Analytics of big geosocial media and crowdsourced data

摘要

Numerous crowdsourcing and social media platforms such as CrowdSpring, Idea Bounty, DesignCrowd, Facebook, Twitter, Flickr, Weibo, WeChat, and Instagram are creating and sharing vast amounts of user-generated content that can reveal timely and useful infor- mation for detecting traffic patterns, mitigating security risks and other types of time- critical events, discovering social structures characteristics, predicting human movement, etc. Crowdsourcing, also known as volunteered geographic information (VGI), has added a new dimension to traditional geospatial data acquisition by providing fine-grained proxy data for human activity research in urban studies (Chen et al., 2016; NiuandSilva, 2020). However, analyzing big geosocial media and crowdsourced data brings significant methodological and theoretical challenges due to the uncertain user representability when referring to human behavior in general, the inherent noisy data that requires high- performance cost of preprocessing, and the heterogeneity in quality and quantity of sources. In particular, geosocial media data and their derived metrics can provide valuable insights and policy strategies, but they require a deep understanding of what the metrics actually measure (Zook, 2017). All of these underpin complex assessments, not mention- ing the ethnic and privacy issues. Therefore, new sets of methods and tools are required to analyze the big data from crowdsourcing and social media platforms.