Breaking through Inequality of Information Acquisition among Social Classes: A Modest Effort on Measuring “Fun” (2022)

Abstract: 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.

## introduction does a researcher necessarily want to be surrounded by research-related content at any time of a day? would under-privileged members in society be aware if they are consuming entertainment content all the time? this paper starts with posing the above questions on two extreme cases, which indicate a misalignment of a (content, concept) pair that members of different social classes are stuck in, during the process of information acquisition. while under-privileged social class is identified to be trapped in entertainment content @xcite , which in turn reinforces their social class; higher social class is prone to content that causes anxiety, especially during the periods of a day that members in this class are in urgent need of escaping from their social roles and mental burdens @xcite , while trapped in highly @xcite @xcite anxious content due to the preference they have shown to the algorithms, attributable to their social roles. we argue that there exists inequality of information acquisition among social classes, through which a powerful concept we leverage might be able to help interpret: "fun" -a construct that has never been studied by the nlp community, or never studied as a linguistic construct at all, and one that we try to distinguish from "humor", with the latter having been heavily investigated in nlp research. in this work, we draw upon deconstructing the inequality among social classes, in combat with the issue that advancements of nlp techniques are in fact sometimes reinforcing traditional social structure of beneficiaries @xcite in society -an evaluation heuristic proposed recently by @xcite on aligning nlp with social good. moreover, we aim not to only address the inequality posed with social groups that are underprivileged socio-economically. we in turn believe that every social class is under-privileged in different ways, with an example discussed above regarding some social groups that are typically deemed privileged oftentimes do not actually have the privilege to liberate from their social roles @xcite . we propose, by understanding a key concept in interpreting social cocoons -how fun a social cocoon is -we could facilitate and empower members of different social classes to emancipate from their cocoons based on their needs. the paper is structured as follows: we first turn to theories in sociology (section 2) to draw connection between social class and information cocoon, through the lens of "fun" -an under-studied concept, and introduce our nlp framework as an external regularization to confront the issues involved. section 3 introduces the first part of our framework by training an intermediate language model to understand the concept of fun at a population level. following that, section 4 shows the strong utility of the intermediate model in terms of its transferability in downstream tasks, corroborated respectively in zero-shot and cross-domain user-aware fine-tuning setups. moreover, in section 4 we not only propose some real-life cases to deploy our framework, but also conduct user studies to align their perception with our intermediate model. we consider our work to be a framework that mitigates the matthew effect of inequality in information acquisition in traditional social structure @xcite and put "nlp for social good" into practice. ## "fun" framework in this section, we sketch the thinking heuristics of our framework by basing it on the grounds of sociology theories, drawing connection between social class and information cocoon, through the lens of "fun". we theoretically reason that, the measurement of "how fun a social cocoon is", has been a strong indicator of the social class, or the under-privileged position, that an individual is stuck in, and thus is a metric that shows strong utility toward helping individuals of different social classes to combat the ubiquitous inequality in information acquisition. lastly, we introduce the "fun" framework, enabling members of a specific social group to escape to the other side of this metric with an nlp model. ## helping individual users to break through inequality in information acquisition when deploying in a user-specific setting, we expect the in-domain training in section 3.3 to serve as an intermediate task @xcite . it is thus of interest to study how well the previous intermediate pre-training on the in-domain data source could transfer to users, respectively under (1) zero-shot inference and (2) further fine-tuning settings. complementing the example that "an nlp researcher might not be keen to read about nlp before they go to bed" (figure 3 ), we present an interesting case study. ## conclusion in this work, we identify a misalignment of a (content, concept) pair existing among social classesone's information cocoon and the corresponding degree of fun. this passively decided characteris-tic might reinforce, as opposed to help deconstruct the traditional social structure of beneficiaries, and therefore is originally not a manifestation of the advancement of ai algorithms contributing to social good. in combat with this prevalent issue, we propose an nlp framework, which is composed of 1) an intermediate language model that could understand/predict the degree of liberating engagement (the degree of fun) of text at a population level. 2) further user-aware fine-tuning method to adapt the intermediate model to each individual's unique perception of fun. moreover, we propose some possible real-life cases to deploy our framework in a platform-agnostic setup as an external regularization over recommender systems, such as a web-based plug-in that could filter content based on user-defined fun range, without having to explicitly interact with or adjust the algorithms behind the recommender system of a platform.