Exploring BERT-Based Classification Models for Detecting Phobia Subtypes: A Novel Tweet Dataset and Comparative Analysis (2024.lrec-main)
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| Challenge: | Phobias are characterized by an intense and irrational fear of specific objects, situations, or activities despite there being no real risk or only a minor threat involved. |
| Approach: | They propose to use a dataset of 811,569 English tweets from user timelines spanning 102 phobia subtypes over six months to classify users into 65 specific phobias. |
| Outcome: | The proposed dataset includes 47,614 self-diagnosed phobia users and a high f1 score for binary classification and multi-class classification. |
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Classifying Social Media Users before and after Depression Diagnosis via Their Language Usage: A Dataset and Study (2024.lrec-main)
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| Challenge: | Mental illness can negatively impact individuals’ quality of life as it is considered one of the causes of years lived with disability and it is related to high suicide rates. |
| Approach: | They collect first dataset of textual posts by same users before and after being diagnosed with depression and build multiple predictive models based on Transformers and BERT. |
| Outcome: | The proposed model can be used to detect depression and suicidal thoughts in users who are not diagnosed with depression or suicide. |
Understanding Emotions: A Dataset of Tweets to Study Interactions between Affect Categories (L18-1)
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| Challenge: | a new dataset is used to classify text into positive, negative, and neutral classes . a large amount of work on automatic detecting emotions from text has focused on classifying text into basic emotion categories . |
| Approach: | They use Twitter as the source of the textual data they annotate to find out which emotions often present together in tweets . |
| Outcome: | The proposed dataset is useful for training and testing supervised machine learning algorithms . it is based on the results of the SemEval-2018 task 1: Affect in Tweets . |
Still Not Quite There! Evaluating Large Language Models for Comorbid Mental Health Diagnosis (2024.emnlp-main)
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Amey Hengle, Atharva Kulkarni, Shantanu Patankar, Madhumitha Chandrasekaran, Sneha D’silva, Jemima Jacob, Rashmi Gupta
| Challenge: | ANGST is a benchmark for depression-anxiety comorbidity classification from social media posts. |
| Approach: | They propose a social media-based benchmark for depression-anxiety comorbidity classification . ANGST enables multi-label classification, allowing each post to be simultaneously identified as indicating depression and/or anxiety. |
| Outcome: | The proposed dataset enables multi-label classification of depression and anxiety . it outperforms existing models but none achieves an F1 score exceeding 72% . |
MentalHelp: A Multi-Task Dataset for Mental Health in Social Media (2024.lrec-main)
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Nishat Raihan, Sadiya Sayara Chowdhury Puspo, Shafkat Farabi, Ana-Maria Bucur, Tharindu Ranasinghe, Marcos Zampieri
| Challenge: | Annotating social media data for mental health disorders is expensive and time-consuming, limiting their size and scope. |
| Approach: | They present a large-scale semi-supervised mental disorder detection dataset containing 14 million instances from Reddit and an ensemble of three separate models. |
| Outcome: | The proposed dataset contains 14 million instances of mental disorders . it was collected from reddit and labeled in a semi-supervised way . |
Adapting Deep Learning Methods for Mental Health Prediction on Social Media (D19-55)
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| Challenge: | a quarter of the population in Europe suffers from an episode of a mental disorder in their life, according to the World Health Organization . text analysis of rich resources like social media can contribute to deeper understanding of mental health and provide means for their early detection. |
| Approach: | They propose to use a hierarchical attention network to predict if a user suffers from one of nine disorders to adapt a deep neural model to the task. |
| Outcome: | The proposed model outperforms previous benchmarks for four out of nine disorders in a binary classification task on social media. |
Sensitive Data Detection and Classification in Spanish Clinical Text: Experiments with BERT (2020.lrec-1)
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| Challenge: | Massive digital data processing can endanger personal data privacy . anonymisation involves removing or replacing sensitive information from data . |
| Approach: | They propose to use a BERT-based sequence labelling model to conduct an experiment on clinical datasets in Spanish. |
| Outcome: | The proposed model outperforms existing models on clinical datasets in Spanish and shows that it is highly competitive with other models. |
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. |
Detecting Perceived Emotions in Hurricane Disasters (2020.acl-main)
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| Challenge: | Existing methods for emotion detection are limited in disaster-centric domains due to distributional shifts. |
| Approach: | They propose to use a Twitter emotion dataset to analyze emotions in natural disasters . they propose to apply classification tasks to discriminate between coarse-grained emotions . |
| Outcome: | The proposed model achieves only 68% accuracy after pre-training with unlabeled Twitter data. |
What BERT Is Not: Lessons from a New Suite of Psycholinguistic Diagnostics for Language Models (2020.tacl-1)
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| Challenge: | Pretraining by language modeling has become popular but we have yet to understand what language models learn during that process. |
| Approach: | They propose diagnostics that ask questions about information used by language models for generating predictions in context. |
| Outcome: | The proposed diagnostics can be used to study the popular BERT model . they show that the model can distinguish good from bad completions, but struggles with inference and role-based event prediction. |
Multi-source Multi-domain Sentiment Analysis with BERT-based Models (2022.lrec-1)
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| Challenge: | Sentiment analysis is a widely studied task in natural language processing. |
| Approach: | They propose to improve BERT-based models for sentiment analysis on italian corpora and evaluate their performance on the basis of eight corpors. |
| Outcome: | The proposed model is evaluated over eight sentiment analysis corpora from different domains and sources on the prediction of positive, negative and neutral classes. |