The rising prevalence of mental health disorders necessitates the development of robust, automated tools for early detection and monitoring. Recent advances in Natural Language Processing (NLP), particularly transformer-based architectures, have demonstrated significant potential in text analysis. This study provides a comprehensive evaluation of state-of-the-art transformer models (BERT, RoBERTa, DistilBERT, ALBERT, and ELECTRA) against Long Short-Term Memory (LSTM) based approaches using different text embedding techniques for mental health disorder classification on Reddit. We construct a large annotated dataset, validating its reliability through statistical judgmental analysis and topic modeling. Experimental results demonstrate the superior performance of transformer models over traditional deep-learning approaches. RoBERTa achieved the highest classification performance, with a 99.54% F1 score on the hold-out test set and a 96.05% F1 score on the external test set. Notably, LSTM models augmented with BERT embeddings proved highly competitive, achieving F1 scores exceeding 94% on the external dataset while requiring significantly fewer computational resources. These findings highlight the effectiveness of transformer-based models for real-time, scalable mental health monitoring. We discuss the implications for clinical applications and digital mental health interventions, offering insights into the capabilities and limitations of state-of-the-art NLP methodologies in mental disorder detection.

Multiclass hate speech detection across demographic categories remains challenging due to implicit targeting strategies and linguistic variability in social media content. This study provides an evaluation of 303 model configurations across three methodological paradigms: traditional machine learning (147 configurations), deep learning with pre-trained embeddings (144 configurations), and transformer models (12 implementations). Using 39,747 tweets spanning five demographic hate speech categories (age, ethnicity, gender, religion, other_hate), we conducted 5-fold cross-validation to establish performance benchmarks across all approaches. Results reveal a clear performance hierarchy with RoBERTa achieving 95.02% accuracy and 95.04% weighted F1-score, followed by Word2Vec-enhanced InceptionCNN at 94.31% accuracy and 94.35% F1-score. Traditional machine learning demonstrates exceptional efficiency, with SGDClassifier achieving 94.10% accuracy and 94.17% F1-score in only 11.5 seconds for a complete cross-validation cycle. Gender-based and other_hate speech categories prove most challenging across all methodologies, exhibiting distinct linguistic patterns that complicate automated detection. These findings provide empirical guidance for developing scalable hate speech detection systems capable of fine-grained demographic targeting identification.

Millions of people openly share mental health struggles on social media, providing rich data for early detection of conditions such as depression, bipolar disorder, etc. However, most prior Natural Language Processing (NLP) research has focused on single-disorder identification, leaving a gap in understanding the efficacy of advanced NLP techniques for distinguishing among multiple mental health conditions. In this work, we present a large-scale comparative study of state-of-the-art transformer versus Long Short-Term Memory (LSTM)-based models to classify mental health posts into exclusive categories of mental health conditions. We first curate a large dataset of Reddit posts spanning six mental health conditions and a control group, using rigorous filtering and statistical exploratory analysis to ensure annotation quality. We then evaluate five transformer architectures (BERT, RoBERTa, DistilBERT, ALBERT, and ELECTRA) against several LSTM variants (with or without attention, using contextual or static embeddings) under identical conditions. Experimental results show that transformer models consistently outperform the alternatives, with RoBERTa achieving 91-99% F1-scores and accuracies across all classes. Notably, attention-augmented LSTMs with BERT embeddings approach transformer performance (up to 97% F1-score) while training 2-3.5 times faster, whereas LSTMs using static embeddings fail to learn useful signals. These findings represent the first comprehensive benchmark for multi-class mental health detection, offering practical guidance on model selection and highlighting an accuracy–efficiency trade-off for real-world deployment of mental health NLP systems.

Bipolar disorder is a chronic mental illness frequently underdiagnosed due to subtle early symptoms and social stigma.This paper explores the advanced natural language processing (NLP) models for recognizing signs of bipolar disorder based on user-generated social media text.We conduct a comprehensive evaluation of transformer-based models (BERT, RoBERTa, ALBERT, ELECTRA, DistilBERT) and Long Short Term Memory (LSTM) models based on contextualized (BERT) and static (GloVe, Word2Vec) word embeddings.Experiments were performed on a large, annotated dataset of Reddit posts after confirming their validity through sentiment variance and judgmental analysis.Our results demonstrate that RoBERTa achieves the highest performance among transformer models with an F1 score of ∼98% while LSTM models using BERT embeddings yield nearly identical results.In contrast, LSTMs trained on static embeddings fail to capture meaningful patterns, scoring near-zero F1.These findings underscore the critical role of contextual language modeling in detecting bipolar disorder.In addition, we report model training times and highlight that DistilBERT offers an optimal balance between efficiency and accuracy.In general, our study offers actionable insights for model selection in mental health NLP applications and validates the potential of contextualized language models to support early bipolar disorder screening.

Suicide is a critical global health problem involving more than 700,000 deaths yearly, particularly among young adults. Many people express their suicidal thoughts on social media platforms such as Reddit. This paper evaluates the effectiveness of the deep learning transformer-based models BERT, RoBERTa, DistilBERT, ALBERT, and ELECTRA and various Long Short-Term Memory (LSTM) based models in detecting suicidal ideation from user posts on Reddit. Toward this objective, we curated an extensive dataset from diverse subreddits and conducted linguistic, topic modeling, and statistical analyses to ensure data quality. Our results indicate that each model could reach high accuracy and F1 scores, but among them, RoBERTa emerged as the most effective model with an accuracy of 93.22% and F1 score of 93.14%. An LSTM model that uses attention and BERT embeddings performed as the second best, with an accuracy of 92.65 % and an F1 score of 92.69 %. Our findings show that transformer-based models have the potential to improve suicide ideation detection, thereby providing a path to develop robust mental health monitoring tools from social media. This research, therefore, underlines the undeniable prospect of advanced techniques in Natural Language Processing (NLP) while improving suicide prevention efforts.

Background. Studies on developer productivity and well-being find that the perceptions of productivity in a software team can be a socio-technical problem. Intuitively, problems and challenges can be better handled by managing expectations in software teams. Aim. Our goal is to understand whether the expectations of software developers vary towards diverse stakeholders in software teams. Method. We surveyed 181 professional software developers to understand their expectations from five different stakeholders: (1) organizations, (2) managers, (3) peers, (4) new hires, and (5) government and educational institutions. The five stakeholders are determined by conducting semi-formal interviews of software developers. We ask open-ended survey questions and analyze the responses using open coding. Results. We observed 18 multi-faceted expectations types. While some expectations are more specific to a stakeholder, other expectations are cross-cutting. For example, developers expect work-benefits from their organizations, but expect the adoption of standard software engineering (SE) practices from their organizations, peers, and new hires. Conclusion. Out of the 18 categories, three categories are related to career growth. This observation supports previous research that happiness cannot be assured by simply offering more money or a promotion. Among the most number of responses, we find expectations from educational institutions to offer relevant teaching and from governments to improve job stability, which indicate the increasingly important roles of these organizations to help software developers. This observation can be especially true during the COVID-19 pandemic.