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    <abstract>This paper describes our submission to Social Media Mining for Health (SMM4H) 2022 Shared Task 8, aimed at detecting self-reported chronic stress on Twitter. Our approach leverages a pre-trained transformer model (RoBERTa) in combination with a Bidirectional Long Short-Term Memory (BiLSTM) network trained on a diverse set of psycholinguistic features. We handle the class imbalance issue in the training dataset by augmenting it by another dataset used for stress classification in social media.</abstract>
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%0 Conference Proceedings
%T MANTIS at SMM4H’2022: Pre-Trained Language Models Meet a Suite of Psycholinguistic Features for the Detection of Self-Reported Chronic Stress
%A Zanwar, Sourabh
%A Wiechmann, Daniel
%A Qiao, Yu
%A Kerz, Elma
%S Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task
%D 2022
%8 October
%I Association for Computational Linguistics
%C Gyeongju, Republic of Korea
%F zanwar-etal-2022-mantis
%X This paper describes our submission to Social Media Mining for Health (SMM4H) 2022 Shared Task 8, aimed at detecting self-reported chronic stress on Twitter. Our approach leverages a pre-trained transformer model (RoBERTa) in combination with a Bidirectional Long Short-Term Memory (BiLSTM) network trained on a diverse set of psycholinguistic features. We handle the class imbalance issue in the training dataset by augmenting it by another dataset used for stress classification in social media.
%U https://aclanthology.org/2022.smm4h-1.5
%P 16-18

Markdown (Informal)

[MANTIS at SMM4H’2022: Pre-Trained Language Models Meet a Suite of Psycholinguistic Features for the Detection of Self-Reported Chronic Stress](https://aclanthology.org/2022.smm4h-1.5) (Zanwar et al., SMM4H 2022)

• MANTIS at SMM4H’2022: Pre-Trained Language Models Meet a Suite of Psycholinguistic Features for the Detection of Self-Reported Chronic Stress (Zanwar et al., SMM4H 2022)

ACL

• Sourabh Zanwar, Daniel Wiechmann, Yu Qiao, and Elma Kerz. 2022. MANTIS at SMM4H’2022: Pre-Trained Language Models Meet a Suite of Psycholinguistic Features for the Detection of Self-Reported Chronic Stress. In Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task, pages 16–18, Gyeongju, Republic of Korea. Association for Computational Linguistics.