Big Data- Good Data: Understanding Society by combining survey data and new forms of data

用户生成的数据，例如社交媒体和众包数据，为研究人员和决策者提供了前所未有的机会，以更高的频率和粒度监测和了解社会。然而，这些“新形式的数据”可能会面临一些挑战，需要与传统数据一起考虑，例如随机调查。其庞大的样本量和可疑的质量（例如偏差，依赖性，代表性和缺失）使传统统计和分析方法的推论受到质疑。因此，需要进行调整，以确保有偏见和不具有代表性的数据不会导致有缺陷的结论。该项目以最近的工作为基础，将地理空间和社会组成部分纳入社会科学中使用用户生成数据的两个主要挑战，即质量-数量平衡和缺失数据填补

User-generated data, e.g. social media and crowdsourced data, have provided researchers and decision makers with an unprecedented opportunity to monitor and understand society at much higher frequency and granularity. However, these 'new forms of data' may have some challenges to be considered alongside traditional data, such as randomised surveys. Their huge sample size and questionable quality (e.g. bias, dependencies, representativeness, and missingness) make inferences from traditional statistical and analytical methods questionable. Adjustments are thus needed to ensure biased and unrepresentative data do not lead to flawed conclusions. This project builds on recent work by incorporating geospatial and social components to two main challenges of using user-generated data in social science, i.e. quality-quantity balance, and missing data imputation