The First Multilingual Model For The Detection of Suicide Texts (2025)

Abstract: Suicidal ideation is a serious health problem affecting millions of people worldwide. Social networks provide information about these mental health problems through users’ emotional expressions. We propose a multilingual model leveraging transformer architectures like mBERT, XML-R, and mT5 to detect suicidal text across posts in six languages - Spanish, English, German, Catalan, Portuguese and Italian. A Spanish suicide ideation tweet dataset was translated into five other languages using SeamlessM4T. Each model was fine-tuned on this multilingual data and evaluated across classification metrics. Results showed mT5 achieving the best performance overall with F1 scores above 85%, highlighting capabilities for cross-lingual transfer learning. The English and Spanish translations also displayed high quality based on perplexity. Our exploration underscores the importance of considering linguistic diversity in developing automated multilingual tools to identify suicidal risk. Limitations exist around semantic fidelity in translations and ethical implications which provide guidance for future human-in-the-loop evaluations.

## introduction according to data published by the world health organization (who), over 700,000 people die by suicide each year @xcite , with an additional 10 to 20 million attempting to take their own lives. suicidal behavior typically begins with thoughts and ideations of death, eventually leading to suicide attempts -conscious acts with the purpose of ending one's existence @xcite . in this context, social networks have become spaces where individuals often disclose emotions and information that they don't feel comfortable sharing with healthcare providers @xcite @xcite . early identification of signs of suicidal ideation in these online environments poses a major chal-lenge. this is where natural language processing (nlp) and deep learning (dl) can play a crucial role in the automatic detection of suicidal thoughts in computational settings. furthermore, these computational approaches may contribute to the development of tools for harm reduction and prevention. however, the majority of research on suicidal ideation detection has been conducted on english language data, resulting in a scarcity of linguistic resources (e.g.: datasets, lexicons, and language models) for most other languages. previous computational approaches to identifying suicidal ideation have relied heavily on handengineered features and domain expertise. for example, some studies have used structural and emotional features to train statistical prediction models on suicide text @xcite . additionally, conventional machine learning algorithms like logistic regression (lr) @xcite @xcite , decision tree (dt) @xcite , naive bayes (nb) @xcite @xcite , support vector machine (svm) @xcite @xcite , k-nearest neighbor algorithm (knn) @xcite and extreme gradient boost (xgboost) (rajesh @xcite @xcite have been applied. while achieving some success, these methods depend on costly feature engineering and professional knowledge. recently, deep learning has emerged as a promising approach that can automatically learn representations from data @xcite . moreover, deep learning techniques like cnns @xcite @xcite , lstms @xcite @xcite @xcite , bilstm @xcite @xcite and dlstmattention @xcite have been applied in detecting suicidal ideation, with competitive performance. on the other hand, with the increasing use of pre-trained language models such as bert @xcite , roberta @xcite , and mt5 @xcite , the landscape of suicide ideation detection has evolved significantly. these pre-trained models, developed through massive unsupervised learning on diverse linguistic tasks, offer a powerful foundation for understanding intricate nuances of language. researchers are now exploring the adaptation of those models for the detection of suicidal ideation @xcite @xcite . leveraging the contextual understanding encoded in these pretrained models, studies have reported promising results in discerning subtle and complex expressions related to suicidal thoughts, contributing to the advancement of automated detection systems @xcite . this shift towards pretrained language models signifies a paradigmatic enhancement in the field, as it allows for a more nuanced comprehension of linguistic patterns associated with suicidal ideation, thereby enhancing the overall accuracy and sensitivity of detection algorithms. in our approach, we emphasize the importance of addressing the detection of suicidal texts in the context of using multilingual language models. translating a corpus from spanish into five different languages and fine-tuning a multilingual language model allows us to classify suicidal texts in various languages, thus expanding the applicability of our approach. in our research, we aim to explore the effectiveness of multilingual pretrained language models such as mbert @xcite , xml-r @xcite and mt5 @xcite for detecting suicidal text on social media. the main focus of our study is to leverage these multilingual pretrained models to translate and recognize suicidal text in six languages: spanish, english, german, catalan, portuguese and italian. our primary contribution lies in the implementation of a multilingual language model with the capability to detect suicidal text in these six distinct languages. additionally, to address the lack of labeled suicidal text in other languages, we utilize a labeled corpus and translate it into five different languages using an automatic translation model (seamlessm4t @xcite ). therefore, this approach enables us to effectively tackle the linguistic diversity present on social media and provides a valuable tool for the early identification of suicidal content in various cultural and linguistic contexts. the main objectives of our study are: ## related work the initial approaches to automatic suicide risk detection were based on identifying specific language features present in psychiatric literature. for instance, in lumontod iii (2020); tadesse et al. however, the limitations of manual feature engineering in terms of scalability and adaptability have led to the exploration of more recent approaches based on deep learning, specifically pre-trained language models. in a comparative study @xcite , bert, roberta, bertweet, and mentalbert were evaluated on a reddit