Depression remains one of the most prevalent mental health conditions worldwide, yet most automated detection systems focus on English text and overlook the multilingual, multimodal nature of social media communication. This limitation is particularly significant in India, where users frequently write using Hinglish and other Hindi variants, posing substantial challenges for standard language processing pipelines. This study introduces a time-aware multilingual multimodal framework for detecting depression-related expressions in social media posts collected from X (formerly Twitter). The model jointly learns from text, images, and posting time, enabling it to capture how users express themselves across languages and modalities. To support this work, we construct and annotate a dataset of 15,739 posts from 3,471 users covering four linguistic varieties: English, Roman Hindi, Devanagari Hindi, and Hinglish. The contributions of this work are twofold: it presents a multilingual multimodal dataset for Indian social media depression-related content detection, and it provides a systematic evaluation of encoding and fusion strategies, achieving an F1-score of 0.782 with cross-lingual transfer capability demonstrated on a Bengali corpus. All outputs are intended as a content-screening aid and do not constitute clinical diagnosis.
Large Language Models (LLMs) have demonstrated remarkable capabilities across various natural language processing tasks, yet their effectiveness for Arabic language processing remains underexplored. This paper presents an in-depth evaluation of instruction tuning for Arabic NLP tasks using three prominent LLMs: LLaMA 3.1-8B, AceGPT-v2-8B, and Qwen3-8B. To support this evaluation and future Arabic NLP research, we introduce STAR (inStruction Tuning for ARabic), a publicly released collection of 355 Jinja2 instruction templates spanning 87 datasets and 20 NLP tasks, together with over 46 million rendered instruction–output pairs. Using a 60-template subset of STAR, we evaluate three model variants for each LLM—base, instruction-tuned, and custom LoRA-tuned—on six diverse Arabic NLP tasks (MCQ-based Natural Language Understanding, Natural Language Inference, Dialect Identification, Sarcasm Detection, Machine Translation, and Summarization) across 12 datasets. We employ both intra-dataset evaluation (generalization within training distribution) and intra-task evaluation (generalization across datasets of the same task) to assess model robustness. Results demonstrate that instruction tuning consistently improves performance across most tasks, with notable variations in effectiveness across different tasks and prompts. Models show sensitivity to cross-dataset generalization and prompt styles, while task-specific tuning noticeably reduces prompt sensitivity. Our findings reveal that optimal results depend on careful prompt engineering and task-specific settings, providing insights for developing more effective Arabic language modeling practices. Our code is available at https://github.com/KFUPM-JRCAI/instructions-tuning .
This article presents the Russian Language-Monolingual corpus (RusLan-M, v.1.0), a longitudinal multimedia collection of early child speech from two Russian-speaking monolingual children: Tosya (ages 0;10–3;10, 246 recordings) and Yasha (ages 1;04–3;00, 42 recordings). The corpus consists of approximately 41 h (2,454 min.) of video recordings and 35,386 child utterances, available with transcriptions in the CHAT format on TalkBank. The corpus adheres to strict ethics requirements for data sharing with anonymization. We also conducted two exploratory investigations of the acquisition of Russian morphology using the mean length of utterance (MLU) and the newly developed Index of Productive Syntax for evaluating grammatical complexity in the nominal system of Russian (IPSyn-NP-R). These investigations illustrate how a comprehensive analysis of syntactic and morphological structures in Russian language development can be conducted and highlight the kinds of questions that can be answered using the RusLan-M data. The RusLan-M corpus represents an application of corpus linguistics methods to the Russian language and fills a significant gap in the limited data and resources available for Russian child language research.
With the rise of conversational AI, sentiment-aware chatbots have become essential for enhancing human-machine interactions, allowing for more natural and engaging conversations by making the chatbot aware of the user’s current sentiment before providing a response. In this study, we present a sentiment-aware chatbot optimized for the Arabic language. The Arabic language is known for its complexity and is one of the most widely used languages worldwide, particularly among Arabic-speaking countries. Unlike traditional chatbots, which struggle with contextual understanding, our approach integrates sentiment analysis to generate emotionally adaptive responses. We employ large language models fine-tuned on two Arabic datasets for sentiment classification, achieving state-of-the-art results on the classification task with an accuracy of 91.63
Detecting sarcasm in Arabic remains a challenging task due to the complex linguistic and contextual nature of sarcastic expressions. Sarcasm often contradicts the literal sentiment of a statement, requiring models to understand both the surface meaning and the underlying emotional tone. This challenge is further compounded by Arabic’s rich morphology, diverse dialects, and nuanced social media language. To address these difficulties, we propose a hybrid cascading multi-task model that leverages sentiment information to enhance sarcasm detection. The model first learns sentiment representations through a dedicated head, which are then integrated with contextual embeddings in the sarcasm detection head, capturing the interaction between sentiment and sarcastic intent. Residual connections stabilize learning and preserve fine-grained semantics. Experiments conducted on Arabic datasets show that our model outperforms fine-tuned baseline models and is competitive with most existing approaches. The results confirm that cascading sentiment signals help improve sarcasm detection in Arabic social media text.
