Towards Mental Health Analysis in Social Media for Low-resourced Languages

IF 1.8 4区计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

ACM Transactions on Asian and Low-Resource Language Information Processing Pub Date : 2023-12-30 DOI:10.1145/3638761

Muskan Garg

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引用次数: 0

Abstract

The surge in internet use for expression of personal thoughts and beliefs has made it increasingly feasible for the social Natural Language Processing (NLP) research community to find and validate associations between social media posts and mental health status. Cross-sectional and longitudinal studies of low-resourced social media data bring to fore the importance of real-time responsible Artificial Intelligence (AI) models for mental health analysis in native languages. Aiming to classify research for social computing and tracking advances in the development of learning-based models, we propose a comprehensive survey on mental health analysis for social media and posit the need of analyzing low-resourced social media data for mental health. We first classify three components for computing on social media as: SM- data mining/ natural language processing on social media, IA- integrated applications with social media data and user-network modeling, and NM- user and network modeling on social networks. To this end, we posit the need of mental health analysis in different languages of East Asia (e.g. Chinese, Japanese, Korean), South Asia (Hindi, Bengali, Tamil), Southeast Asia (Malay, Thai, Vietnamese), European languages (Spanish, French) and the Middle East (Arabic). Our comprehensive study examines available resources and recent advances in low-resourced languages for different aspects of SM, IA and NM to discover new frontiers as potential field of research.

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在社交媒体中对低资源语言进行心理健康分析

互联网在表达个人思想和信念方面的使用激增，使得社会自然语言处理（NLP）研究界越来越有可能发现和验证社交媒体帖子与心理健康状况之间的关联。对资源匮乏的社交媒体数据进行的横向和纵向研究凸显了实时负责任的人工智能（AI）模型对于用母语进行心理健康分析的重要性。为了对社交计算研究进行分类，并跟踪基于学习的模型开发进展，我们提出了一项关于社交媒体心理健康分析的综合调查，并提出了分析低资源社交媒体数据以促进心理健康的必要性。我们首先将社交媒体计算的三个组成部分分类为SM--社交媒体数据挖掘/自然语言处理；IA--社交媒体数据和用户网络建模的集成应用；NM--社交网络的用户和网络建模。为此，我们提出了用东亚（如汉语、日语、韩语）、南亚（印地语、孟加拉语、泰米尔语）、东南亚（马来语、泰语、越南语）、欧洲语言（西班牙语、法语）和中东（阿拉伯语）的不同语言进行心理健康分析的需求。我们的综合研究考察了低资源语言在 SM、IA 和 NM 不同方面的可用资源和最新进展，以发现潜在研究领域的新前沿。

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来源期刊

ACM Transactions on Asian and Low-Resource Language Information Processing Computer Science-General Computer Science

CiteScore

3.60

自引率

15.00%

发文量

241

期刊介绍： The ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) publishes high quality original archival papers and technical notes in the areas of computation and processing of information in Asian languages, low-resource languages of Africa, Australasia, Oceania and the Americas, as well as related disciplines. The subject areas covered by TALLIP include, but are not limited to: -Computational Linguistics: including computational phonology, computational morphology, computational syntax (e.g. parsing), computational semantics, computational pragmatics, etc. -Linguistic Resources: including computational lexicography, terminology, electronic dictionaries, cross-lingual dictionaries, electronic thesauri, etc. -Hardware and software algorithms and tools for Asian or low-resource language processing, e.g., handwritten character recognition. -Information Understanding: including text understanding, speech understanding, character recognition, discourse processing, dialogue systems, etc. -Machine Translation involving Asian or low-resource languages. -Information Retrieval: including natural language processing (NLP) for concept-based indexing, natural language query interfaces, semantic relevance judgments, etc. -Information Extraction and Filtering: including automatic abstraction, user profiling, etc. -Speech processing: including text-to-speech synthesis and automatic speech recognition. -Multimedia Asian Information Processing: including speech, image, video, image/text translation, etc. -Cross-lingual information processing involving Asian or low-resource languages. -Papers that deal in theory, systems design, evaluation and applications in the aforesaid subjects are appropriate for TALLIP. Emphasis will be placed on the originality and the practical significance of the reported research.