Multi-modal social and psycho-linguistic embedding via recurrent neural networks to identify depressed users in online forums

Shrestha, Anu; Serra, Edoardo; Spezzano, Francesca

doi:10.1007/s13721-020-0226-0

Cited by 27 publications

(13 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Unsupervised learning methods to discover patterns from unlabeled data, such as clustering data 55 , 104 , 105 , or by using LDA topic model 27 . However, in most cases, we can apply these unsupervised models to extract additional features for developing supervised learning classifiers 56 , 85 , 106 , 107 .…”

Section: Resultsmentioning

confidence: 99%

Natural language processing applied to mental illness detection: a narrative review

et al. 2022

View full text Add to dashboard Cite

Mental illness is highly prevalent nowadays, constituting a major cause of distress in people’s life with impact on society’s health and well-being. Mental illness is a complex multi-factorial disease associated with individual risk factors and a variety of socioeconomic, clinical associations. In order to capture these complex associations expressed in a wide variety of textual data, including social media posts, interviews, and clinical notes, natural language processing (NLP) methods demonstrate promising improvements to empower proactive mental healthcare and assist early diagnosis. We provide a narrative review of mental illness detection using NLP in the past decade, to understand methods, trends, challenges and future directions. A total of 399 studies from 10,467 records were included. The review reveals that there is an upward trend in mental illness detection NLP research. Deep learning methods receive more attention and perform better than traditional machine learning methods. We also provide some recommendations for future studies, including the development of novel detection methods, deep learning paradigms and interpretable models.

show abstract

Section: Resultsmentioning

confidence: 99%

Natural language processing applied to mental illness detection: a narrative review

et al. 2022

View full text Add to dashboard Cite

show abstract

“…When studying a foreign language, the internet provides access to various resources, which diversifies learning opportunities. Studies of the web environment for language learning [18,19] show that online communication transforms teaching practices and influences learning. Colaric and Jonassen [20] address the educational context's role in making foreign language learning genuinely helpful.…”

Section: Introductionmentioning

confidence: 99%

Online Communication Tools in Teaching Foreign Languages for Education Sustainability

et al. 2021

View full text Add to dashboard Cite

Higher education curricula are developed based on creating conditions for implementing many professional and universal competencies. In Russia, one of the significant competencies for a modern specialist is business communication in oral and written forms in the Russian language and a foreign language. Therefore, teaching students to write in a foreign language is one of the modern requirements for young specialists’ professional training. This article aimed to study the tools of online communication that are used in teaching foreign languages. The article presents the results of an empirical study and analysis of factors of application of online communication technologies in foreign languages teaching, synchronous and asynchronous means used in online learning, and the advantages of integrating online communication tools into the educational process of teaching a foreign language. The study of the advantages and disadvantages of educational platforms is based on an analysis of information and online communication technologies used in the educational process. Based on the example of teaching a foreign language, the article shows how online communication platforms are involved in the educational process. Furthermore, the teachers’ and students’ opinions are presented on how online platforms are convenient. The survey involved 928 first-year students and 76 foreign language teachers of Volgograd State University (Volgograd), Linguistics University of Nizhny Novgorod (Nizhny Novgorod), Tomsk Polytechnic University (Tomsk), Ural Federal University, named after the first President of Russia B.N. Yeltsin (Ekaterinburg). It was revealed that the integration of online communication tools has positive effects on the formation and development of written communication skills.

show abstract

“…In another recent work [60], the authors discuss depression among users during the COVID-19 pandemic using LSTM and fastText [31] embeddings. In [46], the authors also propose a multi-model RNN-based model for depression prediction but apply their model on online user forum datasets. Trotzek et al, [50] study the problem of early detection of depression from social media using deep learning where they leverage different word embeddings in an ensemble-based learning setup.…”

Section: User-level Behavioursmentioning

confidence: 99%

Explainable depression detection with multi-aspect features using a hybrid deep learning model on social media

et al. 2022

View full text Add to dashboard Cite

The ability to explain why the model produced results in such a way is an important problem, especially in the medical domain. Model explainability is important for building trust by providing insight into the model prediction. However, most existing machine learning methods provide no explainability, which is worrying. For instance, in the task of automatic depression prediction, most machine learning models lead to predictions that are obscure to humans. In this work, we propose explainable Multi-Aspect Depression Detection with Hierarchical Attention Network MDHAN, for automatic detection of depressed users on social media and explain the model prediction. We have considered user posts augmented with additional features from Twitter. Specifically, we encode user posts using two levels of attention mechanisms applied at the tweet-level and word-level, calculate each tweet and words’ importance, and capture semantic sequence features from the user timelines (posts). Our hierarchical attention model is developed in such a way that it can capture patterns that leads to explainable results. Our experiments show that MDHAN outperforms several popular and robust baseline methods, demonstrating the effectiveness of combining deep learning with multi-aspect features. We also show that our model helps improve predictive performance when detecting depression in users who are posting messages publicly on social media. MDHAN achieves excellent performance and ensures adequate evidence to explain the prediction.

show abstract

Multi-modal social and psycho-linguistic embedding via recurrent neural networks to identify depressed users in online forums

Cited by 27 publications

References 23 publications

Natural language processing applied to mental illness detection: a narrative review

Natural language processing applied to mental illness detection: a narrative review

Online Communication Tools in Teaching Foreign Languages for Education Sustainability

Explainable depression detection with multi-aspect features using a hybrid deep learning model on social media

Contact Info

Product

Resources

About