Jindi Wang


2022

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Breaking through Inequality of Information Acquisition among Social Classes: A Modest Effort on Measuring “Fun”
Chenghao Xiao | Baicheng Sun | Jindi Wang | Mingyue Liu | Jiayi Feng
Proceedings of the Second Workshop on NLP for Positive Impact (NLP4PI)

With the identification of the inequality encoded in information acquisition among social classes, we propose to leverage a powerful concept that has never been studied as a linguistic construct, “fun”, to deconstruct the inequality. Inspired by theories in sociology, we draw connection between social class and information cocoon, through the lens of fun, and hypothesize the measurement of “how fun one’s dominating social cocoon is” to be an indicator of the social class of an individual. Following this, we propose an NLP framework to combat the issue by measuring how fun one’s information cocoon is, and empower individuals to emancipate from their trapped cocoons. We position our work to be a domain-agnostic framework that can be deployed in a lot of downstream cases, and is one that aims to deconstruct, as opposed to reinforcing, the traditional social structure of beneficiaries.