Epistemic Injustice in Online Communities: Unpacking the Values of Knowledge Creation and Curation Within CSCW Applications

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Abstract

Information flows are pervasive on the internet and often have a low entry barrier. From an epistemological perspective, information evolves into knowledge. For example, information about mental health on TikTok can act as actionable knowledge for someone seeking to improve their mental health. However, social computing has long known that people do not interact with knowledge cleanly, especially in digital environments. While knowledge curation is essential for targeting irrelevant, biased, or even harmful information, it is value-laden; in choosing how to present information, we undermine non-traditional information such as personal experiences. In this workshop, we will bring together researchers from academia, industry, and marginalized communities to discuss how current CSCW applications contribute to the systemic silencing, exclusion, or delegitimization of certain knowledge contributions (i.e., epistemic injustice). We will diagram our own mental models of how knowledge is created and curated and reflect on critical questions to orient the design of inclusive knowledge spaces online, particularly with topics that blend personal experience with factual information, such as mental health.

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Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

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【要点】：本文探讨了在线社区中知识创造和管理的价值观，以及CSCW应用如何导致认知不公正的问题，强调了在涉及个人经验和事实信息融合的领域中设计包容性在线知识空间的重要性。

【方法】：通过召集学术界、产业界和边缘化社区的研究者进行讨论，并绘制知识创造和管理的心理模型，反思设计包容性在线知识空间的关键问题。

【实验】：本文为一篇研讨会论文，未涉及具体实验及数据集名称，因此无实验结果可概括。