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  • Xingjie Wei (Centre for Decision Research, University of Leeds)

  • Gary Dymski (Applied Institute for Research in Economics, University of Leeds)

  • Zheng Wang (School of Computing, University of Leeds)

  • Mingshu Wang (School of Geographical & Earth Sciences, University of Glasgow)


  • Zhengfa Zhang

  • Jaejin Lee

  • Junhao Liang


Predicting inequality from digital textual data

How can we predict inequality using rich data sources in a timely, accurate, and cost-effective manner that allows fora head-to-head comparison for multiple inequality outcomes? Effective prediction indicators and models will help us better understand the elements that lead to inequality trends and how inequality changes as a result of policy changes. 

The purpose of this research is to assess the feasibility and performance of combining publicly available textual data from online news and social media with machine learning to predict urban inequalities in terms of income, safety, and wellbeing. It will provide a comparative assessment of multiple inequality-related outcomes from a single data source using a consistent  methodology  framework. 



A growing number of businesses are participating in greenwashing, which involves deceiving consumers about their environmental performance or the environmental benefits of a product or service they are offering. The increasing prevalence of greenwashing has the potential to have significant negative consequences for consumer and investor confidence in environmentally friendly products. It is extremely difficult to mitigate environmental fraud in an environment with inadequate and unpredictable regulation.


  • Jooyoung Jeon (KAIST)

  • Xingjie Wei (University of Leeds)

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