Papers by Sourabh Zanwar
SMHD-GER: A Large-Scale Benchmark Dataset for Automatic Mental Health Detection from Social Media in German (2023.findings-eacl)
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| Challenge: | Mental health problems are a challenge to our modern society, and their prevalence is predicted to increase worldwide. |
| Approach: | They propose a large-scale, carefully constructed dataset for MHC detection built on high-precision patterns and the approach proposed for English. |
| Outcome: | The proposed model leverages engineered (psycho-)linguistic features as well as BERT-German to facilitate further research and conduct extensive experiments. |
SPADE: A Big Five-Mturk Dataset of Argumentative Speech Enriched with Socio-Demographics for Personality Detection (2022.lrec-1)
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| Challenge: | Recent efforts to create such datasets from social media do not include continuous and contextualized language use. |
| Approach: | They propose to use argumentative speech to generate a dataset with continuous arguments labeled with the Big Five personality traits and enriched with socio-demographic data. |
| Outcome: | The proposed model leverages 436 (psycho)linguistic features extracted from transcribed speech and speaker-level metainformation with transformers to investigate which types of features contribute to the prediction of individual personality traits. |
What to Fuse and How to Fuse: Exploring Emotion and Personality Fusion Strategies for Explainable Mental Disorder Detection (2023.findings-acl)
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| Challenge: | Mental health disorders (MHD) are one of the greatest challenges facing our healthcare systems and modern societies in general. |
| Approach: | They integrate and extend the research by conducting extensive experiments with three types of deep learning-based fusion strategies: feature-level fusion, model fusion and task fusion. |
| Outcome: | The proposed techniques show that they can be used to improve mental health detection from textual data. |