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Harnessing Social Media for Environmental Sustainability: A Measurement Study on Harmful Algal Blooms

机译:利用社交媒体促进环境可持续发展:有害藻华的测量研究

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In recent years, social media has revolutionized citizen science activities. Given its popularity among people and communities, these social media services could be used effectively for environmental surveillance. However in social media, people use different terms to refer to same event for example, Blue Green Algae, Cyan bacteria, Algae Bloom and Red Tide refer to same event but one is very technical and other is more generic term. The technical terms are normally known to field experts or the domain scientists which inherently would mean more reliable information on social media but the more generic term is used by people of various backgrounds putting a question on the trustworthiness of the post. Moreover, the user base and the number of posts for more technical terms are relatively less compared to the generic terms. But the dichotomy is that the more common the term, the more noisy the data. One can say using generic terms to track the environmental events would be more effective. But the social media data has lot of flux thus using train once and classify ever model of machine learning will miss to classify many of the relevant events as shown in the paper. Our research seeks to explore the various opportunities, challenges and approaches in using social media for environmental monitoring.
机译:近年来,社交媒体彻底改变了公民科学活动。鉴于其在人们和社区中的流行程度,这些社交媒体服务可以有效地用于环境监控。但是,在社交媒体中,人们使用不同的术语来指代同一事件,例如,蓝绿色藻类,青色细菌,藻类绽放和赤潮指的是同一事件,但是一个是技术性很强的术语,另一个是更通用的术语。技术术语通常是领域专家或领域科学家已知的,这本质上意味着在社交媒体上提供更可靠的信息,但是更通用的术语被各种背景的人们使用,从而对职位的可信赖性提出了疑问。而且,与通用术语相比,更多技术术语的用户群和帖子数量相对较少。但二分法是,术语越通用,数据越嘈杂。可以说使用通用术语来跟踪环境事件会更有效。但是社交媒体数据具有很大的变化性,因此,如本文所示,一次使用训练并进行分类的机器学习模型将无法对许多相关事件进行分类。我们的研究旨在探索使用社交媒体进行环境监测的各种机遇,挑战和方法。

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