free use and validation. The proposed model achieves solid performance with F1 = .72. Furthermore, we created a feature engineered logistic model tree (LMT) trained on hand-labeled tweets, to which the data is also made available with this work. With this second model, German tweets before and during the pandemic have been investigated. Extraverts display more positive emotions, whilst introverts show more insight and higher rates of anxiety. Even though such a model can not replace proper psychological diagnostics, it can help shed light on linguistic markers and to help understand introversion and extraversion better for a variety of applications and investigations.
%U https://aclanthology.org/2022.rapid-1.5
%P 31-40
Markdown (Informal)
[Classification of German Jungian Extraversion and Introversion Texts with Assessment of Changes During the COVID-19 Pandemic](https://aclanthology.org/2022.rapid-1.5) (Johannßen et al., RaPID 2022)
ACL