面向可持续城市与社会的社交媒体与人工智能：一个水质分析用例
Social Media and Artificial Intelligence for Sustainable Cities and Societies: A Water Quality Analysis Use-case
AI总结：
本文提出一种融合多个大语言模型与BERTopic的NLP框架，自动分析社交媒体水相关帖子，以支持水质数据驱动决策，并构建了大规模标注数据集。
摘要：
本文聚焦于水质分析这一非常重要的社会挑战。作为经济社会发展的关键因素之一，供水及其质量保障一直是公共当局的首要任务之一。为确保水质，人们采用不同的方法来监测和评估供水网络，例如线下和线上调查。然而，这些调查存在若干局限性，例如参与者数量有限以及由于开展此类调查所涉及的人力劳动而导致的频率较低。在本文中，我们提出了一种自然语言处理（NLP）框架，用于自动收集和分析社交媒体上与水资源相关的帖子，以支持数据驱动的决策。所提出的框架由两个组件组成，即（i）文本分类和（ii）主题建模。在文本分类方面，我们提出了一种基于价值融合的框架，该框架融合了多个大型语言模型（LLMs），其中采用不同的权重选择和优化方法来为这些LLMs分配权重。在主题建模方面，我们使用BERTopic库来发现与水相关推文中的隐藏主题模式。我们还分析了来自不同地区和国家的相关推文，以探讨全球性、区域性和国家特定问题以及与水资源相关的关切。我们还收集并人工标注了一个大规模数据集，预计将有助于未来在该主题上的研究。
英文摘要：
This paper focuses on a very important societal challenge of water quality analysis. Being one of the key factors in the economic and social development of society, the provision of water and ensuring its quality has always remained one of the top priorities of public authorities. To ensure the quality of water, different methods for monitoring and assessing the water networks, such as offline and online surveys, are used. However, these surveys have several limitations, such as the limited number of participants and low frequency due to the labor involved in conducting such surveys. In this paper, we propose a Natural Language Processing (NLP) framework to automatically collect and analyze water-related posts from social media for data-driven decisions. The proposed framework is composed of two components, namely (i) text classification, and (ii) topic modeling. For text classification, we propose a merit-fusion-based framework incorporating several Large Language Models (LLMs) where different weight selection and optimization methods are employed to assign weights to the LLMs. In topic modeling, we employed the BERTopic library to discover the hidden topic patterns in the water-related tweets. We also analyzed relevant tweets originating from different regions and countries to explore global, regional, and country-specific issues and water-related concerns. We also collected and manually annotated a large-scale dataset, which is expected to facilitate future research on the topic.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