dataset, revealing that pre-trained models consistently outperformed traditional classifiers @xcite @xcite . in summary, pre-trained language models have shown promising results, often outperforming traditional methods in automatic suicide risk detection. however, most studies have been limited to relatively small datasets. regarding linguistic diversity, studies have predominantly focused on english data from platforms such as twitter @xcite , reddit @xcite @xcite , and facebook. nevertheless, no research has been found exploring multilingual language models for suicide risk detection. models like mbert @xcite , xlm-roberta @xcite , and mt5 @xcite , trained on multilingual data, could transfer linguistic knowledge across related languages, improving performance in low-resource situations for languages with less training data. as far as is known, there are also no studies utilizing automatically translated datasets to leverage data from other languages. the quality of automatic translations of datasets from a source language to a target language could be crucial in increasing dataset size and improving the performance of trained models. both research directions, i.e., multilingual models and automatic translation of data, represent promising yet unexplored areas for automatic suicide risk detection, opening opportunities for significant contributions in this field. ## experiments in this section, we delineate the setup of diverse experiments aimed at exploring the feasibility of a multilingual model capable of classifying suicidal texts across six different languages. ## results in this section, we present an analysis of the results obtained from our fine-tuned language models-mbert, xml-r and mt5 deployed in the task of suicidal text classification across six languages. our objective is to scrutinize the models' performance intricacies, assess their multilingual adaptability, and glean insights into the efficacy of lang. ## translation analysis for the translation of the spanish dataset into the other 5 target languages (english, catalan, german, italian and portuguese), this study employed the seamlessm4t 4 model developed by facebook @xcite . seamlessm4t is based on the transformer architecture, demonstrating the effectiveness of crosslingual model pretraining and transfer learning. in particular, it leverages a sequence-to-sequence model with encoder-decoder structure trained on large-scale data across multiple languages (100 languages). the key advantages of this specific architecture include: it was pretrained on a variety of language pairs, including spanish, english, catalan, italian and portuguese. it demonstrated excellent bleu metrics on translations between these languages, corroborating its suitability for the present crosslingual research task. ## discussion this study explores the use of pre-trained multilingual language models, including mbert, xml-r, and mt5, for the automatic detection of suicidal texts in social media posts across six languages: spanish, english, german, catalan, portuguese, and italian. the results show mt5 achieving the best performance overall, with f1 scores above 85%, highlighting capabilities for cross-lingual transfer learning. an interesting finding is that the relative gaps between models remain remarkably stable across languages. this implies that the inherent strengths of each model transcend linguistic particularities. while some languages are more complex, all benefit from mt5's architectural improvements over bert models. regarding limitations, direct extrapolation of the results to other languages must be approached cautiously, given the wide linguistic diversity and potential impact of cultural nuances on interpreting suicidal texts. furthermore, the quality of translations and, consequently, the predictive model, is inherently tied to the effectiveness of pre-trained models, indicating a constant need for improvements in this area. while this study presents a promising model for multilingual detection of suicidal texts, there are several directions to extend and strengthen this line of research. some of these include: expanding linguistic scope by incorporating a broader spectrum of languages; enriching training data with more instances and diversity of sources; using specialized metrics to quantify the usefulness of the early detection model; and implementation of a user-friendly interface enabling integration into healthcare settings. in summary, the focus on multilingual translation emerges as a crucial step in constructing an effective predictive model for suicidal texts across six languages. the identified conclusions and limitations provide guidance for future developments, emphasizing the need for linguistic and cultural considerations. ## conclusion in the pursuit of a predictive model for suicidal texts in six languages, our exploration into multilingual translation yields critical insights. we observe that translations into english and portuguese excel, showcasing the ability to preserve intent and coherence in sensitive contexts such as suicidal content. sensitivity to linguistic diversity emerges as a pivotal element in this process. while synchronization in translations into catalan was acceptable, adaptations into german and italian posed challenges, underscoring the importance of considering linguistic nuances in constructing a robust predictive model. the versatility of multilingual models, especially mt5, proves to be a valuable resource in this scenario. these models demonstrate a remarkable ability to maintain the integrity of suicidal content across diverse languages, providing a solid foundation for building a multilingual predictive model. automated evaluation, though guided by objective metrics such as perplexity, does not replace human assessment for sensitivity and semantic fidelity in suicidal content. the implementation of human evaluations in subsequent phases is essential to ensure the appropriateness and ethical considerations of the model. in summary, our focus on multilingual translation emerges as a crucial step in constructing a predictive model for suicidal texts in six languages. the identified conclusions and limitations provide guidance for future developments, emphasizing the need for linguistic and cultural considerations, as well as continuous improvements in pre-trained models and human evaluations to achieve an effective and ethical model.