Dirk Johannßen


2022

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Classification of German Jungian Extraversion and Introversion Texts with Assessment of Changes During the COVID-19 Pandemic
Dirk Johannßen | Chris Biemann | David Scheffer
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

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.

2020

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Social Media Unrest Prediction during the COVID-19 Pandemic: Neural Implicit Motive Pattern Recognition as Psychometric Signs of Severe Crises
Dirk Johannßen | Chris Biemann
Proceedings of the Third Workshop on Computational Modeling of People's Opinions, Personality, and Emotion's in Social Media

The COVID-19 pandemic has caused international social tension and unrest. Besides the crisis itself, there are growing signs of rising conflict potential of societies around the world. Indicators of global mood changes are hard to detect and direct questionnaires suffer from social desirability biases. However, so-called implicit methods can reveal humans intrinsic desires from e.g. social media texts. We present psychologically validated social unrest predictors and replicate scalable and automated predictions, setting a new state of the art on a recent German shared task dataset. We employ this model to investigate a change of language towards social unrest during the COVID-19 pandemic by comparing established psychological predictors on samples of tweets from spring 2019 with spring 2020. The results show a significant increase of the conflict indicating psychometrics. With this work, we demonstrate the applicability of automated NLP-based approaches to quantitative psychological research.

2019

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Reviving a psychometric measure: Classification and prediction of the Operant Motive Test
Dirk Johannßen | Chris Biemann | David Scheffer
Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology

Implicit motives allow for the characterization of behavior, subsequent success and long-term development. While this has been operationalized in the operant motive test, research on motives has declined mainly due to labor-intensive and costly human annotation. In this study, we analyze over 200,000 labeled data items from 40,000 participants and utilize them for engineering features for training a logistic model tree machine learning model. It captures manually assigned motives well with an F-score of 80%, coming close to the pairwise annotator intraclass correlation coefficient of r = .85. In addition, we found a significant correlation of r = .2 between subsequent academic success and data automatically labeled with our model in an extrinsic evaluation.